Philosophy of Technology and Engineering Sciences (Handbook of the Philosophy of Science)

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Author: Anthonie W.M. Meijers | Dov M. Gabbay | Paul Thagard | John Woods

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Philosophy of Technology and Engineering Sciences

Handbook of the Philosophy of Science

General Editors

Dov Gabbay Paul Thagard John Woods

AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO North Holland is an imprint of Elsevier

Philosophy of Technology and Engineering Sciences Volume 9

Edited by

Anthonie Meijers Eindhoven University of Technology, Eindhoven, The Netherlands

AMSTERDAM BOSTON HEIDELBERG LONDON NEW YORK OXFORD PARIS SAN DIEGO SAN FRANCISCO SINGAPORE SYDNEY TOKYO North Holland is an imprint of Elsevier

North Holland is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington, MA 01803, USA Linacre House, Jordan Hill, Oxford OX2 8DP, UK Radarweg 29, PO Box 211, 1000 AE Amsterdam, The Netherlands First edition 2009 Copyright © 2009 Elsevier B.V. All rights reserved No part of this publication may be reproduced, stored in a retrieval system or transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without the prior written permission of the publisher Permissions may be sought directly from Elsevier’s Science & Technology Rights Department in Oxford, UK: phone ( 44) (0) 1865 843830; fax ( 44) (0) 1865 853333; email: [email protected]. Alternatively you can submit your request online by visiting the Elsevier web site at http://elsevier.com/locate/permissions, and selecting Obtaining permission to use Elsevier material Notice No responsibility is assumed by the publisher for any injury and/or damage to persons or property as a matter of products liability, negligence or otherwise, or from any use or operation of any methods, products, instructions or ideas contained in the material herein. Because of rapid advances in the medical sciences, in particular, independent verification of diagnoses and drug dosages should be made British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library Library of Congress Cataloging-in-Publication Data A catalog record for this book is available from the Library of Congress ISBN: 978-0-444-51667-1

For information on all North Holland publications visit our web site at elsevierdirect.com

Printed and bound in The Netherlands 09 10 11

11 10 9 8 7 6 5 4 3 2 1

GENERAL PREFACE Dov Gabbay, Paul Thagard, and John Woods Whenever science operates at the cutting edge of what is known, it invariably runs into philosophical issues about the nature of knowledge and reality. Scientific controversies raise such questions as the relation of theory and experiment, the nature of explanation, and the extent to which science can approximate to the truth. Within particular sciences, special concerns arise about what exists and how it can be known, for example in physics about the nature of space and time, and in psychology about the nature of consciousness. Hence the philosophy of science is an essential part of the scientific investigation of the world. In recent decades, philosophy of science has become an increasingly central part of philosophy in general. Although there are still philosophers who think that theories of knowledge and reality can be developed by pure reflection, much current philosophical work finds it necessary and valuable to take into account relevant scientific findings. For example, the philosophy of mind is now closely tied to empirical psychology, and political theory often intersects with economics. Thus philosophy of science provides a valuable bridge between philosophical and scientific inquiry. More and more, the philosophy of science concerns itself not just with general issues about the nature and validity of science, but especially with particular issues that arise in specific sciences. Accordingly, we have organized this Handbook into many volumes reflecting the full range of current research in the philosophy of science. We invited volume editors who are fully involved in the specific sciences, and are delighted that they have solicited contributions by scientifically-informed philosophers and (in a few cases) philosophically-informed scientists. The result is the most comprehensive review ever provided of the philosophy of science. Here are the volumes in the Handbook: Philosophy of Science: Focal Issues, edited by Theo Kuipers. Philosophy of Physics, edited by Jeremy Butterfield and John Earman. Philosophy of Biology, edited by Mohan Matthen and Christopher Stephens. Philosophy of Mathematics, edited by Andrew Irvine. Philosophy of Logic, edited by Dale Jacquette. Philosophy of Chemistry and Pharmacology, edited by Andrea Woody, Robin Hendry and Paul Needham.

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Dov Gabbay, Paul Thagard, and John Woods

Philosophy of Statistics, edited by Prasanta S. Bandyopadhyay and Malcolm Forster. Philosophy of Information, edited by Pieter Adriaans and Johan van Benthem. Philosophy of Technology and Engineering Sciences, edited by Anthonie Meijers. Philosophy of Complex Systems, edited by Cliff Hooker. Philosophy of Ecology, edited by Bryson Brown, Kent Peacock and Kevin de Laplante. Philosophy of Psychology and Cognitive Science, edited by Paul Thagard. Philosophy of Economics, edited by Uskali Mäki. Philosophy of Linguistics, edited by Ruth Kempson, Tim Fernando and Nicholas Asher. Philosophy of Anthropology and Sociology, edited by Stephen Turner and Mark Risjord. Philosophy of Medicine, edited by Fred Gifford. Details about the contents and publishing schedule of the volumes can be found at http://www.elsevier.com/wps/find/bookseriesdescription.cws home/BS HPHS/ description As general editors, we are extremely grateful to the volume editors for arranging such a distinguished array of contributors and for managing their contributions. Production of these volumes has been a huge enterprise, and our warmest thanks go to Jane Spurr and Carol Woods for putting them together. Thanks also to Andy Deelen and Arjen Sevenster at Elsevier for their support and direction.

CONTRIBUTORS

Jennifer K. Alexander University of Minnesota, USA. [email protected] Gerhard Banse Institut f¨ ur Technikfolgenabsch¨ atzung und Systemanalyse, Germany. [email protected] Johannes M. Bauer Michigan State University, USA. [email protected] Henk van den Belt Wageningen University, The Netherlands [email protected] Mieke Boon University of Twente, The Netherlands. [email protected] Stefano Borgo University of Trento, Italy. [email protected] Philip Brey University of Twente, The Netherlands. [email protected] Adam Briggle University of Twente, The Netherlands. [email protected] Louis Bucciarelli Massachussets Institute of Technology, USA. [email protected] Richard Buchanan Case Western Reserve University, USA. [email protected] David F. Channell University of Texas at Dallas, USA. [email protected]

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Contributors

Kees Dorst Eindhoven University of Technology, The Netherlands, and University of Technology, Sydney, Australia. [email protected];[email protected] Maarten Franssen Delft University of Technology, The Netherlands. [email protected] Pawel Garbacz The John Paul II Catholic University of Lublin, Poland [email protected] Armin Grunwald Karlsruhe Institute of Technology, Germany. [email protected] Sven Ove Hansson Royal Institute of Technology, Sweden. [email protected] Paulien M. Herder Delft University of Technology, The Netherlands. [email protected] Wilfrid Hodges Queen Mary University of London, UK [email protected] Wybo Houkes Eindhoven University of Technology, The Netherlands. [email protected] Jesse Hughes Arlington, USA. [email protected] Christian Illies University of Bamberg, Germany. [email protected] Tarja Knuuttila University of Helsinki, Finland. [email protected] Peter Kroes Delft University of Technology, The Netherlands. [email protected] Bruce MacLennan University of Tennessee, USA. [email protected] Anthonie Meijers Eindhoven University of Technology, The Netherlands. [email protected]

Contributors

Carl Mitcham Colorado School of Mines, USA. [email protected] Roland M¨ uller Switzerland. [email protected] Nancy J. Nersessian Georgia Institute of Technology, USA. [email protected] Paul Nightingale SPRU, University of Sussex, UK. [email protected] Alfred Nordmann TU Darmstadt, Germany [email protected] Kees van Overveld Eindhoven University of Technology, The Netherlands. [email protected] Christopher Patton Georgia Institute of Technology, USA. [email protected] Joseph C. Pitt Virginia Tech, USA. [email protected] Beth Preston University of Georgia, USA. [email protected] Michael S. Pritchard Western Michigan University, USA. [email protected] Hans Radder VU University Amsterdam, The Netherlands. [email protected] Nicholas Ray Cambridge University, UK. [email protected] Eric Schatzberg University of Wisconsin, USA. [email protected] Marcel Scheele The Netherlands. [email protected]

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Contributors

Joachim Schummer University of Darmstadt, Germany. [email protected] Johnny Hartz Søraker University of Twente, The Netherlands. [email protected] Knut Holtan Sørenssen Norwegian University of Science and Technology, Norway. [email protected] Andreas Spahn Eindhoven University of Technology, The Netherlands. [email protected] Susan G. Sterrett Duke University, USA. [email protected] Patrick Suppes Stanford University, USA. [email protected] Nigel Taylor University of the West of England, Bristol, UK. [email protected] Amie L. Thomasson University of Miami, USA. [email protected] Paul Thompson Michigan State University, USA. [email protected] Ibo van de Poel Delft University of Technology, The Netherlands. [email protected] Pieter Vermaas Delft University of Technology, The Netherlands. [email protected] Laure Vieu IRIT-CNRS, France. [email protected] Marc J. de Vries Eindhoven University of Technology, The Netherlands. [email protected] William H. Wood United States Naval Academy, USA. [email protected]

Contributors

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Sjoerd D. Zwart Eindhoven University of Technology, and Delft University of Technology, The Netherlands. [email protected];[email protected]

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CONTENTS

General Preface Dov Gabbay, Paul Thagard, and John Woods List of Contributors

General Introduction Anthonie Meijers, editor

v vii

1

Part I: Technology, Engineering and the Sciences Introduction to Part I Hans Radder, associate editor

23

Defining Technology and the Engineering Sciences Carl Mitcham and Eric Schatzberg

27

Science, Technology and the Science–Technology Relationship Hans Radder

65

The Role of Social Science in Engineering Knut H. Sørensen

93

The Emergence of the Engineering Sciences: An Historical Analysis David F. Channell

117

Coherence and Diversity in the Engineering Sciences Gerhard Banse and Armin Grunwald

155

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Contents

Part II: Ontology and Epistemology of Artifacts Introduction to Part II Wybo Houkes, associate editor

187

Artefacts in Metaphysics Amie L. Thomasson

191

Philosophical Theories of Artifact Function Beth Preston

213

Functional Decomposition and Mereology in Engineering Pieter Vermaas and Pawel Garbacz

235

Artefacts in Formal Ontology Stefano Borgo and Laure Vieu

273

The Nature of Technological Knowledge Wybo Houkes

309

Tacit Knowledge and Engineering Design Paul Nightingale

351

Practical Reasoning and Engineering Jesse Hughes

375

Part III: Philosophy of Engineering Design Introduction to Part III Peter Kroes, associate editor

405

Thinking about Design: An Historical Perspective Richard Buchanan

409

Typologies of Design Practice Kees Dorst and Kees van Overveld

455

Translating Customer Requirements into Technical Specifications 489 Marc J. de Vries Foundational Issues of Engineering Design Peter Kroes

513

Contents

xv

Computational Representations of Function in Engineering Design William H. Wood

543

Rationality in Design Peter Kroes, Maarten Franssen and Louis Bucciarelli

565

Designing Socio-Technical Systems Johannes M. Bauer and Paulien M. Herder

601

Part IV: Modelling in Engineering Sciences Introduction to Part IV Sjoerd D. Zwart, associate editor

633

The Notion of a Model: A Historical Overview Roland M¨ uller

637

Functional Modelling and Mathematical Models: A Semantic Analysis Wilfrid Hodges

665

Models as Epistemic Tools in Engineering Sciences Mieke Boon and Tarja Knuuttila

693

Model-Based Reasoning in Interdisciplinary Engineering Nancy J. Nersessian and Christopher Patton

727

Scale Modelling in Engineering: Froude’s Case Sjoerd D. Zwart

759

Similarity and Dimensional Analysis Susan G. Sterrett

799

Measurement Theory and Engineering Patrick Suppes

825

Technological Explanation Joseph C. Pitt

861

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Contents

Part V: Norms and Values in Technology and Engineering Introduction to Part V Ibo van de Poel, associate editor

883

Why Technologies Are Inherently Normative Hans Radder

887

Artefacts and Normativity Maarten Franssen

923

Professional Standards in Engineering Practice Michael S. Pritchard

953

Values in Engineering Design Ibo van de Poel

973

The Concept of Efficiency: An Historical Analysis Jennifer K. Alexander

1007

Aesthetic Values in Technology and Engineering Design 1031 Joachim Schummer, Bruce MacLennan, and Nigel Taylor Risk and Safety in Technology Sven Ove Hansson

1069

Technology Assessment: Concepts and Methods Armin Grunwald

1103

The Interaction of Ethics and Technology in Historical Perspective Carl Mitcham and Adam Briggle

1147

Part VI. Philosophical Issues in Engineering Disciplines Introduction to Part VI Sven Ove Hansson, associate editor

1195

Contents

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Philosophy of Architecture Christian Illies and Nicholas Ray

1199

Philosophy of Agricultural Technology Paul Thompson

1257

Philosophy of Medical Technology Sven Ove Hansson

1275

Philosophy of Biotechnology Henk van den Belt

1301

Philosophy of Computing and Information Technology Philip Brey and Johnny Hartz Søraker

1341

Index Compiled by Marcel Scheele and Andreas Spahn

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GENERAL INTRODUCTION Anthonie Meijers, editor “Now, if there are no artifacts, then there are no philosophical problems about artifacts.” [Van Inwagen, 1990, p. 128] Not so very long ago most philosophers of science maintained that the subjectmatter of this volume was uninteresting and most ontologists claimed it was nonexistent. It was thought to be uninteresting because technology was taken to be an applied science in which the application itself presented no new philosophical challenges. It was believed to be non-existent, because technological artifacts and systems did not live up to the criteria for being part of the ultimate inventory of the world. A combination of these two views leads to the rather fatal conclusion that the philosophy of technology and engineering sciences is boring stuff about non-existing entities! This volume shows how completely wrong that conclusion is. The fact that most philosophers of science have not regarded technology or engineering as a subject worthy of serious study clearly emerges from various wellknown introductions, companions and anthologies. [Curd and Cover, 1998] and [Curd and Psillos, 2008], for example, do not have a single index entry for ‘artifact’, ‘design’, ‘engineering’ or ‘technology’ in 2000 pages of philosophy of science. There are some exceptions though, such as [Newton-Smith, 2000] which contains a small section on the philosophy of technology.1 In analytic ontology interest in technological artifacts has also been largely lacking.2 If such artifacts are discussed at all it is often in the context of arguments intended to show that they do not really exist. The roots of this attitude lie in the positivist rejection of metaphysics.3 What survived of metaphysics after positivism focused on the fundamental concepts of the natural sciences. Basic social sciences and humanities concepts were ignored, taken to refer to non-existing entities, or thought to be reducible to concepts in physics. Since technological artifacts are 1 More evidence for the lack of interest shown by philosophers of science can be obtained from the Philosopher’s Index (Philosopher’s Information Center 2008), database 1940–2008. A search for the keyword ‘science’ produces 46,250 entries, a search for ‘engineering’ only 450 entries, and a search for ‘technology’ 1250 entries. The keywords ‘artifact’ and ‘design’ generated 300 and 1200 entries respectively. Entries with the subject label ‘ethics’ were excluded, because the focus of the search was on the philosophy of science. 2 A combined search for ‘artifact’ and ‘ontology’ led to only 16 (!) entries in the Philosophers Index database 1940-2008. 3 See Thomasson’s chapter in this Volume, Part II.

Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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Anthonie Meijers, editor

human-dependent objects, they do not fit the physicalist mould and are therefore an easy target for eliminativists. There are many reasons why the above conclusion is wrong. The relation between science and technology is infinitely more complex than suggested by the simplistic idea that technology is just an applied science.4 One only has to look at the pervasive role of technology in modern science to see this. Furthermore, the fact that most ontological accounts of artifacts, or medium sized objects in general, are eliminativist can be taken as an indication that there are serious problems with key concepts in metaphysics, such as the concepts of co-location and existence. So instead of simply biting the bullet about the non-existence of artifacts the conclusion might be that we should rethink basic ontological concepts.5 Technology forms a very rich philosophical terrain and the Handbook can be read as a map of the many fascinating issues that can be found here. A number of them have been investigated in depth in the philosophical literature, such as the relation between science and technology,6 the theory of measurement,7 or the role of professional standards in engineering practice.8 Other issues have only been partially explored, such as the types of design problems that engineers solve;9 the epistemic role of models in engineering,10 or the notion of technological explanation as distinct from scientific explanation.11 Many issues, however, have not been addressed at all and that is why there still is a lot of pioneering work to be done in the philosophy of technology and engineering sciences. In what follows I will first define technology and the engineering sciences, which is the subject of this Handbook (Section 1). In Section 2, I will discuss various ways of studying the subject. This will include the approaches taken by historians of technology, by researchers working in the field of Science and Technology Studies (STS), and by philosophers of technology. I will then briefly review highlights in the history of the philosophy of technology and engineering sciences (Section 3). In Section 4, I discuss the architecture of the Handbook which consists of six parts, each covering major aspects of the field. Section 5, the final section, reflects on the nature of the essays in this volume. The philosophy of technology and engineering sciences is a relatively young discipline. In addition to well-established accounts there are explorative essays on a number of areas so far more or less uncharted by philosophers. The Handbook thus also aims to set a research agenda.

4 See Radder’s chapter Science, technology and the science-technology relationship in this Volume, Part I; and Houkes’ chapter, The nature of technological knowledge, in this Volume Part II. 5 See Thomasson’s chapter Artifacts in metaphysics in this Volume Part II. 6 See Radder’s chapter, Science, technology and the science-technology relationship in this Volume Part I. 7 See Suppes’ chapter, Measurement theory and engineering in this Volume Part IV. 8 See Pritchard’s chapter, Professional standards in engineering practice, this Volume Part V. 9 See Dorst and Van Overveld, Typologies of design practices, this volume Part III. 10 See Part IV, this Volume. 11 See Pitt’s chapter, Technological explanation in this Volume Part IV.

General Introduction

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1 TECHNOLOGY AND THE ENGINEERING SCIENCES The difficulty of delimiting the subject of this volume does not arise from the lack of definitions of technology or engineering as there are dozens of such definitions.12 The problem is rather how to come up with a sensible definition given this multitude. The aim of providing such a definition here would be to mark out the kinds of phenomena this Handbook covers. The purpose would not be to specify the essence of the subject (if any), to describe the linguistic origin of these words or to prescribe what the terms should mean. The English word ‘technology’ comes from the Greek τέχνη, which is usually translated as art, craft or skill.13 For modern language users this needs further clarification, because the Greek notion of τέχνη was intimately connected to the notion of knowledge.14 For the Greeks there was therefore no need to combine the word τέχνη with the word logos (as in technology), because τέχνη already involved logos. In Plato’s early writings there are two types of τέχνη: one requiring a lot of physical work (resulting in paintings or sculptures) and one requiring only minimal physical work (arithmetic, logic, astronomy). In later works the notion of τέχνη became associated with the knowledge and activities aimed at making or producing. The English word ‘engineering’ originates from the Latin ingenera, meaning to implant, generate or produce.15 In the late Middle Ages it was linked to the making and operating of military hardware. The term ‘civil engineering’ was introduced in the 17th century to distinguish non-military applications, such as roads and bridges. Engineering was defined at the beginning of the 19th century as ‘the art of directing the great sources of power in nature for the use and convenience of man’.16 In later definitions ‘art’ was substituted by ‘science and mathematics’: engineering is “the application of science and mathematics by which properties of matter and the sources of energy are made useful to people”.17 These definitions show that technology and engineering cannot be identified exclusively in terms of a body of systematic knowledge. After all they do not aim at knowledge for its own sake, but rather at the development and use of knowledge for practical purposes. Technology or engineering is primarily a practice which is knowledge-based. In this practice scientific knowledge, but also experience-based know-how, codes and standards, customer requirements, organizational, legal and economic constraints, physical circumstances, scarcity of resources, uncertainty and ignorance play an important role. The title of the Handbook seeks to empha12 See Mitcham and Schatzberg’s chapter, Defining technology and engineering science in this Volume Part I. 13 See for an extended discussion [Mitcham, 1994, 114–134]. 14 This excluded those skills that the Greeks took to be solely based on experience, such as cooking or swimming. 15 Ibid., 144–149. 16 This is the classic definition of engineering as a civilian enterprise formulated by Thomas Tredgold for the Royal Charter of the British Institution of Civil Engineers (1828). See also Mitcham and Schatzberg’s chapter in this Volume Part I. 17 Webster’s Third New International Directory (2002).

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size both aspects. It refers to the practice of technology and engineering, but also to the engineering sciences as a body of systematic knowledge. Thus defined the philosophy of technology and engineering sciences has a broader scope than most philosophies of the so-called special sciences. It is therefore better to see it as part of the philosophy of technology than as part of the philosophy of science, though these are partly overlapping domains. Carl Mitcham made a useful distinction between four modes of technology:18 • technology as a set of artifacts or systems of artifacts; • technology as a form of knowledge (for the design, production, maintenance and use of technological artifacts and systems); • technology as a range of activities (designing, producing, maintaining and using artifacts); and • technology as an expression of the will of its makers, designers and producers (volition). This distinction shows in another way that the cognitive dimension of technology is important, but does not suffice to define technology.19 It is on the basis of Mitcham’s distinction that the subject-matter of this Handbook can be delimited. It first of all deals with technological artifacts and systems, the objects that technology and the engineering sciences produce. In the second place it covers technology as a body of systematic knowledge. This includes the methodology and epistemology of the engineering sciences as well as the relationship of technology to the natural and social sciences. The Handbook finally addresses technology as a range of activities. The main focus is on the activity of design but the Handbook also looks at other key engineering activities. An important qualification needs to be made at this point. Though Mitcham’s first three modes of technology clearly fall within the scope of the Handbook, the focus is on science-based engineering. The authors of this Volume are mainly interested in the knowledge and activities of modern engineers and in the objects they produce. Users of technological artifacts are only considered insofar as they are relevant to science-based engineering (for example, artifacts are usually designed by engineers with users in mind and they come with a manual). The Handbook only marginally touches on the roles of craftsmen, managers and other professionals involved in the technological domain. This reflects an important decision in the design of the Handbook. The rationale behind this decision is twofold. Firstly, the editors wanted to focus on those aspects that are currently underexposed and ill-understood within the realm of the philosophy of technology. The Handbook thus clearly fills a gap in the field. Secondly, since this is a Handbook in a series 18 See

[Mitcham, 1994]. argues in detail in Part II of this Volume that it is very difficult to distinguish between science and technology solely in terms of this cognitive dimension. 19 Houkes


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on the philosophy of science, it also seemed appropriate to focus on science-based engineering. Several definitions of technology and engineering given in the Handbook refer to one of these three modes or to a combination of modes. For example, Hans Radder describes technology as “a (type of) artifactual, functional system with a certain degree of stability and reproducibility” (this Volume Part V). Paul Nightingale, on the other hand, defines engineering as “the art of organizing and negotiating the design, production, operation and decommissioning of artifacts, devices, systems and processes that fulfil useful functions by transforming the world to solve recognized problems” (this Volume Part II). The first definition primarily perceives technology as a system of artifacts whilst the second sees technology as a range of activities. Mitcham’s fourth mode of technology, technology as volition, largely extends beyond the scope of this Handbook. It concerns the social, cultural, political and anthropological aspects of technology. The philosophy of technology has a rich tradition of analysing these aspects as testified by authors such as Mumford, Ortega Y Gasset, Heidegger and Ellul. In addition, there has always been a strong emphasis on the ethics of technology, both from the point of view of the user and the professional engineer That the subject-matter of the Handbook is limited to the first three modes of technology reflects once again the desire of the editors to concentrate on those aspects that are currently underexposed. The four modes of technology, however, should not be taken as independent of each other. That is why there is also some discussion of the ethical, social and anthropological aspects of technology in Part V.20

2

VARIOUS APPROACHES

The subject-matter of the Handbook can be studied in many ways. Historians, STS researchers, engineers themselves and philosophers of technology have all contributed to a better understanding of the theory and practice of engineering. They do this from different theoretical and methodological perspectives. Some studies are of an empirical and descriptive nature, others are conceptual and/or normative; yet other studies seek to explain while others aim to evaluate; some studies focus on specific theories and methods of engineering while yet others concentrate on the social and economic forces interacting with technology and the engineering sciences. Obviously, one need not be committed to just one of these approaches. Historians have long been interested in technology as an object of empirical study. Apart from comprehensive overviews of the history of technology [Singer et al., 1954; McNeil, 1996], there are numerous historical case studies of engineers and engineering. For example, there are the biographies of individual engineers, such as Isambard Brunel [Rolt, 1959; Buchanan, 2002], Thomas Edison [Israel, 20 These

aspects of technology are prominent in, for example, [Scharff and Dusek, 2003].

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2000] or Vannevar Bush [Zachary, 1997]. Likewise there are the studies of the development of certain artifacts, such as the steam engine [Hills, 1993], the airplane [Constant, 1980; Abzug and Larrabee, 2005] or the atomic bomb [Rhodes, 1995]. There are also inquiries into the nature of technological knowledge that are based on historical cases [Vincenti, 1990]. Increasingly, however, the focus of historical studies has shifted from technology as a subject in its own right to the role of technology in the development of modern societies. Examples are the role of steel in the making of modern America [Misa, 1998], or the role of computers in the transition to an information society [Friedman, 2005]). Landmark studies in this respect are two book series on the role of technology in the formation of Dutch society in the 19th and 20th centuries, edited by Lintsen and Schot [Lintsen, 1995; Schot, 1998]. Researchers engaged in the field of Science and Technology Studies (STS) have always been averse to traditional disciplinary boundaries. They are interested in using social science methods (for example ethnographical methods) to study science and technology. They try to explain their object of study primarily in terms of social action. Science and technology are seen as historically situated social practices that produce knowledge, meaning and impact. Instead of looking at the relation between a theory and the available empirical evidence, STS researchers focus rather on the negotiation processes between actors in the scientific field when explaining the acceptance of a given theory. The primary explanatory objective of STS is to produce “a precise, empirical, multilevel account of the production [of knowledge], influence, and change”.21 One example is the study by Geels en Schot of the various ways in which sociotechnical regimes change.22 The concept of a sociotechnical regime includes here not only the shared cognitive routines in an engineering community but also the social context of policy makers, users and special interest groups. There are two main theoretical positions in STS: the social construction of technology23 and actor-network theory.24 They share a strong empirical orientation in their study of science and technology, whereas their differences concern, among other things, the question of whether all scientific phenomena can ultimately be explained in terms of social action.25 It is fair to say that STS has mainly been a explanatory enterprise. Though the editors of the recent Handbook of Science and Technology Studies believe that the explanatory goal of STS must be “wedded to an agenda of social change, grounded in the bedrock of ethical principles and explicit values (equality, democracy, equity,

21 See

[Hackett et al., 2008, Introduction, p. 4]. Given the large numbers of STS researchers it is probably a simplification to subsume all of them under one explanatory goal. 22 See [Geels and Schot, 2007]. 23 See for example [Bijker, T. P. Hughes and Pinch, 1987] and [MacKenzie, 1993]. 24 See for example [Latour, 1987; 2005]. 25 In his later work Latour opposed Bloor’s so-called ‘strong programme’ in the sociology of scientific knowledge, according to which success and failure in science should be examined and explained symmetrically. Latour saw this as a form of sociological reductionism. See [Latour, 1992].


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freedom, and others)”, this is largely taken to be an emerging challenge rather than a reflection of current practice.26 Numerous engineers have also contributed to a better understanding of the knowledge, activities and objects of the engineering sciences and technological practices. Examples include David Billington’s work on the role of aesthetic values in structural engineering,27 Larry Bucciarelli’s, Clive Dym’s, John Gero’s and Henry Petroski’s respective work on engineering design,28 Billy Koen’s work on heuristics and the engineering method,29 and Andries van Renssen’s work on an applied ontology for the process industry.30 Engineers-historians have also carried out important studies. Walter Vincenti’s and Edwin Layton’s work on the nature and taxonomy of technological knowledge are exemplary.31 These studies are primarily descriptive and aim at clarifying and giving a systematic account of the practice and science of engineering. The distinctive character of the approach taken in this Handbook cannot be defined in terms of a unique method. There is no such method and in this respect the philosophy of technology and the engineering sciences will always be eclectic. Descriptive studies, historical and social explanations, conceptual analyses and normative evaluations can all be found in this Handbook. There are, for example, historical chapters on the emergence of the engineering sciences (Part I), on the way that conceptions of design have evolved over the course of time (Part III), on the notion of a model (Part IV) and on the concept of efficiency (Part V). What sets the Handbook apart from historical, STS, and engineering approaches, though, is its strong emphasis on conceptual, methodological and normative issues (or combinations of them). For example, in Part I Mitcham and Schatzberg reflect on the very idea of defining technology and the engineering sciences, and on the types of definitions that can be given in relation to explanatory purposes and contexts. Houkes critically examines, in Part II, the epistemological claim put forward by Layton, Staudenmaier, Vincenti and others that technological knowledge forms a category of its own. He concludes that such a strong claim cannot be upheld on the basis of the arguments given but that there is still room for a weaker form of emancipation from scientific knowledge. In Part III Kroes, Franssen and Bucciarelli evaluate to what extent engineering design, which is a creative and social process of decision making, can be called a rational process. In addressing this issue they distinguish between various notions of rationality, such as means-ends rationality, procedural rationality and substantive rationality. Several chapters in Part IV explore the notion of a model, the varieties of models, and the methodological and epistemic roles of models in the engineering sciences. Radder investigates, 26 [Hackett et al., 2008, Introduction, p. 5]. Philosophers of technology inspired by Latour’s actor-network theory, such as Achterhuis and Verbeek, have focused on these moral aspects from the very beginning. See, for example, [Achterhuis, 1995] and [Verbeek, 2000/2005]. 27 See [Billington, 1985]. 28 See [Bucciarelli, 1996; Gero, Tham and Lee, 1992] and [Dym, 1994]. 29 See [Koen, 2003]. 30 See [van Renssen, 2005]. 31 See [Vincenti, 1990] and [Layton, 1974].

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in Part V, the normativity of technology and argues that it is not only normative in a contingent sense but also inherently. He thus makes a conceptual claim. Though the emphasis is on conceptual, methodological and normative issues, the editors of the Handbook believe that the philosophy of technology and engineering sciences should be empirically informed. Many chapters therefore refer to specific technologies, engineering theories and engineering practices as cases from which something can be learned. For example, Nersessian and Patton partly base their account of model-based reasoning (in Part IV) on cases drawn from biomedical engineering. Borgo and Vieu, when developing an applied ontology for artifacts in Part II, analyse ways in which these artifacts are represented in information systems. Boon and Knuutilla and Zwart discuss in Part IV the work of Carnot and Froude in order to gain a better grasp of the epistemological roles of models in engineering. The final part of the Handbook, which is devoted to philosophical issues in specific technologies, reflects the empirically informed approach taken here as a whole. 3 A BRIEF HISTORY OF THE FIELD The history of the philosophy of technology and engineering sciences as defined above is not very long. One could alternatively say that it does not yet have a history, only a prehistory. During the last century individual researchers worked on topics such as the nature of technological knowledge, the analysis of design problems, the difference between natural and artificial objects, and the difference between science and technology. Their number has been very small, especially when compared to the number of researchers working on science and technology studies, on the social and ethical problems of technology or on the history of technology. Only after the turn of the century did a community of researchers of a certain size emerge which had a joint interest in the philosophy of technology and engineering sciences.32 From the very beginning to the second half of the 20th century the philosophy of technology (in a broad sense) paid little attention to the topics of this Handbook. Philosophers such as Ernst Kapp, Lewis Mumford, José Ortega Y Gasset, Martin Heidegger, Jacques Ellul, and Hans Jonas were primarily interested in anthropological, ethical and metaphysical studies of technology. There were exceptions though. The work of Jacques Lafitte, Gilbert Simondon, Tadeusz Kotarbinsky, Alard DuBois-Reymond, and Hendrik van Riessen, to name but a few, contained analyses of the concepts of machine and system, taxonomies of machines and their parts and discussions of the process of invention and technological evolution.33 The eighties witnessed a small wave of publications in the field, largely based on studies carried out in the previous decade. Books were published by Rogers on 32 The American-European Society for Philosophy and Technology (SPT) has a much broader orientation than the philosophy of technology and engineering sciences and has historically been dominated by social and ethical questions concerning technology. 33 See [Mitcham, 1994].


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the nature of engineering [Rogers, 1983], by Laudan on the nature of technological knowledge [Laudan, 1984], by Bunge on the philosophy of science and technology [Bunge, 1985] and by Staudenmaier on key issues concerning our understanding of technology [Staudenmaier, 1985].34 One landmark study was Walter Vincenti’s book What Engineers Know and How They Know It: Analytical Studies from Aeronautical History [Vincenti, 1990]. The productivity of the nineties did not match that of the eighties though there were a number of articles on the subject in the journal Techné and in the book series Research in Philosophy and Technology), edited by Carl Mitcham. Mitcham’s own book Thinking Through Technology; The Path Between Engineering and Philosophy, which was published in 1994, gave a predominantly historical overview of the philosophy of technology. The book was (and still is) influential because it contains an analytic framework for studying technology philosophically (see Section 1). It also made an impassioned appeal to philosophers to engage more intensively in the dialogue with engineers and to take technology much more seriously.35 In so doing, Mitcham paved the way for more research on the subject of this Handbook. On a whole, however, the nineties can be characterized as a period of stagnation. No major studies were published and no major initiatives were taken. The subject was also virtually absent in mainstream philosophy journals. Gradually things started to change. At the turn of the century a lot of new activities were launched in the Netherlands. The research programme The Dual Nature of Technical Artifacts in Delft turned out to be a kernel for much more research in the philosophy of technology and engineering sciences, not only in Delft but also elsewhere. The Dual Nature programme focused on the general concept of a technical artifact, as an entity that can be described in functional-intentional and in physical terms. The results of the programme were published in a special issue of a mainstream philosophy of science journal: Studies in History and Philosophy of Science [Kroes and Meijers, 2006], which was a remarkable deviation from standard publication practices in both the philosophy of technology and the philosophy of science. In parallel developments several philosophies of specific technologies emerged. The electronic journal Hyle started publishing articles on the philosophy of chemistry from 1995 onwards, including articles on chemical technology.36 The philosophy of information technology became a major topic of specialized research in its own right,37 while the philosophy of biotechnology gained prominence.38 Other important developments included the emergence of a philosophy of scientific

34 In his book, Staudenmaier analyzed 25 years of publications in the historical journal Technology and Culture. 35 See [Mitcham, 1994, p. 268]. 36 See http://www.hyle.org. 37 See, for example, [Floridi, 2003] and Brey and Søraker’s chapter in this Volume Part VI. A separate volume in the Handbook series Philosophy of Science is also devoted to the philosophy of information. 38 See Van de Belt’s chapter in this Volume, Part VI.

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instrumentation,39 and of a philosophy of risk. The philosophy group at KTH Stockholm played a leading role in the development of the latter.40 In the last decade the community of researchers working on the philosophy of technology and engineering sciences has also become better organized. They have created and organized a portfolio of substantive research programmes, publication projects, regular conferences and workshops. Professional societies such as the SPT showed increased interest by arranging sessions at its biennial conferences on issues such as engineering design, nanotechnology and artifact ontology. Furthermore, the Society’s journal Techné started publishing more and more articles on the subject. There were new initiatives as well. Specialized workshops on the philosophy of engineering were organized in Delft (2007) and in London (2008). Finally, in another initiative, the Division of Logic, Methodology and Philosophy of Science of the International Union of History and Philosophy of Science has decided to give the philosophy of technology and engineering sciences much more prominence in its future activities. Despite all these positive developments there is also good reason to be modest about what has been accomplished. In his book The Nature of Engineering: A Philosophy of Technology (1983) Gordon Rogers tried to give a systematic account of the notion of technological explanation, as distinct from scientific explanation and historical explanation. He distinguished between two types of technological explanation. First-order explanations are teleological in nature and are intended to answer questions of the type ‘What is this flywheel for?’ (To reduce the fluctuations in torque which arise from the intermittent nature of the processes in a reciprocating engine), or ‘Why is the spark initiated before the end of the compression stroke?’ (To compensate for the delay in the ignition process).41 More mature technological explanations are of a causal nature and try to answer such questions as ‘Why did this bridge collapse?’, or ‘What causes the ignition delay in an engine?’, or ‘How can one account for the heat transfer in fluid metals in a fast breeder reactor?’. According to Rogers these technological explanations are causal explanations but they differ from each other in that they occupy a different place in a spectrum of causal explanation ranging from scientific explanations to historical explanations.42 Obviously, much more can and should be said about this. Technological explanation is an important issue in the philosophy of engineering. However, it is fair to say that little if any work has been done on the subject in the last twenty-five years.43 Notwithstanding this sobering fact, the Handbook Philosophy of Technology and Engineering Sciences clearly marks a milestone in the history of the field. It brings together for the first time more than fifty scholars who have written extensively 39 See

[Radder, 2003] and [Baird, 2004]. http://www.infra.kth.se/phil/riskpage/index/htm and [Hansson, 2003]. The philosophy of risk focusses on the epistemological as well as the ethical aspects of risks. 41 [Rogers, 1983, p. 42]. 42 Ibid., p. 43. 43 An exception is Jeroen de Ridder’s PhD thesis on the design and explanation of artifacts [de Ridder, 2007] and Joseph Pitt’s chapter “Technical Explanation”, this Volume Part IV. 40 See


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on such diverse topics as the function theories of artifacts, means-end reasoning, the role of scale models in engineering, notions of computation and efficiency, and philosophical theories of architecture. 4 ARCHITECTURE OF THE HANDBOOK The Handbook consists of six parts, each of which covers a cluster of related issues. These parts correspond to major aspects of the philosophy of technology and engineering sciences. Together they do not exhaust the field though. There is very little on production and maintenance, for example, while the existing parts could easily be expanded (see also the next section). Part I of the Handbook focuses on the demarcation of the object of study: technology and the engineering sciences. Various types of definitions of technology and engineering are discussed as are the aims that these definitions serve. The relationships between technology and the natural sciences and between technology and the social sciences are subsequently analyzed, to situate technology in the disciplinary landscape. Finally, the historical emergence of the engineering sciences together with their diversity and coherence are examined. These issues are central to the field. Part I also contributes to the other parts of the Handbook because it defines their object of analysis. Part II addresses the ontology and epistemology of technological artifacts. It discusses these artifacts in the context of analytic metaphysics and applied ontology. The latter is crucial to the representation of artifacts in information systems. There is also an in-depth discussion of a key property of artifacts: their function. Existing theories of function are evaluated in terms of their ability to account for the functions of technological artifacts. In addition, functional part-whole relationships and their use in engineering are explored. Part II furthermore analyses the notion of technological knowledge. This is a wide concept consisting of many elements. Taxonomies of these elements are presented and the claim that technological knowledge is different from scientific knowledge is carefully evaluated. In addition the role of tacit knowledge in engineering design is discussed. Finally, there is an analysis of means-end reasoning which is central to technological rationality. Part III focuses on a defining activity of engineering: the design of technological artifacts and systems. It includes a historical account of design concepts, a typology of design practices and a discussion of how customer requirements are translated into technical specifications. An analysis is made of the design process in terms of the so-called function-structure relation. A design starts with the specification of an artifact’s desired function. In the process of designing this is transformed into a description of the artifact’s structural properties and a manual for its use. Another topic that is studied is the computational representation of functions in engineering design. Increasingly, engineering design is a computersupported activity and the ability to represent functions in information systems is crucial then. There is also an in-depth analysis of the rationality of design. Design

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includes rational problem solving activities but also social and creative processes. Finally, the complexities of the design of socio-technical systems or mixed systems of artifacts and humans, are discussed. Part IV is about methodological issues. Since models are so central to engineering, for example computer models or scale models, most of this part is devoted to the analysis of models and modelling. Firstly, there is an extensive historical account of the notion of a model. This is followed by a semantic analysis of functional modelling and mathematical models. Several case-studies are presented to show how in engineering models are actually used as epistemic and methodological tools. Case studies also form the basis for an account of model-based reasoning. Since scales and dimensions play an important role in modelling, there is also an in-depth discussion of dimensional analysis and measurement theory. Finally, there is an analysis of the notion of explanation, traditionally a key concept in methodology, but in engineering its meaning and use is distinct from that in the natural sciences. Part V investigates the norms and values that are at work in engineering. As discussed in Section 1, technology and engineering aim at the design and production of technological artifacts and systems that are useful or valuable to human beings. They have inherent normativity. This normativity and the normative statements that can be made about artifacts are analysed in great detail in this part. There are also accounts of the various ways in which non-epistemic values and norms play a role in engineering design and engineering practice. In particular the role of aesthetic values is discussed in relation to the design of such diverse artifacts as urban areas, software, and molecules. In addition to this the values of efficiency and safety in engineering are analyzed. Finally, the central concepts and methods of technology assessment are discussed as is the interaction between technology and ethics. Part VI is of a different nature in that it takes specific engineering disciplines as its object of analysis. It thus gives a different cross-section of the landscape of the philosophy of technology and engineering sciences. Some of the analyses presented are strictly specific to certain disciplines, such as the notion of a gene in biotechnology or the notion of computation in information technology. Other analyses are more general. The part also contains discussion about how developments in one discipline can influence developments in another. For example, developments in medical technology have influenced the notion of disease in medicine. The focus is both on more classical engineering disciplines such as architecture, agricultural technology and medical technology, and on more recent disciplines such as biotechnology and information technology. 5 THE HANDBOOK AS A RESEARCH AGENDA The overview of the various parts of the Handbook clearly shows that it does not cover the field of philosophy of technology and engineering sciences in full. As already mentioned, the Handbook can also be read as a research agenda. This


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will be discussed below using a distinction made at the beginning of the chapter between topics that have been studied in depth, topics that have been partially explored and topics that have not been addressed at all. Topics that fall into the first category are the following: the definition of technology and engineering, the relation between science and technology, the history of design, the translation of customer requirements into technical specifications, the notion of a model, measurement theory, the normative status of artifacts, professional standards in engineering, notions of risk and safety, technology assessment, the philosophy of biotechnology and the philosophy of information technology. There are many more topics that fit into the second category in the Handbook. I have already mentioned the ontology of artifacts which is a topic in need of further elaboration. Similarly, the theories of function in the philosophy of biology appear to be inadequate when it comes to accounting for technological artifacts, while the theories of function that have been specifically developed for these artifacts are still in their early stages.44 Technological knowledge is also an underdeveloped topic. The notion is intrinsically linked to human goals and actions. Many of its elements require further systematic philosophical analysis. For example, the role of practical usefulness (rather than truth-likeness) when validating theories and models in the engineering sciences, or the role of technological rules in engineering practices.45 Other topics that fall into the second category and are included in the Handbook are these: the role of social science in engineering, functional decomposition and mereology in engineering, typologies of design practices, the design of sociotechnical systems, the epistemic roles of models in engineering sciences, similarity and dimensional analysis, technological explanation, the concept of efficiency, the philosophy of architecture and the philosophy of medical technology. A number of topics that belong to the first two categories are unfortunately lacking in the Handbook or only briefly addressed. They are: • the role of technology in experimental sciences (Part I);46 • the dual nature of technical artifacts as functional-intentional and as physical objects;47 the distinction between technical artifacts and natural objects; a structural mereology of artifacts as opposed to a functional mereology; knowledge management in large engineering projects and organizations (Part II); • the evaluation of design methodologies;48 optimization methods in engineering design (Part III); • the relevance of systems theory to engineering; the engineer’s toolbox (finite elements methods, unified modelling language, simulation techniques); 44 See

Preston’s chapter, Philosophical theories of artifact function in this Volume Part II. Houkes’ chapter, The nature of technological knowledge in this Volume Part II. 46 See [Radder, 2003]. 47 See [Kroes and Meijers, 2006]. 48 See journals such as Design Studies and Research in Design. 45 See

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the principles of performance measurement; error theory; the foundations of control theory; the role of heuristics and rules of thumb; the role of technical codes and standards; the visual aspects of engineering; the role of idealization in technology; an account of how computers have changed engineering methodologically (Part IV); • the role of aesthetic values in the design of medium-sized objects (Part V). • the philosophy of chemical engineering (Part VI).49 The number of topics falling into the third category is also substantial. They have been on the editors’ lists of issues to explore for a long time. Occasionally we found researchers who were prepared to do serious work in those areas so that their finding could be turned into chapters in the Handbook. But the remaining list of items is still very long. It includes: • an analysis of basic artifact types (Part II); • an account of the trade-offs in design (Part III); • an analysis of the notion of technological rule, as opposed to the notion of scientific law; an investigation into the role and justification of engineering theories; an account of the operational principles of artifacts (Part IV); • epistemic norms in engineering (Part V); • the foundations of nanotechnology, and of classical engineering disciplines such as mechanical engineering and structural mechanics (Part VI). Military technology is also a terra incognita in terms of philosophical analyses, but it cannot be strictly called a discipline. It is rather a collection of technologies used in a certain field of application. The role of military technology is of fundamental importance to the understanding of the development of other technological disciplines. In addition to these topics there is also an entire aspect not yet covered by the Handbook. That has to do with all the issues related to the production, operation and maintenance of technological artifacts and systems as a part of science-based engineering.50 Scientific theories of production and organization such as Taylor’s, theories of multi-agent systems, the role of ISO standards, decision and planning theories and the social context of engineering are among the topics still to be explored in this domain. Thus defined, the research agenda requires substantial effort on the part of philosophers. It calls for a widening of the community of researchers involved. There are indications that this may occur in the near future. Increasingly, philosophers of technology are publishing in mainstream philosophical journals, thus 49 See

the journal Hyle. of these topics are briefly mentioned by Sørensen in Part I and by Radder in Part V.

50 Some


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reaching a larger audience when discussing topics studied in this Handbook.51 At the same time, philosophers specialized in action theory, ontology, the philosophy of mind and the philosophy of science are turning increasingly to the philosophical problems of artifacts. This has recently resulted in books such as Baker’s The Metaphysics of Everyday Life (2007), Thomasson’s Ordinary Objects (2007) and Margolis and Laurence’s Creations of the Mind: Theories of Artifacts and their Representation (2007).52 To conclude, the Handbook gives the reader an overview of the current state of affairs in the philosophy of technology and the engineering sciences. This field can best be characterized as a field in transition. There are very interesting developments going on and many new topics are being explored. Since this situation will probably continue for some time to come, given the extensive research agenda sketched above, the editors hope that the Handbook will also be made available online in the not too distant future. Ideally it should become a living document that can be improved and extended whenever new or better studies become available. It should give philosophers and engineers easy access to the best and most up-to-date knowledge on the subject. Viewed from this angle the Philosophy of Technology and Engineering Sciences Handbook is merely a step, albeit a step in the right direction.53 6 THE COMPILATION OF THE HANDBOOK AND ACKNOWLEDGEMENTS “Thalassa! Thalassa!” (The sea! The sea!) Xenophon, Anabasis. This Handbook has been a very ambitious project, both in its intellectual and its organizational scope. From the start it was clear that in addition to having chapters on well-researched topics the Handbook would contain explorative chapters on new aspects of the field. The aim was not only to survey the philosophy of technology and engineering sciences in its present state but also to contribute substantially to its development. On the basis of extensive literature searches the first Handbook outlines were produced in the spring of 2004. More than 10 areas were defined, each containing between 5 to 10 topics that were considered to be important. In total 65 topics were chosen that could possibly be turned into chapters. A number of these topics were suggested by the philosophers who later became the associate editors of the 51 See, for instance: [Lelas, 1993; Houkes and Vermaas, 2004; Boon, 2006; Hansson, 2007; Kroes and Meijers, 2006; Zwart and Franssen, 2007; Hughes, Kroes and Zwart, 2007; Vaesen and van Amerongen, 2008], and [Radder, 2008]. 52 See also [Dipert, 1993; Perlman, 2004; Baker, 2004; 2007; Thomasson, 2007] and [Margolis and Laurence, 2007]. 53 The author would like to thank Sven Ove Hansson, Wybo Houkes, Peter Kroes and Hans Radder for their valuable comments on an earlier version of this Introduction.

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Handbook. What then followed was a time-consuming effort to find authors who were sufficiently qualified to write on these topics. In some cases that was easy because there were well-known experts in the fields in question. In other cases it turned out to be extremely difficult or sometimes even impossible. The list of authors and topics became relatively stable after two years, though changes were made even in the final phase. Over the five years of the making of the Handbook the number of topics gradually decreased from 65 to 41 and came to involve 51 authors. In January 2007 a workshop was organized at Eindhoven University of Technology to discuss the first versions of the chapters. In parallel sessions in-depth discussions were held between authors writing on similar topics. The aim was to give feedback and improve the chapters but also to create a certain synergy and demarcate the topics. After the workshop a long process followed. The practical limitations of a number of authors and the fact that many chapters addressed new topics requiring a great deal of new research all caused the completion of the Handbook to be delayed. This did not come as a surprise to the editors, given the ambitious nature of the project. We had to walk the tightrope between including chapters on new topics in the Handbook and meeting a certain deadline. In the last phase several chapters had to be omitted so that the final deadline could be honoured. ACKNOWLEDGEMENTS It would be an understatement to say that I am most grateful to my associate editors Sven Ove Hansson, Wybo Houkes, Peter Kroes, Ibo van de Poel, Hans Radder, and Sjoerd Zwart for their intellectual contributions to the Handbook, the enormous amount of work and effort put into the project, and their continued moral support and good spirits. Without them this Handbook would simply not have been possible. We had a common purpose in this project and we shared a belief in its importance. Jane Spurr’s hard work, support and dedication at the London office have been invaluable in the last phase and I am very much indebted to her. I am also grateful to Rianne Schaaf and to Krist Vaesen for organizing the above-mentioned workshop in such a way that the authors of the Handbook were able to develop a sense of a joint mission. Krist Vaesen was also involved in the literature searches, in the development and updating of the Handbook’s website and in the manuscript handling. I would also like to thank the members of the Philosophy and Ethics Section at Eindhoven for their last minute efforts with proof-reading and indexing the chapters of this Handbook. Finally, I would like to thank the editors Dov M. Gabbay, Paul Thagard and John Woods for their sustained support during the development of this Volume for their Handbook Series.


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BIBLIOGRAPHY [Abzug and Larrabee, 2005] M. J. Abzug and E. E. Larrabee. Airplane Stability and Control: A History of the Technologies That Made Aviation Possible 2nd ed., Cambridge University Press, 2005. [Achterhuis, 1995] H. Achterhuis. De moralisering van de apparaten. Socialisme en Democratie, 52(1), 3-12, 1995. [Baird, 2004] D. Baird. Thing Knowledge: A Philosophy of Scientific Instruments, Berkeley: University of California Press 2004. [Baker, 2007] L. R. Baker. The Metaphysics of Everyday Life: An Essay in Practical Realism, Cambridge (UK): Cambridge University Press, 2007. [Baker, 2004] L. R. Baker. The ontology of artifacts. Philosophical Explorations, 7(2), 99-111, 2004. [Bijker et al., 1987] W. Bijker, T. P. Hughes, and T. Pinch. The Social Construction of Technological Systems: New Directions in the Sociology and History of Technology, Cambridge (Mass.): The MIT Press 1987. [Billington, 1985] D. P. Billington. The Tower and the Bridge, Princeton: Princeton University Press, 1985. [Boon, 2006] M. Boon. How science is applied in technology. International Studies in the Philosophy of Science, 20(1), 27-47, 2006. [Bucciarelli, 1996] L. L. Bucciarelli. Designing Engineers, Cambridge (Mass.): The MIT Press, 1996. [Buchanan, 2002] R. Buchanan. Brunel: The Life and Times of Isambard Kingdom Brunel, New York: Hambledon and London, 2002. [Bunge, 1985] M. A. Bunge. Epistemology and Methodology III: Philosophy of Science and Technology, Dordrecht: Reidel, 1985. [Constant, 1980] E. W. Constant. The Origins of the Turbojet Revolution, Baltimore: Johns Hopkins University Press, 1980. [Curd and Cover, 1998] M. Curd and J. A. Cover, eds. Philosophy of Science: The Central Issues, New York: W.W. Norton, 1998. [Curd and Psillos, 2008] M. Curd and S. Psillos, eds. The Routledge Companion to Philosophy of Science, London: Routledge, 2008. [Dipert, 1993] R. R. Dipert. Artifacts, Art works, and Agency, Philadelphia: Temple University Press, 1993. [Dym, 1994] C. L. Dym. Engineering Design: A Synthesis of Views, Cambridge: Cambridge University Press, 1994. [Floridi, 2003] L. Floridi, ed. The Blackwell Guide to the Philosophy of Computing and Information, Oxford: Blackwell Publishers, 2003. [Friedman, 2005] T. Friedman. Electric Dreams: Computers in American Culture, New York: NYU Press, 2005. [Geels and Schot, 2007] F. W. Geels and J. Schot. Typology of sociotechnical transition pathways. Research Policy, 36(3), 399-417, 2007. [Gero et al., 1992] J. Gero, K. Tham, and H. Lee. Behaviour: a link between function and structure in design. In Intelligent Computer Aided Design. Amsterdam: Elsevier, pp. 193– 225, 1992. [Hackett et al., 2008] E. J. Hackett, et al., eds. The Handbook of Science and Technology Studies. 3rd ed., Cambridge (Mass.): The MIT Press, 2008. [Hansson, 2003] S. O. Hansson. Ethical criteria of risk acceptance. Erkenntnis, 59(3), 291-309, 2003. [Hansson, 2007] S. O. Hansson. What is technological science? Studies In History and Philosophy of Science Part A, 38(3), 523-527, 2007. [Hills, 1993] R. L. Hills. Power From Steam: A History of the Stationary Steam Engine, Cambridge (Mass.): Cambridge University Press, 1993. [Houkes and Vermaas, 2004] W. Houkes and P. Vermaas. Actions versus functions: a plea for an alternative metaphysics of artifacts. The Monist, 87(1), 52-71, 2004. [Hughes et al., 2007] J. Hughes, P. Kroes, and S. Zwart. A semantics for means-end relations. Synthese, 158(2), 207-231, 2007. [Israel, 2000] P. Israel. Edison: A Life of Invention 1st ed., Hoboken: Wiley, 2000.

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[Koen, 2003] B. V. Koen. Discussion of the Method: Conducting the Engineer’s Approach to Problem Solving, New York: Oxford University Press, 2003. [Kroes and Meijers, 2006] P. Kroes and A. Meijers, eds. Special Issue: The dual nature of technical artefacts. Studies In History and Philosophy of Science Part A, 37(1), 1-185, 2006. [Latour, 2005] B. Latour. Reassembling the Social: An Introduction to Actor-Network-Theory, Oxford: Oxford University Press, 2005. [Latour, 1987] B. Latour. Science in Action: How to Follow Scientists and Engineers Through Society, Cambridge (Mass.): Harvard University Press, 1987. [Latour, 1992] B. Latour. Where are the missing masses? The sociology of a few mundane artifacts. In Shaping Technology/Building Society: Studies in Sociotechnical Change, ed. W.E. Bijker and J. Law. Cambridge (Mass.): MIT Press, pp. 225-258, 1992. [Laudan, 1984] R. Laudan, ed. The Nature of Technological Knowledge: Are Models of Scientific Change Relevant?, Dordrecht: Reidel, 1984. [Layton, 1974] E. Layton. Technology as knowledge. Technology and Culture, Vol. 15(1), 31-41, 1974. [Lelas, 1993] S. Lelas. Science as technology. British Journal for the Philosophy of Science, 44, 423-442, 1993. [Lintsen et al., 1995] H. Lintsen et al., eds. Geschiedenis van de Techniek in Nederland: de Wording van een Moderne Samenleving, 1800-1890, ’s-Gravenhage; Zutphen: Stichting Historie der Techniek; Walburg Pers, 1995. [MacKenzie, 1993] D. MacKenzie. Inventing Accuracy: A Historical Sociology of Nuclear Missile Guidance, Cambridge (Mass.): The MIT Press, 1993. [Margolis and Laurence, 2007] E. Margolis and S. Laurence, eds. Creations of the Mind: Theories of Artifacts and their Representation, Oxford: Oxford University Press, 2007. [McNeil, 1996] I. McNeil. An Encyclopedia of the History of Technology 1st ed., London: Routledge, 1996. [Misa, 1998] T. J. Misa. A Nation of Steel: The Making of Modern America, 1865-1925, Baltimore: The Johns Hopkins University Press, 1998. [Mitcham, 1994] C. Mitcham. Thinking Through Technology: The Path Between Engineering and Philosophy, Chicago: University of Chicago Press, 1994. [Newton-Smith, 2000] W. Newton-Smith. A Companion to the Philosophy of Science, Malden (Mass.): Blackwell Publishers, 2000. [Perlman, 2004] M. Perlman. The modern philosophical resurrection of teleology. The Monist, 97, 3-51, 2004. [Philosopher’s Information Center, 2008] Philosopher’s Information Center. Philosopher’s Index 1940-2008, New York: Ovid Technologies, 2008. [Radder, 2003] H. Radder, ed. The Philosophy of Scientific Experimentation, Pittsburgh: University of Pittsburgh Press, 2003. [Radder, 2008] H. Radder, ed. Critical approaches to technology: special issue. Social Epistemology, 22(1), 1-124, 2008. [Renssen, 2005] A. Renssen. Gellish: A Generic Extensible Ontological Language Design and Application of a Universal Data Structure, Delft: Delft University Press, 2005. [Rhodes, 1995] R. Rhodes. The Making of the Atomic Bomb, New York: Simon and Schuster, 1995. [de Ridder, 2007] J. de Ridder. Reconstructing Design, Explaining Artefacts; Philosophical Reflections on the Design and Explanation of Technical Artefacts. PhD thesis, Delft, 2007. [Rogers, 1983] G. F. C. Rogers. The Nature of Engineering: A Philosophy of Technology, London: Macmillan Press, 1983. [Rolt, 1959] L. Rolt. Isambard Kingdom Brunel: A Biography, London: Longmans, 1959. [Scharff and Dusek, 2003] R. C. Scharff and V. Dusek, eds. Philosophy of Technology: The Technological Condition - An Anthology, Malden (MA): Blackwell Publishers, 2003. [Schot et al., 1998] J. W. Schot et al., eds. Techniek in Nederland in de Twintigste Eeuw, ’sGravenhage; Zutphen: Stichting Historie der Techniek: Walburg Pers, 1998. [Singer et al., 1954] C. J. Singer et al. A History of Technology, Oxford: Clarendon Press, 1954. [Staudenmaier, 1985] J. M. Staudenmaier. Technology’s Storytellers: Reweaving the Human Fabric, Cambridge (Mass.): MIT Press, 1985. [Thomasson, 2007] A. L. Thomasson. Ordinary Objects, Oxford: Oxford University Press, 2007. [Vaesen and van Amerongen, 2008] K. Vaesen and M. van Amerongen. Optimality vs. intent: limitations of Dennett’s artifact hermeneutics. Philosophical Psychology, 21(6), 779-797, 2008.


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[van Inwagen, 1990] P. van Inwagen. Material Beings, Ithaca, N.Y.: Cornell University Press, 1990. [Verbeek, 2000] P. Verbeek. De Daadkracht der Dingen: Over Techniek, Filosofie en Vormgeving, Amsterdam: Boom, 2000. [Verbeek, 2005] P. Verbeek. What Things Do; Philosophical Reflections on Technology, Agency, and Design, University Park (Penn.): Pennsylvania State University Press, 2005. Translation of Verbeek (2000). [Vincenti, 1990] W. G. Vincenti. What Engineers Know and How They Know It: Analytical Studies from Aeronautical History, Baltimore: Johns Hopkins University Press, 1990. [Zachary, 1997] G. Zachary. Endless Frontier: Vannevar Bush, Engineer of the American Century, New York: Free Press, 1997. [Zwart and Franssen, 2007] S. Zwart and M. Franssen. An impossibility theorem for verisimilitude. Synthese, 158(1), 75-92, 2007.


Part I

Technology, Engineering and the Sciences


INTRODUCTION TO PART I

Hans Radder, associate editor Part I of this Handbook addresses the relationship between technology, engineering and the sciences. On the one hand, this is an interesting and important subject in its own right; on the other, it provides the necessary background to several of the more focused chapters in the other parts of the Handbook. Looking at this subject from a general perspective, three clusters of issues may be distinguished.

1

VARIETIES OF DEFINITION

First, there are questions concerning the appropriate terminology and the definitions of the chosen relata. Why use ‘technology’ and not, for instance, ‘technics’ or ‘techniques’ ? Can the modern concept of engineering be taken to be equivalent to the older notions of the mechanical or industrial arts? Does it make sense to distinguish between engineering and engineering science? What to include among the sciences: the natural sciences, the engineering sciences, the social sciences or even, in the European tradition, the Geisteswissenschaften? A comprehensive discussion of such issues entails the task of providing more elaborate definitions of the terms used. This leads to several further questions. The overarching question is which variety of definition should be used. As is shown in detail in Carl Mitcham’s chapter, there are at least five approaches available: etymological, essential, prescriptive, linguistic, and pragmatic approaches to definition. An important, related issue is this: what can, or should, be achieved by providing a definition? What I mean is the question of the nature of the relationship between the definition and that what is being defined (the definiendum). Clearly, how to answer this question will depend on the chosen variety of definition. A logicist approach requires that the definition specifies a set of necessary and sufficient conditions for the instances to which it applies. However, in the case of wide-ranging and multidimensional notions, such as technology, engineering and science, this proves to be hard, if not impossible, to achieve. Hence a more realistic approach requires that the definition should capture key features or typical patterns of the definiendum, or that it should specify significant family resemblances among its instances. This is the approach taken in the chapters of this part of the Handbook, sometimes explicitly, as in my own chapter, and sometimes more implicitly, as in the chapter by Gerhard Banse and Armin Grunwald. Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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Hans Radder, associate editor

2 KINDS OF RELATIONS Next to specifying the relata, we need to study the relationship between technology, engineering and science. A crucial methodological issue is the existence of different kinds of relations that may be studied. Again, from a general perspective the following three approaches can be distinguished. We may look at the empirical relations between technology, engineering and the various sciences. This approach may include an historical account of the actual relations between technology and the natural sciences, as can be found in David Channell’s chapter, or a study of the (problematic) role of the social sciences in engineering and the engineering sciences, as provided in the chapter by Knut Sørensen. Although it is important that philosophers are knowledgeable about the actual practices of technology, engineering and the sciences, philosophy cannot be limited to an empirical study of its subject matter. Hence, a second approach focuses on conceptual relations: it characterizes technology, engineering and the sciences through a conceptual specification of their similarities and dissimilarities. This is the approach taken in the second chapter of this part, which addresses the relationship between technology and natural science, and in the last chapter, where the focus is on a conceptualization of the engineering sciences in relation to natural sciences and technical practices. A subject which is occasionally discussed in this part of the Handbook but which deserves more detailed (empirical and conceptual) study, is the important role of technological instrumentation in the various sciences.1 A third approach focuses on the relationship between technology, engineering and the sciences from an evaluative perspective. How are the various relata evaluated and how should they be evaluated, both in themselves and as compared to each other, and both as regards their epistemic value and in terms of their social and moral value? Although such questions crop up occasionally (for instance, in my own chapter) and although several chapters in Part V of this Handbook include relevant material, exploring these evaluative relations in more detail remains an important task for further research in the philosophy of technology and engineering sciences.2

3 TYPES OF MODELS Finally, different types of models are possible of each of these three kinds of relationship and the corresponding relata. The first type may be called primacy models. In these models, empirical, conceptual or evaluative primacy is given to either technology, to engineering, or to science. Authors who emphasize the practical basis of engineering and science will often give primacy to technology, while 1 For some studies of scientific instrumentation and its philosophical significance, see the contributions by Rom Harré, Davis Baird and Michael Heidelberger in [Radder, 2003]. 2 A comprehensive historical discussion is presented in [Forman, 2007].

Introduction to Part I

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authors who stress the scientific basis of engineering and technology will be inclined to assign primacy to science. The ‘humanities tradition’ in the philosophy of technology frequently endorses the former position, while the ‘engineering tradition’ often advocates the latter.3 For instance, a ‘conceptual primacy of technology model’ can be found in Heidegger’s philosophy of technology. The so-called linear model of the science-technology relationship, discussed in several chapters, exemplifies a ‘primacy of science model’, which may be further specified as empirical and/or conceptual and/or evaluative. A second type of model rejects claims to primacy in favor of a two-way interactive approach, which assumes that technology, engineering and science are independent, yet interacting, entities. For instance, as described in the second and fourth chapters of this part, without denying its interaction with scientific knowledge, historians of technology have often emphasized the independent character of technological knowledge. The third chapter both demonstrates the independence of engineering from social science and implies that a greater interaction between the two would be desirable. The fifth chapter provides an independent characterization of the engineering sciences in terms of its methods and goals, yet it also emphasizes the significance of its interactions with practical technologies and basic sciences. A third type of model is based on the idea of a seamless web: these models assume that technology, engineering and science are so strongly intertwined that they cannot be sensibly distinguished. Because of the claimed seamlessness, proponents of such models often use the notion of technoscience (see the second and fourth chapters). Again, both interactive and seamless-web models may be developed from an empirical, a conceptual or an evaluative perspective. From this brief sketch of a comprehensive conceptual framework for studying the relationship between technology, engineering and the sciences it will be clear that the subject of this part of the Handbook covers a large variety of relevant issues. On several of these issues, substantial work has been done and hence this work is presented and reviewed in the subsequent chapters; on other, less researched issues, the chapters of this part of the Handbook offer more exploratory accounts; discussion of further issues can occasionally be found in other parts of this Handbook (in particular, in Part VI); finally, still other issues have to await the future development of the relatively young area of the philosophy of technology and engineering sciences. BIBLIOGRAPHY [Forman, 2007] P. Forman. The Primacy of Science in Modernity, of Technology in Postmodernity, and of Ideology in the History of Technology History and Technology, 23, 1-152, 2007. [Mitcham, 1994] C. Mitcham. Thinking through Technology. The Path between Engineering and Philosophy. University of Chicago Press, 1994. [Radder, 2003] H. Radder, ed. The Philosophy of Scientific Experimentation. University of Pittsburgh Press, 2003. 3 For

these two traditions, see [Mitcham, 1994].


DEFINING TECHNOLOGY AND THE ENGINEERING SCIENCES Carl Mitcham and Eric Schatzberg One of the more difficult issues in any regionalization of philosophy often centers on reaching consensus about how best to define the subject under investigation. This is certainly the case with regard to technology and engineering science (often in the plural and also sometimes called technological sciences), the region of reality or human experience on which the philosophy of technology focuses. These phenomena and their key terms are loaded with contested connotations and interpretations, in part because definition and associated conceptualization has implications for other issues, such as the relations between technology, science, and art. The present chapter thus begins (section one) by reviewing various approaches to definition before turning to the issue at hand, that is, defining technology. It then sketches (section two) the etymological and historical background before undertaking to analyze how “technology” has emerged in conjunction with diverse definitional and conceptualizing strategies in science and engineering (section three), in the humanities (section four), and in the social sciences (section five). A conclusion (section six) argues in favor of distinctions that mirror common language use and real-world phenomena while paying special attention to context and implications. A further difficulty with defining technology arises from problems of translation. Most continental European languages use two distinct terms that are commonly rendered in English as “technology,” namely the vernacular forms of the Latin technica and technologia. Although nuances and exceptions exist in each language, in essence the distinction is what one would expect from etymology. The cognates of “technology” generally refer to the science of or discourse about the practical, material arts, while cognates of “technique” are applied to the actual processes and methods of these activities. Through most of the 20th century, “technique” was the dominant term; most philosophical discourse about technology in French, German, Dutch, Spanish, Portuguese, Italian and more is in fact a discourse about “technique”: la technique, die Technik, de techniek, la técnica. (These cognates can also mean “technique” in the conventional English sense of skill or means to an end; contrast Klaviertechnik, piano technique, with Elektrotechnik, electrical engineering. But such usage is distinct from the one generally translated as “technology.”) Since World War II, however, all words rooted in both technica and technologia have regularly been translated into English as “technology,” thus occluding a basic distinction. A subsequent reverberation of English-language discourse about Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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Carl Mitcham and Eric Schatzberg

“technology,” often derived from the European discourse on “technique,” when translated into other languages by a cognate of “technology,” has tended to blur the distinction further. In this chapter, “technology” covers both discourses, while nevertheless attempting whenever possible and appropriate to call attention to a distinction between technique and technology.

1

APPROACHES TO DEFINITION

Definitions and how they function are fundamental to philosophy. In the present instance the definitional issue is both nominal and real, that is, it concerns the word “technology” (as well as associated terms) and technical phenomena. The question with regard to technology in the philosophy of technology parallels discussions in the philosophy of science about how properly to demarcate science from nonscience, in the philosophy of religion about how best to describe religion, and in the philosophy of language about how to characterize what constitutes language. Answers have implications as well for defining the engineering sciences. Given its significance, prior to exploring definitional alternatives, it is thus propaedeutic to review different approaches to definition and conceptualization in general theories that are closely related to philosophy of language, theory of meaning, and conceptions of truth. In the present case, however, focus will be restricted to five basic approaches to definition and conceptualization: etymological, essential, prescriptive, linguistic, and pragmatic. 1) Etymological definitions are oriented around the origins of terms. In its study of the historical roots of words, etymology is closely related to philology and linguistics. “Etymology” is itself etymologically a compound of the Greek έτυμον (true) and λόγος (speech or reason). In the European tradition, the approach to meaning through linguistic origins can be found as early as the 4th century BCE in Plato’s Crytalus, where an argument for natural meaning involved the development of numerous fanciful etymologies. The Hebrew Scriptures, as well, included etymologies to explain especially place names. Outside the European tradition, as early as the 7th century BCE Sanskrit scholars employed etymology to examine words of sacred significance. Numerous classical Greek and Roman authors continued to utilize etymology to present meanings, and in the 7th century CE Isadore of Seville’s Etymologiae was written as a general handbook of learning. In its modern (and sometimes scientific) sense, a major contributor to etymology was the linguistic prodigy William Jones who, as a British civil servant in 19th century colonial India, did comparative research on Indo-European words. One of Jones’ discoveries was the existence of a common Greek-Sanskrit stem, tekhn-, meaning “woodwork” or “carpentry,” which is obviously present in “technology.” During this same period Friedrich Nietzsche deployed etymology in his genealogical criticism of bourgeois Christian morality, to be followed in the 20th century by Martin Heidegger, who used etymology to revisit accepted meanings in a variety of contexts. As a method for constructing philosophical definition, however,

Defining Technology and the Engineering Sciences

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etymology was already in the Crytalus challenged by Socrates, who argued that “knowledge of things is not to be derived from names” and that instead things must “be studied and investigated in themselves” (439b). Calls for a turn from words to things have ever since been a motif in the history of philosophy. 2) Investigating things in themselves aims to produce essential definitions or concepts. In classical theory, definitions are of concepts, which are themselves “structured mental representations that encode a set of necessary and sufficient conditions for their [perceptual] applications” [Margolis and Laurence, 1999, p. 10]. Essential definitions typically take the form of indicative propositions that state the whatness of something. For Aristotle (and many others) essential definitions have a genus and species or differentia structure. One example might be the claim that technology is the systematic human making of physical objects and/or the using of such objects: technology is human behavior (genus) involved with the systematic making or using of artifacts (species). In genus-species definitions, however, questions arise about whether the differentia indicates a structure or reality or is simply a convenient means for controlling behavior and word usage. Biological taxonomies, for instance, employ convenient features of organisms that do not necessarily reflect their most fundamental genetic determinants. But when technology is defined, as it sometimes is, as applied science, this is often taken to express the inner structure of the phenomenon — a view that has also been widely contested as inaccurate, given that such engineering sciences as thermodynamics often follow the invention of technologies, in this case the steam engine. Essential definitions are also often called connotative or intensional, insofar as they specify the necessary and sufficient conditions for something being a member of a class. Connotative definitions are contrasted with denotative or extensional (also enumerative) definitions, respectively, the latter of which simply list all the members of the class. One important form of an extensional definition is an ostensive definition, which points at the thing or things being referenced: “That airplane overhead is a technology.” Denotation has given rise to the prototype theory of concepts, in which concepts are described as “structured representations that encode the properties that objects in their extension tend to possess” [Margolis and Laurence, 1999, p. 31]. 3) Prescriptive definitions have the structure of imperative sentences that instruct or command how a word is to be used. Giving proper names is a prescriptive act: “Call me Ishmael.” Stipulative definitions are another type of prescription, as in geometry: “Let us define a point as a location without any dimensions (height, width, or depth).” Such stipulations may, as in this instance, also have a genus-species structure and indicate some non-linguistic entity, although the non-linguistic entity is usually imaginary, abstract, or ideal. As such examples suggest, prescriptive definitions can be nominal or formal. With prescriptive nominalism, definitions function as semantic rules for word usage; with prescriptive formalism, definitions are syntactic rules for symbol ma-

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nipulation. Insofar as it goes beyond proper names, prescriptive nominalism is promoted (as with Francis Bacon) as a way to clarify natural language and avoid confusion in argument. Insofar as this tactic begins with ordinary words and makes them more precise, it has been called precising definition (a method recommended by John Locke). Prescriptive formalism is more associated (as in the work of Alfred North Whitehead and Bertrand Russell) with the construction of artificial languages, especially in logic, mathematics, and other formal systems. This version of prescriptivism has also been described as creating theoretical definitions. Persuasive and genetic definitions, as well, illustrate the prescriptive approach. A persuasive definition is rhetorically structured to appeal to the psychology of the person to whom it is addressed and aims to elicit a positive or negative attitude. For example, to an engineer it might be said: “A good technological design is one that uses materials and energy efficiently.” (Virtually the same definition might also be proposed by a humanities critic of engineering: “Technology is focused only on efficiency.”) Thus persuasive definition is highly context dependent. A genetic definition conceives something in terms of its construction: “An atomic bomb is what you get when you create a critical mass of U-235.” Such definitions are compatible with the neoclassical theory of concepts as “structured mental representations that encode partial definitions, i.e., necessary conditions for their application” [Margolis and Laurence, 1999, p. 54]. Prescriptive definitions come into play in particular in various social and historical contexts; “technology,” that is, has a social history. As historians Ronald Kline [1995], Ruth Oldenziel [2006], Eric Schatzberg [2006], and others have pointed out, “technology” only acquired its current valence in English in the early 1900s — first in the United States and then after World War II by international adoption. As Schatzberg summarizes the issue, when economist Thorstein Veblen transplanted early 20th century German discussions about the social impact of Technik into the North American context, he subtly replaced the more common term “industrial arts” with “technology” in order in part to emphasize the historical power and scientific associations of mechanized forms of production. As Oldenziel further observes, “the ascendancy of technology as a keyword in the United States neatly parallels the emergence of America as a superpower committed to technology as the key tool for development in the rest of the world” [Oldenziel, 2006, p. 485]. Although such social analyses of word usage bear principally on issues related to prescription and persuasion, the focus on rhetorical dimensions is associated with what has been referred to as a linguistic turn in the history of philosophy. 4) The linguistic approach to definition focuses on words more than things and, as a philosophical position, can draw on etymology and reflect a behaviorist rather than essentialist interpretation of genus-species form; as such it is related but not reducible to the construction of lexical definitions (in dictionaries) that simply report usage. In the case of “technology,” for instance, the relevant volume of the first edition of the Oxford English Dictionary (1919) reported, moving from the most to the least common, three usages: (1) “a discourse or treatise on an art or arts; the scientific study of the practical or industrial arts”; and by exten-


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sion, “the practical arts collectively”; (2) “the terminology of a particular art or subject; technical nomenclature”; and (3), obsolete and rare, grammar. The second edition (1989) significantly expanded post-World War II references to the first extended meaning and then introduced the further extension of “a particular practical or industrial art” as well as numerous qualified usages such as “high-” and “low-technology.” It then added a fourth usage in special combinations such as “technology assessment” and “technology transfer.” Remarkably, the OED does not recognize the common sense of technology as artifact, as when Americans are said to have, in comparison to the poor in developing countries, houses filled with technologies (dishwashers, disposals, TVs, computers, etc.). Linguistic philosophy, however, seeks to go beyond lexicography by distinguishing use from usage. For instance, Gilbert Ryle [1953] argued that while “misusage” is not possible (people simply speak the way they speak) “misuse” is. Conceptual analysis can identify logical misuses in common linguistic usage, as when people utter inconsistent propositions. A more subtle effort to distinguish use from usage depends on Ludwig Wittgenstein’s notion of language games. For Wittgenstein, the meaning of a word is dependent on the role it plays in a language game, the rules of which provide the basis for determining proper use; misuse occurs when usage veers beyond the rules of any particular game. With reference to this approach, Fran¸cois Lyotard [1979] described technique (“technology” in the English translation) as a game in which the governing rule is neither truth, justice, nor beauty, but efficiency thus implying that to call an inefficient productive process a “technology” would be to misuse the word. A linguistic approach to definition has deep roots in the conceptual analyses present in many philosophical arguments. One classic example occurred in Plato’s Euthyphro, where Socrates tried to get the eponymous interlocutor to be clear about the meaning of piety. In the tradition of British philosophy that runs from Locke to John Stuart Mill, appeal has also often been made for terminological clarification as necessary to sound argumentation. This approach was strongly promoted by G.E. Moore and subsequently by Wittgenstein, who famously believed that many philosophical difficulties such as free will and the mind-body problem arose from linguistic misuse and could be dissolved by increasing linguistic precision. Richard Robinson [1950], in a philosophical monograph on definition, made an extended argument for a strictly linguistic account of definitions as reports of word usage plus a prescriptive or stipulative element concerning use. In such cases, it may be noted, definition is often the conclusion rather than the beginning of an argument. One example might be: “A careful review of various usages for the term ‘technology’ shows that it is best limited to scientific making and using.” (For one suggestive examination of word usage and thus a linguistic philosophical approach within rather on technology, see [Hollister-Short, 1977].) 5) In an overview of definition on which the present one draws, Raziel Abelson argued that “[e]ssentialists conclude that the knowledge conveyed by definitions is descriptive knowledge of essences, linguistic philosophers conclude that it is descriptive knowledge of language usage, while prescriptivists maintain that defi-

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nitions do not convey knowledge of any kind” [Abelson, 1967, p. 321]. In her view, however, none of these approaches, even within their own frameworks, provide sufficient criteria for distinguishing good from bad definitions. There exist a set of rules for the construction of good definitions inherited from scholastic philosophy. Such rules include that a definition should fully cover the definitum (thing being defined), that the definitum should not reappear in the definition, and that definitions should use univocal terms to avoid ambiguity. But even following these rules, definitions can sometimes be inadequate to their contexts. Pragmatic consideration of how a definition fits in or serves the context in and for which it is formulated thus constitutes another approach to definition — one that is able to utilize, as appropriate, etymological, essential, prescriptive, or linguistic approaches. In short, pragmatism seeks definitions that work well in context, denying that there is anything such as a pure definition or a definition that does not serve some purpose. Stipulative definitions work in mathematics. Essential definitions are mistaken in presuming the existence of some essence outside a functioning relationship, but within a context (e.g., biology) may function quite well; one should just not presume that a genus-species definition of a plant or animal has anything to say about (for instance) its inner structure or how the plant or animal can or should be used by human beings. The context for linguistic definitions is general language usage more than language use in any specialized situation, when precising may be more appropriate. To some extent this reflects the anonymously named theory-theory of concepts understood as “representations whose structure consists in their relations to other concepts as specified by a mental theory” [Margolis and Laurence, 1999, p. 47].

2 ETYMOLOGY AND EARLY CONCEPTUAL HISTORY It is not necessary to believe etymology uncovers the true meanings of words to grant that linguistic history often influences current meanings. J. L. Austin, for example, has acknowledged “trailing clouds of etymology” as persistent influences in current meanings [Austin, 1961, p. 149]. Particularly is this so for terms such as “technology,” which were often created with explicit reference to etymology. Understandings of the English “technology” and “engineering/technological sciences” thus benefit from an examination of their links to related terms in other languages. Nevertheless, scholars must take care not to project present meanings onto terms in the past, even the relatively recent past. Just as the present-day German Technologie is not the same as English “technology,” neither are the meanings of “technology” the same in 19th and 21st century English. The English words “technics,” “technique,” and “technology” are all rooted in the Greek τέχνη, commonly translated as “art,” “craft,” or “skill.” The same goes for the German Technik /Technologie and the French technique/technologie. (As already noted, the Greek has behind it a common Indo-European stem.) The root phoneme projects into the Latin texere (to weave) and tegere (to cover). In


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popular classical literature techne and its Latin translation, ars (from which the English “art”), could refer as well to cleverness or deviousness in getting, making, or doing and to specific trades, crafts, and skills of many kinds. Detailed etymologies can be found in [Schadewaldt, 1960; Heyde, 1963; Seibieke, 1968]. The Greek philosophical tradition contained rich discussions on the nature of techne. Carl Mitcham [1994] has noted how techne came to be conceived not only as an activity but as a kind of knowledge. In Plato’s Gorgias, for instance, Socrates argued that every techne involves logoi (words, speech, reason) bearing on the art involved (450b). Additionally, Socrates distinguished two types of techne, one consisting primarily of physical work that required minimal use of conscious reason (such as painting or sculpture) and another depending more intimately on reason that required little physical exertion (such as arithmetic, logistic, or astronomy). Activities such as cooking and persuading were labeled atechnos (nontechnical) — each being a mere tribe (knack or routine) based simply on empeiria (experience) (501a). In the Ion, poets who exercised their poiesis (making) by virtue of divine inspiration were also described as devoid of techne (cf. 533d). The early Plato, then, seemed to adopt what has been called the prototype theory of the concept of technics as coextensive with those human activities that can be talked or reasoned about. Contrary to often repeated characterizations of Plato as looking down on technics, this reading gave techne a nonutilitarian, not to say transhuman, dimension. The later Plato articulated a different but related understanding of techne. In the Philebus, for instance, knowing was divided into that involved with education or upbringing and that engaged with making or producing (55c). Of the second, technical knowledge, there were again two kinds: one sort (exemplified by music, medicine, and agriculture) that proceeded by conjecture and intuition based simply on practice and experience, and another (such as carpentry) that consciously involved the use of numbering, measuring, weighing (55e-56c). The latter possessed greater akribeia (exactness or precision) and this was denominated techne in a primary sense. Thus techne was distinguished from all human activity and knowledge of a political sort (education and, by extension, statecraft) so as to be associated more closely with the activities of physical making or producing. In addition, those making activities were most truly techne that involved some quantitative precision. Aristotle argued a complementary understanding of techne as one in a spectrum of different forms of engagement with reality, moving from sensation through experience to theory (Metaphysics I). According to his formal definition, techne is ἕξις μετὰ λόγου ἀληθοῦς ποιτική (Nicomachean Ethics VI, 4; 1140a11). Translated with slavish literalness: Techne is a habit (or stable disposition to act) with a true logos ordered toward making (human production). This definition of techne was used repeatedly to define art (Latin ars) by later scholars, among them Thomas Aquinas, Ephraim Chambers (in his Cyclopedia of 1728), and more recently by the neothomists Etienne Gilson and Jacques Maritain. Once again the realist, nonutilitarian character of technical knowledge came to the fore; insofar as it was true,

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this logos was based on gnosis (mental grasping or cognition) of aitia (causes) (Metaphysics I, 1; 981b6-7). Techne was thus conceived as episteme in that it involved true consciousness of the world and could be taught or communicated (Metaphysics I, 1; 981b8-10); but it was a distinctive form of episteme insofar as it bore on changing rather than unchanging things (cf. Nicomachean Ethics VI, 6; 1141b 31-36). Plato and Aristotle agreed in stressing the “logical” character of techne, even while they disagreed about the kind of logos involved. Yet neither felt drawn to join the two words — to speak of a logos of techne. Techne simply used logos. To put it simply, what can be grasped or known by techne through logos was the eidos (idea or form) in Plato or the aitia (causes) in Aristotle. What was not able to be grasped was the process, the “how to do it” of the poiesis (making). As if to emphasize the point, Aristotle argued further that it was part of techne “to know the form and the matter,” but the matter, hyle, only “up to a point” (Physics II, 2; 194a23). “Matter is unknowable (agnosis) in itself” (Metaphysics VII, 10; 103a9). The form was the idea in the mind of the artisan (Metaphysics VII, 7; 1032a35), but its union with matter was at the mercy of matter and its specific receptivity. The ultimate guide for the making process was not reason but perception, aisthesis (Nicomachean Ethics II,9; 1109b23; cf. II, 2; 1104al-9). In one instance Aristotle went so far as to describe the coming together of form and matter as dependent on a “desire” or “reaching out” in matter (Physics I, 9; 192a18). Reflecting precisely this sensibility, one scholar [Dunne, 1993] has argued at length the strong connections between techne and phronesis or that practical wisdom constituted by recognition of potential ready and able with the right assistance to be actualized in practical affairs — a recognition that was unable to be reduced to some method. In an insightful analysis of technology in Greek and Roman antiquity, the historian Serafina Cuomo [2007] compares Platonic and Aristotelian conceptualizations with those of the Hippocratic corpus, arguing that both philosophical and medical understandings observed a strong connection between techne and tyche or luck. Despite the teachability of techne, the actual making of artifacts or health remained fundamentally stochastic insofar as it depended on the particularity of matter and thus the luck of acting in accord with the kairos or right moment. At the same time this ability to seize the moment introduced into human affairs a clever or devious power that was both necessary and a threat to social order. In a gloss on a passage in the Physics, Thomas noted how Aristotle’s position implied that matter, at least particular matter, was not just privation of form, but a reality in its own right. Although with respect to the object to be made the matter could be spoken of as formless, in reality it was something that “seeks form or further form according to its proper nature” (In libros VIII physicorum I, lec. 15, par. 8). As Thomas argued elsewhere, “Act and form are received into matter according to the capacity of the matter” (Summa theologiae I, qu. 85, art. 7). Absent an artisan’s sensitivity to particular capacities of this ordering toward form, techne or ars would fail to achieve its end.


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Perhaps the limited role granted to logos for techne explains the absence of the Greek term τεχνολογια in both Plato and Aristotle — except in one case, Aristotle’s treatise on the techne of rhetoric, where a form of the word appears four times (Rhetoric I, 1; 1354b17; 1354b27; 1355a19, and I, 2; 1356a110). The exact meaning in each occurrence is debatable. But on one interpretation the connection is reflects how in the case of rhetoric, which deals with the nonsubstantial matter of words, it becomes possible to know not just formal ends of making but also the processes by which the related making takes place. From this imaginative Aristotelian usage it is easy to develop the definition of τεχνολογια as a treatise on (or terminology of) the arts of language, especially grammar and rhetoric, a usage that can be found among Hellenistic and Byzantine authors. Yet the Latin transliteration technologia was unknown in classical or medieval Latin literature. (Cicero used the term once, but only in Greek.) Although Seibicke [1968] concludes that the Greek τεχνολογια had “no direct influence” on scholarly discourse during the Middle Ages or the beginning of the modern era, it is difficult not to suspect some connection between the older Greek usage and the emergence in late 17th century English of “technology” as a treatise on grammar. There is, then, no continuous history of usage linking the classical Greek τεχνολογια with the current meanings of “technology.” Yet the term did reappear in Latin during the Reformation with connotations close to those of its classical roots, in the work of the 16th century French Protestant rhetorician Peter Ramus. Ramus used technologia to refer not to the logos of one techne (with words) but to the logos of relations among all technai. Infected by a passion for method associated with Renaissance humanism, and inspired by a perennial call to move from words to things, Ramist technologia systematically ordered and arranged the arts and sciences. Ramus also coined the term technometria (which occurs in neither Greek nor Latin) as a synonym for technologia. Both terms were taken up by a number of Protestant theologians in the 17th and early 18th centuries, in particular by the English Puritan William Ames. Although in principle Ramist technologia applied to the arts (technai ) as a whole, in practice it referred to the liberal arts, especially as dealt with in higher education. Students at Harvard and Yale, for example, produced a series of theses technologicae from the mid-17th century into the 18th century. This early modern technologia had little to do with the mechanical arts. But by the beginning of the 19th century, “technology” had become strongly linked to the practical arts and modern industry. Perhaps the earliest attestation of this usage is provided by the second or 1661 edition of Thomas Blount’s Glossographia, a dictionary of “Hard Words,” generally of foreign origin, that had become common in “our refined English tongue.” Although the first edition of 1656 did not have an entry for “technology,” the second edition identified the term as Greek and defined it as “a treating or description of Crafts, Arts or Workmanship.” Similarly, John Kersey’s edition of Edward Phillips’s 1706 dictionary, The New World of English Words, defined “technology” as “a Description of Arts, especially the Mechanical.” This new definition also appeared in the work of the German Enlightenment

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scholar Christian Wolff. The second volume of his Philosophia rationalis siva logica, that is, Praemittitur discursus praeliminaris de philosophia in genere (1728), defined technologia as scientia atrium et operum artis (science of arts and of works of art) or scientia eorum, quae organorum corporis, manuum potissimum, opera ab hominibus perficuntur (science of what humans produce by the work of the organs of the body, mainly the hands). At both the linguistic and philosophical levels, in England and in Germany, “technology” was beginning to gain a new meaning. Nevertheless, “technology” in any of its forms remained a rare term in the 18th century. For example, Samuel Johnson’s famous English dictionary of 1755 did not include it, nor did subsequent editions of the dictionary well into the 19th century. “Technology” first gained currency not in English, but in German. Johann Beckmann’s Anleitung zur Technologie [1777] was the first work that self-consciously developed the concept of technology as a discipline devoted to the systematic description of handicrafts and industrial arts (see also Part I in this Volume, “Coherence and Diversity in the Engineering Sciences”). Beckmann was a prominent figure in German cameralism, a set of practically-oriented academic disciplines concerned with state administrative organization. In the Anleitung, Beckmann made Technologie a key academic field within cameralism, as an approach to an emerging area of German higher education. In academic discourse, the Anleitung included both craft work and industrial production as aspects of Technologie. Yet this cameralist concept remained distinct from the late 20th century notion of technology. Cameralism was grounded in a natural-historical approach to knowledge and as such focused on classifying rather than explaining. Beckmann’s Technologie rested firmly in the tradition of Bacon’s proposal for a natural history of trades, a project also pursued in Denis Diderot and Jean D’Alembert’s contemporary project of the Encyclopédie ou dictionnaire raisonné des sciences, des arts et des métiers. Beckmann, however, sought to make Technologie into a true Wissenschaft by creating a classificatory scheme equivalent to the Linnaean system for plants and animals. In this endeavor he ultimately failed, especially to develop a classification system that could accommodate the new machines and processes that were transforming British industry, such as the steam engine or mechanized spinning, and thereby to alter the practical imagination [Lindenfeld, 1997]. In an insightful assessment of Beckmann’s approach to what the Czech-French philosopher and historian Jan Sebestik terms “technological science,” Sebestik [1983] traces a hundred years of Beckmann’s European influence and a mid-20th century revival of especially French interest in this conception of technology in, for instance, the work of Jean-Claude Beaune [1980]. (In this regard, see also [Guillerme and Sebestik, 1966; Guillerme, 1985; Mertens, 2002].) A work similar to Beckmann’s Anleitung emerged almost half a century later in the United States with Jacob Bigelow’s Elements of Technology [1829]. Subtitled On the Application of the Sciences to the Useful Arts, this was the first English work to use “technology” in its title, and is thus often mistakenly credited with introducing the term into American usage. Bigelow, a physician and Harvard professor, claimed that he had adopted a word “found in some of the


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older dictionaries,” and that “under this title” he included “an account. . . of the principles, processes, and nomenclatures of the more conspicuous arts, particularly those which involve applications of science, and which may be considered useful.” In contrast to Beckmann, who made Technologie into a minor but significant academic field in Germany, “technology” was little more than a pretentious adornment for Bigelow. He only invoked the term once in the preface of this 500page work, and dropped all reference to “technology” in the 1840 edition, which was retitled simply The Useful Arts. Nevertheless, Bigelow’s book was consistent with the classifying spirit of cameralist Technologie, although in substance the Elements was little more than a turgid compilation drawn from published sources. More significant for American usage was the 1861 decision in Boston to name its new engineering school the Massachusetts Institute of Technology. The choice of “technology” rather than “polytechnic” was odd, and may have been suggested by Bigelow [Stratton and Mannix, 2005]. As with Bigelow’s Elements, the “technology” in MIT carried little theoretical weight aside from its usage as a name, although the name did help associate “technology” with engineering education. Yet for the remainder of the 19th century, “technology” remained a secondary term of little philosophical importance. Its meaning was captured well by the American Century Dictionary of 1891: “That branch of knowledge which deals with the various industrial arts; the science or systematic knowledge of the industrial arts and crafts, as spinning, metal-working, or brewing,” a definition in essence little changed from Christian Wolff’s early in the 18th century. Something like the Wolff-Bigelow usage continued to be manifest late in the 19th century in a series of textbooks for correspondence courses that promised students information “such as can be immediately applied in practice” and in which “the knowledge of mathematics required is limited to the simplest principles of arithmetic and mensuration” [International Library of Technology, 1897, pp. iii-iv]. In retrospect, the lack of definitional discourse and critical reflection is surprising, given the central role of industrial arts in the momentous economic, social, and cultural transformations during the second half of the 19th century. When such philosophical and critical discourse did arise, it centered not on “technology” or its cognates but on the German term Technik. This review of the etymological roots of “technology” suggests a more continuous development than is fully warranted. In part, “technology” was repeatedly reinvented by scholars who drew on etymology in new contexts. This construction of meaning continued as the term spread to the various communities of discourse in which the philosophy of technology was to be pursued. “Technology” is not a word or concept that has some pure or univocal meaning outside these contexts, so that its etymology has undergone multiple adaptations. Although there are many ways to describe these contexts, for the purposes of the philosophy of technology and the engineering sciences, they can be divided into three broad scholarly communities: natural sciences and engineering, humanities (and humanistic discourse in general), and the social sciences, all of which emerged as distinct fields of knowledge in the 19th century. Adopting a combined pragmatic and linguistic approach

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to definition, it is thus appropriate to consider in more detail how the term functions and related phenomena are understood in different but related manners in these three communities — beginning with that community to which etymology most immediately points, science and engineering. 3 IN SCIENCE AND ENGINEERING A sophisticated philosophical discourse on technology first emerged among German engineers in the late-19th century. Yet instead of the cameralist term Technologie it adopted the more common Technik, which was nevertheless interpreted in new ways. Germany was rapidly industrializing during the second half of the 19th century, in part on the basis of new, science-shaped industries such as those producing synthetic dyes and using electrical power. Although Technologie never disappeared completely, it was not embraced by German engineers and industrialists, for several reasons. Among engineering educators, the new theory-centered approaches to engineering education were incompatible with the encyclopedic, taxonomic approach of Technologie. Nor was the field of Technologie, intended for training administrators, of much use to practicing engineers. Finally, cameralism itself became a discredited doctrine in liberal economic though of the late-19th century, as did Technologie by association. Rather than Technologie, German engineers embraced Technik — and “the notion of Technik cannot be considered the heir of Technologie” [Frison, 1998, p. 119]. Technik and Technologie were the focus of independent discourses and rarely discussed together or compared. Like Technologie, Technik entered German through modern Latin, mainly in the late-18th century. In its broadest sense Technik indicated the practical rules and methods used to achieve any given end. This usage is similar to the English “technique,” in the sense that one speaks of the technique of a painter or musician. By the mid-19th century, however, Technik had also become firmly associated with the industrial arts. Used without modification, Technik encompassed all the arts of material production, conceived as a whole. Thus the meanings of Technik split into two related strands, a narrower one referring to the material aspects of industry, and a broader one encompassing the rules, procedures, and skills for achieving any goal. In this context the German words are appropriately translated as “engineering” and “engineering sciences” (sometimes “technological sciences”). In the second half of the 19th century, Technik became central to the selfunderstanding of the German engineering profession. According to its 1856 constitution, for instance, the primary purpose of the pan-German engineering association Verein Deutscher Ingenieure (VDI) was the advancement of German Technik more than promotion of the interests of engineers. The organization defined membership almost completely in terms of Technik or things technische open to practicing Techniker (engineers), teachers of Technik or Technikwissenschaften, along with the owners and managers of technical establishments. Engineers were only part of a larger technical professional community.


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As Mitcham [1994] has argued, it was from the reflections of German engineers that a sustained philosophy of technology initially emerged, a philosophy nonetheless centered on the concept of Technik. The first work in this spirit was not actually from an engineer, but from the left-Hegelian Ernst Kapp, whose two decades of exile on the Texas frontier gave him considerable experience with tools and machinery. In his Grundlinien einer Philosophie der Technik [1877], the first book to explicitly call itself a philosophy of either Technik or technology, Kapp conceptualized Technik in terms of “organ projection,” that is, as an extension of the human body. More significant than Kapp were the philosophical writings of a small cadre of late 19th century Techniker. In the rapid industrialization that followed German unification, engineers as part of a technical community sought status within the German cultural world in which pride of place was associated with Bildung, understood roughly as educational development through the principles of high culture. In pursuit of recognition through a distinctly technical Bildung, engineers developed an engineering-based philosophy of technology. Contributions to such a philosophy had become extensive by the end of the century, when the German-speaking Russian engineer Peter von Engelmeyer published a series of 12 articles in a German engineering journal on the “Allgemeine Fragen der Technik ” (or general questions of technology). Engelmeyer discussed dozens of authors who addressed fundamental issues regarding Technik, including other engineers, such as Max-Maria von Weber, Franz Releaux, and Josef Popper-Lynkeus. These engineer-philosophers, including Engelmeyer himself, grappled with the relationships between science and Technik, culture and Technik, and progress and Technik, as well as the social status of Techniker and the nature of Technik itself — all issues that have remained central to the philosophy of technology. This engineering philosophy continued into the 20th century, with engineers emphasizing the creative and spiritual aspects of Technik to defend themselves against attacks from humanities intellectuals. Some of this work contributed to what Jeffrey Herf [1984] has termed “reactionary modernism,” an approach to technology common among German national socialists. In English “technology” received little attention within scientific and engineering communities before World War II. When the term did emerge as a focus of discussion, it combined two sets of meanings: its 19th -century definition as the science of the industrial arts and connotations borrowed from the German Technik. By the 1960s, “technology” with these merged meanings appeared promiscuously among scientists and engineers in reference to objects (mostly products and devices, less to structures), processes (from skills to systems or networks of production, transportation, and communication), and knowledge (of both how to make and use) — with a bias toward products and processes. When pushed for specificity, both scientists and engineers tended to conceive technology in essentialist terms as applied science. This conception was derived from the idea present in Bigelow of technology as the application of science to the useful arts. But the genus and species for applied science can take two different forms: for scientists,

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the genus is more likely to be science, with application as the species differentia; for engineers the genus is more commonly the industrial or mechanical arts, with their scientific pursuit as differentia. The former takes the results of science and transposes them into forms that can be of use in the design and creation of structures, products, processes, or systems; the latter adopts and adapts the methods of science to transpose traditional technical practice into more systematic or scientific forms (see also Part I in this Volume: “Models of the Science-Technology Relationship”). In what has become a minor classic of interpretation, historian Edwin Layton summarized the emergence of both forms of technology as applied science in the “mirror-image twin” communities of science and engineering. It is nevertheless the second of these transpositions that is most notable. During the 19th century, Technological knowledge was uprooted from its matrix in centuries-old craft traditions and grafted onto science. The technological community, which in 1800 had been a craft affair but little changed since the middle ages, was reconstructed as a mirror-image twin of the scientific community. The artisan was replaced in the vanguard of technological progress by a new breed of scientific practitioner. For the oral traditions passed from master to apprentice, the new technologist substituted a college education, a professional organization, and a technical literature patterned on those of science. Equivalents were created in technology for the experimental and theoretical branches of science. As a result, by the [early 20th century], technological problems could be treated as scientific ones; traditional methods and cut-and-try empiricism could be supplemented by powerful tools borrowed from science. This change was most marked in the physical sciences and civil, mechanical, and electrical engineering, [the result of which] might be termed “the scientific revolution in technology”. [Layton, 1971, p. 562] Paradoxically, the first transposition — of the content of science into engineering — was more difficult ; but since this seemed the more ready interpretation, there readily emerged a common resistance to the definition of technology as applied science. The example of James Clerk Maxwell is illustrative. Although “Maxwell was one of those scientists who consciously attempted to contribute to technology,” it was necessary for the “engineer Oliver Heaviside to translate [Maxwell’s] electromagnetic equations into a form usable by engineers” [Layton, 1971, p. 577]. Scientific laws of nature do not immediately function as engineering design principles. Implicitly present in Layton’s exposition are thus two different conceptualizations of technology: technology as craft or technics that can be transformed by science, and technology as the result of such a transformation. In the latter case, technology is closely identified with engineering, so that it is appropriate to consider as well the meanings of this commonly associated term.


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Like German engineers in the late-19th century, subsequent engineers have commonly identified their profession in some sense with technology, but in ways influenced by the history of engineering. The term “engineering” is itself rooted in the classical Latin ingenera (to implant, generate, or produce). As the name for a kind of maker, however, “engineer” and its cognates originated in the late Middle Ages to designate builders and operators of battering rams, catapults, and other “engines of war.” This military focus remained primary as late as Noah Webster’s American Dictionary of the English Language (1828), which defined the engineer as “a person skilled in mathematics and mechanics, who forms plans of works for offense or defense, and marks out the ground for fortifications.” From classical times, planning or designing in the civilian realm, for peace rather than for war, was the work of the architect — as illustrated by Vitruvius’s De architectura (1st century CE), which dealt with urban planning, options in building materials, aesthetic principles, general construction strategies, hydraulics, geometry, mechanics, and so forth. In the 18th century, as the Industrial Revolution began a militarylike exploitation of nature, it was the Englishman John Smeaton who coined what it would have been reasonable to take for an oxymoron, the term “civil engineer.” (In English, civil engineering has become restricted to the design, construction, and maintenance of roads, bridges, water supply and sanitation systems, railroads, and such, although in some continental European contexts civil engineering continues to cover all non-military engineering.) Importantly, the classic definition of engineering as a civilian enterprise, formulated by Thomas Tredgold for the royal charter of the British Institution of Civil Engineers (1828), used standard genus-species form to distinguish engineering, not within the genus of science but of art: “Engineering is the art of directing the great sources of power in nature for the use and convenience of man.” Interestingly, almost a hundred years later an engineering professor at a British technical college opened a textbook with a definition of technology that simply shifted the genus: technology is “that branch of knowledge which deals with the processes and apparatus employed in the conversion of the raw products of nature into finished articles of utility” [Charnock, 1916, p. 1]. In English these definitions of engineering (and sometimes of technology as well) have found repeated echoes without and within the technical community. Webster’s Third New International Dictionary (2002), for instance, defines engineering as “the application of science and mathematics by which properties of matter and the sources of energy in nature are made useful to people.” The McGraw-Hill Dictionary of Scientific and Technical Terms (10th ed., 2007) describes engineering as “the art of directing the great sources of power in nature for the use and the convenience of humans.” Supplementing this general definition, many engineers claim that design for efficiency is the essence of engineering. Ralph J. Smith, an influential engineering educator, has argued that “the conception and design of a structure, device, or system to meet specified conditions in an optimum manner is engineering.” Furthermore, “it is the desire for efficiency and economy that differentiates ceramic engineering from the work of the potter, textile engineering from weaving,

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and agricultural engineering from farming.” “In a broad sense,” Smith concluded, “the essence of engineering is design, planning in the mind a device or process or system that will effectively solve a problem or meet a need” [Smith et al., 1983, pp. 10-12]. Complexities of usage are nevertheless manifested by the ways “engineer” can refer, in a more restricted sense, to one who operates engines, as in the expression “railroad engineer.” But the operating engineer is such by virtue of using skills rather than any systematic body of knowledge. Railroad engineering insofar as it involves more than the skill of using engines, is not expressed in the operating of locomotives but in the designing of railroad systems. Engineering is thus identified as a profession possessing the systematic knowledge of how to design structures, products, or processes, a profession that (as the standard engineering curriculum illustrates) includes some pure science and mathematics, the “applied” or “engineering sciences” (e.g., strength of materials, thermodynamics, electronics), and aims to meet some social utility. The existence of engineering or technological sciences presents another definitional challenge closely related to that of technology. To begin, engineering/technological sciences (usually in the plural) are to be distinguished from technological science (in the singular). Technological science exists external to engineering and functions as a synonym for Technologie rather than Technikwissenschaften. By contrast, “engineering science” or Technikwissenschaft (which can legitimately be used in the singular) is constituted by a knowledge production activity internal to engineering. Two useful efforts to deal with the challenge of defining this activity and its cognitive products, which include as well reference to other significant discussions of the topic, can be found in the work of G¨ unter Ropohl and Sven Ove Hansson. Drawing on earlier analyses [especially the largeonig, 1995], Ropohl [1979] argues for distinguishscale illustrated examination of K¨ ing the engineering sciences from natural sciences in terms of objectives, objects, methodology, characteristics of results, and quality criteria. (Complementary contributions to this discourse on distinguishing Technik and Technikwissenschaften in the German scholarly community can be found in [Lenk and Moser, 1973] and [Friedrich, 1999].) Hansson [2007], in turn, argues for understanding the engineering sciences as efforts to bring engineering into the academic world by adaptation of the methods of science oriented not toward explaining the facts of nature but artifacts. However, rather than formulating a definition in terms of genus and species (as a specific application of science), Hansson argues for identifying the engineering or technological sciences in terms of a set of six major characteristics: a focus on the human-made, attention to design practice, use of functional analysis, evaluation with category-specified value judgments, utilization of restricted idealizations, and the eschewing of precise mathematical solutions in favor of close approximations. As Hansson also interestingly notes, there are parallels between the development of the engineering and medical sciences. Both emerged from context specific efforts to transpose the methods of the natural sciences into a realm of technical professional practice.


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The most careful effort to define engineering as a profession rather than as an art or knowledge is that of philosopher Michael Davis. As he rightly notes, “Defining a field is more than semantics” [Davis, 1998, p. 31]. According to Davis, semantics is subservient to social organization, and engineering is defined in relation to engineers: engineering is what distinguishes that self-organizing professional group called engineers. Despite reservations, he ultimately favors a modified version of the definition from the Canadian Engineering Qualifications Board: “The ‘practice of professional engineering’ means any act of planning, designing, composing, evaluating, advising, reporting, directing or supervising, or managing any of the foregoing that requires the application of engineering principles, and that concerns the safeguarding of life, health, property, economic interests, the public welfare or the environment” [quoted in Davis, 1998, pp. 203204, note 6]. Davis himself avoids the mistake of including the definitum in the definition with the following reformulation: Being a professional engineer requires being accepted in a self-organizing professional community on the basis of “(1) specific knowledge and (2) commitment to use that knowledge in certain ways” [Davis, 1998, p. 37]. This approach reduces the centrality of design in favor of professional community engagement. For many professional engineers, engineering and technology are coterminous [see, e.g., Petroski, 1996]. But beginning in the 1960s, a new, subordinate category of “engineering technology” emerged within American engineering to describe the skills and instruments of those who function as support personnel or follow the directions of engineers. This category arose as a result of new educational programs established to produce technical workers operating at an intermediate level between skilled workers and engineers. Typically taught at the community college level, programs in engineering technology emphasize the hands-on aspects of engineering and require less advanced coursework in mathematics and basic sciences. Many introductory engineering textbooks (e.g., [Kemper, 2001; Wright, 2002]) now describe a spectrum of activities that run from those of the engineer (more knowledge-based) through technologist and technician to craftsperson or artisan (more hand-skill based). Any philosophy of technology emerging within the engineering and scientific communities will need to be sensitive to the tensions between these different linguistic usages. 4

IN THE HUMANITIES

In marked contrast to the discourse of science and engineering stands humanities discourse. Construed narrowly, the “humanities” — German Geisteswissenschaften or Kulturwissenschaften, as distinct from Naturwissenschaften and Technikwissenschaften — enacts a categorization of knowledge that emerged in higher education in the late 19th and early 20th centuries. The humanities category consists of a heterogeneous set of disciplines that remained after the newly professionalized natural sciences laid claim to their own spheres of knowledge and initially included the social sciences (German Sozialwissenschaften or Gesellschafts-

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wissenschaften), which were subsequently separated in part by their own trajectory of professionalization. For the U.S. National Endowment for the Humanities, for instance, the humanities include the study of language, linguistics, literature, history, jurisprudence, philosophy, archeology, comparative religion, ethics, history and criticism of the arts, and non-quantitative social sciences. But for present purposes, humanities discourse is taken to be part of a continuous tradition of critical reflection on what it means to be human, a tradition much older than the 19th century and manifest prominently in philosophy, literature, religion, the fine arts, and similar fields. Philosophical reflection about making and using activities has deep roots in the European intellectual tradition, but there is no typical humanities approach to technology. Certainly most humanities discourse is produced by intellectuals who experience technology primarily from the outside. Until recently, much of this discourse displayed significant ignorance if not hostility toward the industrial arts. The American pragmatist John Dewey often criticized this attitude of “profound distrust of the arts” and “disparagement attending the idea of the material,” which was expressed philosophically in “the sharp division between theory and practice” [Dewey, 1929, pp. 2-3]. At the same time there exists an alternative humanistic tradition that has affirmed the dignity of the mechanical arts, from Hugh of St. Victor through F. Bacon to Karl Marx. Theorists of the fine arts such as the British critic John Ruskin condemned modern technology, while others such as the Italian futurist Filippo Marinetti embraced the “machine age.” Some conservative German intellectuals of the early 20th century attacked the soullessness and materialism of modern technology, while others romanticized technological creativity, sometimes using the term “technological eros,” or linked an explosion of productivity and power variously with capitalist, national socialist, and communist ideologies. In early 21st century contexts, democratic and non-democratic alike, there continues to be a divide between humanist public intellectuals taking critical versus promotional stances; one need only consider the conflicting views with regard to post- or transhumanist technologies between, for example, the American Leon Kass and the Swede Nick Bostrom. Despite some differences of opinion, perhaps the most persistent strain in the European philosophical tradition has been a recurring theme of critical distrust in the practical arts and technology. Along with many others, Cuomo [2007] has traced this uneasiness to a criticism of techne in classical Greek philosophy. Reflecting a common interpretation, she argues that in the classical humanities techne, being teachable, was seen to pose a threat to a social order based on birth, because technicians could use acquired skills for social mobility. She notes, for example, Plato’s concern in the Republic with keeping technicians in their place, subordinate to the guardians, by promulgating the “noble lie” that one’s position in society is determined by an innate metallic constitution: gold for guardians, silver for soldiers and bronze for farmers and artisans. Similarly, Plato stressed the moral neutrality or ambiguity of techne, which thus depends on external ends for its moral value. Aristotle drew a similar distinction between poiesis and praxis,


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the former interpreted as production that is properly subordinated to the latter understood as moral action (Nicomachian Ethics VI, 4;1140a2). In effect, suggests Cuomo, such claims about techne justified a rejection of the idea that technicians either could or should exercise “control of their own activities” [Cuomo, 2007, p. 30] and became the font of long tradition of ethical and political concern about technics. In the 20th century, however, this tradition was given new life in response to the rise of an effort at the rationalization of technics and other forms of practice in industrial manufacture, consumer economics, behavioral psychology, business management, and totalitarian politics. In each case, the claim was that forms of manipulation and control, which had once depended on personal experience and insight, could be replaced and enhanced by their more conscious and systematic development. This effort was criticized by Jacques Ellul [1954], for instance, as an effort to turn everyone into technicians who at the same time failed to recognize the extent to which technique is inherently unable fully to master its own consequences. For Ellul, there are always unintended consequences to human action, even and perhaps especially when action becomes enlarged in power and scope through technology. Exploring this tradition of distinctly modern humanities criticism, Joseph Dunne places Aristotle’s analysis of techne and phronesis into dialogue with John Henry Newman, R. G. Collingwood, Hannah Arendt, Hans-Georg Gadamer, and J¨ urgen Habermas, all of whom have tried to rethink the nature of practical activity in the face of pressures toward technization. Stimulated especially by efforts to transform pedagogy into an efficient technique, Dunne’s study questioned “the attainability of technical mastery” in many areas of life by arguing that “practical knowledge . . . [is] a fruit which can grow only in the soil of a person’s experience and character” [Dunne, 1993, p. 358]. Despite this tradition of concern about what is now called technology, within humanities discourse there is a curious paradox with regard to the English term. In Dunne, for instance, it does not even occur in the index. Indeed, from the early 19th century forward, poetry and fiction — and especially modernist poetry and fiction — increasingly dealt with modern forms of technics, yet there were few if any significant poems, short stories, plays, or novels in which “technology” appeared in title or text. One can find closely related terms such as “machine,” “industrialization,” “invention,” and “applied science,” along with particular technologies (such as trains and bridges) in titles and many texts. The whole genre of science fiction might more accurately be labeled “technology fiction.” Even beyond that mislabeled genre, imaginative literature dealing with technology has favored terms such as “science” or “machine”; think of Mary Shelley’s Frankenstein (1818), Samuel Butler’s Erewhon (1871), Mark Twain’s A Connecticut Yankee in King Arthur’s Court (1889), Henry Adams’ The Education of Henry Adams (1901), Aldous Huxley’s Brave New World (1932), and Kurt Vonnegut’s Player Piano (1952). In each case, although the focus of interest was the problematic relations between advanced technical artifacts and human affairs, the term “technology” is conspicuous by its absence. On the one hand, the absence of the term

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even in technology-themed fiction of the 1950s supports the idea that “technology” only became a keyword relatively recently. On the other, as a term covering general phenomena, “technology” apparently lacked the specificity required by literature, even when that literature dealt with some aspect of industrialized making and using. Appropriately enough, then, the word “technology” made its initial sustained appearance in the humanities in the 1960s via works of literary criticism — criticism that called attention to a second curious and perhaps ironic feature: Literature, especially modernist literature, manifested a paradoxical attitude toward technology. While such literature more likely than not criticized technological culture, it also often implicitly integrated technology into culture. Thomas Reed West’s Flesh of Steel: Literature and the Machine in American Culture, for instance, documented “a dominant attitude toward the machine and its disciplines [as] one of repudiation” [West, 1967, p. 133]. Wylie Sypher’s Literature and Technology: The Alien Vision [1968], by contrast, demonstrated a modernist obsession with a technological aesthetic, and Cecelia Tichi’s Shifting Gears: Technology, Literature, Culture in Modernist America similarly explored the extent to which “the culture of the gear-and-girder technology was a collaborative effort of the engineer, the architect, the fiction writer, and the poet” [Tichi, 1987, p. 16]. Two other classics of literary criticism further explored related issues: Leo Marx’s The Machine in the Garden: Technology and the Pastoral Ideal in America [1964] and Herbert Sussman’s Victorians and the Machine: The Literary Response to Technology [1968]. Note how even in both Marx and Sussman the primary term was “machine,” the secondary one “technology.” Only in subsequent decades did humanities studies increasingly foreground the term “technology” while occasionally moving a particular technology into the subtitle — as in, for instance, Carolyn Marvin’s When Old Technologies Were New: Thinking about Electric Communcation in the Late Nineteenth Century [1988], Tim Armstrong’s Modernism, Technology and the Body: A Cultural Study [1998], or Pamela Thurschwell’s Literature, Technology and Magical Thinking, 1880—1920 [2001]. (See also [Greenberg and Schachterle, 1992].) In her World War I novel, One of Ours (1922), Willa Cather described the reaction of newspaper readers in the central United States to reports of the fall of the forts at Liège, which were reduced in a few hours by siege guns. . . which evidently could destroy any fortifications that ever had been, or ever could be constructed. Even to these quiet wheat-growing people, the siege guns before Liège were a menace; not to their safety or their goods, but to their comfortable, established way of thinking. They introduced the greater-thanman force which afterward repeatedly brought into this war the effect of unforeseeable natural disaster, like tidal waves, earthquakes, or the eruption of volcanoes.


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An “unprecedented power of destruction had broken loose in the world” for which “none of the well-worn words descriptive of human behavior seemed adequate” (Cather, One of Ours, Book II, chapter 9, paragraphs 2 and 3). The emergence “technology” to fill the terminological gap noted by Cather can be documented in two manifestations of synthetic humanities scholarship: libraries and encyclopedias. With regard to libraries, note how in 1876 Melvil Dewey’s decimal classification system grouping number of the 600s “Useful Arts” (which included medicine, engineering, agriculture, domestic economy, ordnance, etc.) was sandwiched between the 500s “Natural Sciences and Mathematics” and the 700s “The Arts.” Originally “technology” only occurred in the compound “chemical technology,” as one of several useful arts along with pyrotechnics, “wines, liquors and ales,” and metallurgy. In other words, “technology” was not a central conceptual category in 19th century English [Dewey, 1876, pp. 18-20]. “Useful Arts” remained one of the nine fundamental categories in the Dewey system until after World War II, when it was replaced first by “Applied Science” and then in 1958 by “Technology.” The same year witnessed the change of name of the H. W. Wilson Company’s Industrial Arts Index of technical literature (created in 1913) to the Applied Science and Technology Index. By contrast, the original U.S. Library of Congress classification system, which began to be conceptualized in 1897 and was, like other reference frameworks, quite conservative in its principles, had by 1904 already made “technology” one of its 20 top-level categories. But this was not true from the beginning. Rathar than “technology,” a 1901 draft grouped together “Useful Arts, Agriculture, [and] Manufactures” in one category [Miksa, 1984, p. 24]. With development of the LC system, “technology” was not only stabilized as a primary category that included all forms of engineering, engineering science, manufactures, handicrafts, and home economics, but also became distributed in subject headings across any number of other categories, from philosophy to social sciences and fine arts. Although these subject headings appeared as additions to the original structure, it is nevertheless significant that “technology” became salted throughout in so many classificatory combinations. With regard to encyclopedias, consider as a representative case the Encyclopaedia Britannica: A Dictionary of Arts, Sciences, Literature and General Information, the first edition of which was published in 1771 in three volumes in pale imitation of the great French Encyclopédie (1751-1772). In none of the first ten editions does the word “technology” even appear. Because the term was indeed extant in English, the failure of the editors to include it demonstrates their judgment of its relative unimportance. “Technology” finally appeared in the classic 11th edition (1910-1911), but just twice in subsidiary roles. Only in the reconfigured 15th edition (1974 et seq.) does “technology” become a major theme — a process that takes place in conjunction with a generally intensified appreciation of its presence [see Oldenziel, 1999]. When “technology” arrived on the encyclopedic stage, it did so in a big way, and no doubt under the influence of developments in other fields of the humanities.

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Under tutelage of neoaristotelian philosopher Mortimer Adler, the 15th edition included what was termed a Propaedia, which classified knowledge into ten subject areas: (1) matter and energy, that is, the physical sciences, and (2) the earth, moving on through (3) the sciences of living things, then (4) human life, and finally (5) human society, (6) art, (7) technology, (8) religion, (9) history, and (10) knowledge itself, including science and philosophy. Although its proximity to art is revealing, technology was easily the most anomalous of these major categories; it was, for instance, the only one that did not appear at all in the first edition of the Britannica. (It was also the only category not closely linked to one of Adler’s 101 great ideas, as surveyed in The Great Ideas: A Syntopicon of Great Books of the Western World of 1952.) As part seven, technology was approached from three main perspectives: its historical development and social impact (particularly on work), its internal divisions (energy conversion, tools, measurement and control, extraction of raw materials, industrial production), and its major fields of application (agriculture, industrial production, construction, transportation, information processing, the military, the city, earth and space exploration). In a kind of echo, “The Technological Sciences” were considered again in part ten as a seventh and last subdivision, with a four-part analysis in terms of history, professional branches (civil, aeronautical, chemical, electrical, mechanical, etc., engineering), agricultural sciences, and interdisciplinary technological sciences (bionics, systems engineering, cybernetics). One of the closely related areas of activity in the humanities that surely influenced this encyclopedic scheme had been taking place in conjunction with a series of international expositions or world fairs that began in 1851 with the Crystal Palace in Hyde Park, London, under the title “Great Exhibition of the Works of Industry of All Nations.” (The 1933-1934 Chicago World’s Fair had the famous motto, “Science Finds, Industry Applies, Man Conforms” — implicitly conceiving of industry as applied science.) It was in conjunction with the 15th exhibition, held in 1900 in Paris (in response to which The Education of Henry Adams included a chapter on “The Dynamo and the Virgin”), that a proposal developed to create a parallel intellectual inventory of the types of knowledge being exhibited. This Congress of the Arts and Science took place in association with the St. Louis exhibition of 1904 and resulted in an eight-volume assessment of the state of knowledge across seven broad areas [Rogers, 1905-1906]: normative sciences, historical sciences, physical sciences, mental sciences, utilitarian sciences, regulative sciences, and cultural sciences. The fifth of these areas, utilitarian sciences, was composed of three divisions: medical, economic, and technological sciences. The technological sciences in turn included such disciplines as civil engineering, mechanical engineering, electrical engineering, and more. (It is noteworthy that in volume one, which provided a conceptual overview, these disciplines were titled mechanical technology, electrical technology, etc., whereas when reviewed at length in volume six they were denominated as branches of engineering.) There was thus emerging during the same period as Cather’s novel a new term broader than machines to refer to an “unprecedented power” — creative as well


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as destructive — that “had broken loose in the world” and for which “none of the well-worn words . . . seemed adequate.” The need in the humanities for a new term, less particular than machines but inclusive thereof, has also been analyzed by L. Marx [1997]. For Marx the need was two-fold: ideological and substantive. Ideologically, new ideas about progress, in the 1800s so closely associated with discovery and invention, increasingly called for a more expansive and abstract objective correlative. Substantively, the emergence of systems or networks of machines also demanded a more abstract and general terms than “machines” or “industry” to reference phenomena such as linkages of canals, railroads, telegraphs, radio, and more — which were themselves becoming interconnected. The idea of tools or machines or, for that matter, any other material artifacts did not begin to convey the complex, quasiscientific, corporate character of the new sociotechnical formations that emerged at that time. The curious fact is that the discursive triumph of the concept of technology is in large measure attributable to its vague, intangible, indeterminate character — the fact that it does not refer to anything as specific or tangible as a tool or machine. [Marx, 1997, p. 981] In the humanities, then, during the last half of the 20th century, “technology” increasingly began to reference the complex of industries, industrial products, and technical infrastructures that were variously enmeshed with but also distinct from modern science. Humanities scholars struggled to identify appropriate language to reference these new forms of making and using that increasingly transformed not just agriculture, industry, transportation, and communication, but elements of higher culture as well. Looking back, scholars such as Lewis Mumford argued for greater exploration of the interactions between Technics and Civilization [1934], pointing out how the mechanical clock transformed experiences of time and the printing press literacy. Looking forward, scholars such as Walter Benjamin (in an essay written in 1935-1936 but not published until after his death) analyzed the interaction of technology and culture in “Das Kunstwerk im Zeitalter seiner technischen Reproduzierbarkeit” [1961]. The original English title for this influential essay was “The Work of Art in the Age of Mechanical Reproduction” — rendering the German technischen as “mechanical” rather than the more literal “technical.” Had Benjamin’s concerns been articulated during the last third of the 20th century, they would have considered a manifold of digital reproduction and image manipulation that exceeded anything merely technical or mechanical, so that the pivotal English adjective would have been “technological.” Likewise, by the last quarter of the century, in fictions as different as Thomas Pynchon’s Gravity’s Rainbow (1973) and Robert Pirsig’s Zen and the Art of Motorcycle Maintenance: An Inquiry into Values (1974), “technology” had become a term of sufficiently definite reference — and seemed to include not only industrial making activities and their social or institutional extensions but the knowledge involved with such activities, the products both physical and organizational, and the use of such products as well as the diverse intentions and motivations bound

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up therewith. Technology had become a way of life, a form of consciousness, an attitude toward the world — although Don Delillo’s White Noise (1985) continued to eschew the term in favor of a plethora of more precisely imagined objective correlatives such as toxic events, televisions, traffic, and drugs. Nevertheless, late in his life, Dewey acknowledged the rise of this new term by suggesting a preference for “technology” over “instrumentalism” for his analysis of human experience: “It is probable that I might have avoided a considerable amount of misunderstanding if I had systematically used ‘technology’ instead of ‘instrumentalism’ in connection with the view I put forth regarding the distinctive quality of science as knowledge” [Dewey, 1944, p. 285n]. Dewey’s connection of “technology” with “instrumentalism” suggests a link between the concept of “technique” in continental European languages and the English “technology.” A number of humanities scholars have apparently sought to preserve the continental emphasis by embracing the term “technics.” Across a long and productive life, Mumford, a literary and social critic, always preferred the term “technics” to “technology.” (For an extended defense of Mumford’s usage, see Fores [1981].) In some early work so did the political theorist Langdon Winner and the philosopher Don Ihde. Winner merged the two terms in Autonomous Technology: Technics-Out-of-Control as a Theme in Political Thought [1977]. Ihde maintained a subtle distinction in Technics and Praxis: A Philosophy of Technology [1979], where “technics” was identified with the instrumental use, but then shifted in Lifeworld and Technology: From Garden to Earth [1990] to “technology” as an inclusive term. To some extent, then, English usage of technics instead of technology appears to be a distinction without a difference. Nevertheless, it is reasonable to postulate “technics” as less expansive than “technology,” which is typically perceived in the humanities as having become a distinctive way of being in the world, a form of consciousness, and thus — like religious belief or democracy — a phenomenon deserving of expressive critical assessment and careful reflective analysis. Philosophy of technology, arising within the context of this humanities community of discourse, thus initially took on a decidedly moral tone — often censorious or negative, sometimes celebratory and affirmative, always parsing and distinguishing. 5

IN THE SOCIAL SCIENCES

As a distinct area of knowledge, the social sciences emerged during the 19th century, based on an Enlightenment faith in the applicability of reason to human affairs, but refracted through the lenses of the Industrial and French Revolutions. Only in the early 20th century, however, did the social sciences become securely institutionalized as a stable set of disciplines. Their complexities and contested origins played out in shifting constellations of economics, sociology, anthropology, and political science, as well as in debates about the nature of social science research, theory construction, relations to the natural sciences and to the humanities, and relevance to practice and policy. Throughout all these originating struggles,


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there existed an effort to bring science to bear on concerns traditionally associated with the humanities, reflecting a desire to be more directly relevant to human interests than the physical sciences, yet with a methodological effectiveness that transcended the often apparently impotent rhetoric typical of the humanities. Yet just as with the humanities, there is no canonical social science understanding of or approach to technology, particularly across disciplines. The social sciences have nevertheless directed more sustained attention to the definition of technology than any other area of scholarly inquiry. In general, “technology” has a broader meaning in the social sciences than it does in engineering, but one that is more determinate than in the humanities. To some extent, the social-science usage includes both the expansive and the restrictive connotations found in engineering, while including a critical stance typical of the humanities. It emphasizes not so much the study of any internal features of technical processes or engineering sciences but of those processes and their sciences as socially effective givens, with an interest directed toward how such processes and forms of knowledge arise from and influence society. The first sustained use of a “technology” cognate to cover industrial processes, Beckmann’s Technologie, occurred (as already noted) in the context of an early form of German social science known as cameralism. Indeed, questions posed by technological change, in particular the social dislocations associated with industrialization, provided much of the early focus in the social sciences. In the 19th century, the “machinery question” was central to British political economy and social reform movements, as Maxine Berg [1980] has argued. From Smith’s analysis of pin manufacture to K. Marx’s dissection of the factory, political economists incorporated industrial technology into their analyses. Yet for all the attention paid to industry, 19th century social sciences rarely made technology itself the focus of analysis. As Frison [1998] has observed, the terms “technology” and “technique” are largely absent from the treatises by Smith, J. S. Mill, and other key works of classical British political economy. Furthermore, these authors show little evidence of having had a general concept of technology and technological change. For example, one key debate in the early 19th century arose over claims by Thomas Malthus that diminishing marginal productivity of land would inevitably produce stagnation. David Ricardo, among others, contested Malthus by arguing that the tendency toward stagnation was “more than counterbalanced by the improvements in machinery, by the better division of labor, and by the increasing skill, both in science and art, of the producers” (On the Principles of Political Economy and Taxation, ch. 5, paragraph 4). Present-day economists would, of course, make the same argument in terms of technological change, but “technology” did not become a key term in economic theory until the 1930s. The most sustained discussion of industrial technology in classical political economy is found in Marx’s Das Kapital, volume one, especially the chapter on “Machinery and Large-scale Industry.” Even Marx, however, did not deploy a general concept of technology, although he combined cameralist Technologie with a focus

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on machinery drawn from British political economy. Marx’s most general description of Technologie occurs in a lengthy footnote to his discussion of the concept of the machine. This note began by calling for “a critical history of technology” that would demonstrate the collective basis of invention. It then constructed an analogy to Charles Darwin’s interest in the history of “natural technology, i.e., . . . the formation of plant and animal organs as productive instruments for the lives of plants and animals.” Marx proposed a similar “developmental history of the productive organs of social humans,” which led to one of his most well-known statements: “Technologie discloses [enth¨ ullt] the active relations of humans with nature, the immediate production processes of their lives, and thereby also their social life relations and the mental presentations that flow from them” [Marx, Kapital I, ch. xiii, n4]. One can certainly interpret this use of “technology” as a direct reference to material processes of production, a usage comparable to meanings. Yet elsewhere in Das Kapital Marx made it clear that he interpreted Technologie along cameralist lines as a Wissenschaft concerned with directing the process of production. In the note Marx was apparently conceptualizing Technologie as a potential (social) science of production that could help reveal the material basis of social relations and ideologies. Although this view was not developed further by Marx or his followers, subsequent German social scientists were influenced by Marx’s insistence that the technical process of production was central to human history. Much of the work of Max Weber and Werner Sombart, for example, can be read as a response to the perceived determinism of Marxist theory. Yet when these scholars addressed Marx’s analysis of production, they did so using the term Technik rather than Technologie. Technik entered German social science through the discourse generated by German engineers in the late 19th century, as the phenomenon it referenced was attended to by the work of Gustav Schmoller, Weber, Georg Simmel, and especially Sombart. In the first edition of his Der Moderne Kapitalismus [1902], Sombart discussed at length die neuen Technik (modern technology). He expanded on this theme the following year with an analysis of Technik in 19th century Germany, stressing the shift from empirical to scientific Technik. Sombart and Weber elaborated their views in 1910 at the first conference of the Deutsche Gesellschaft f¨ ur Soziologie (German Society for Sociology), where Sombart presented a paper on “Technik und Kultur.” Both Sombart and Weber rejected Technik as an independent variable in human affairs, instead insisting that Geist (spirit or mind) played a more fundamental, causal role. Weber briefly discussed Technik in his famous analysis of types of action in Wirtschaft und Gesellschaft [1914], where he drew a sharp distinction between Technik and Wirtschaft (or technology and economy). Weber restricted Technik to the question of the optimal means to a given end, arguing that any consideration of costs implied choices among ends; such choices, for Weber, belonged to the sphere of economic action. This understanding in effect removed Technik from the domain of culture, reducing it to an almost mechanical application of


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scientific principles. In France between the two world wars a number of social scientists — among them Marcel Mauss, Ignace Meyerson, Andé Leroi-Gourham, and Lucien Febvre — developed a similar discourse about la technique. In comparison to the Germans, this French discourse focused more on anthropology, social history, and workers ([Long, 2005]; see also [Salmon, 1984]). The English term “technology” became significant in American social sciences in the early 20th century largely through its use to translate the discourse of “technique” from continental European languages. Before 1900, “technology” remained of marginal significance in the social sciences, used mainly as a classificatory heading. For example, the 1882 constitution of the Anthropological Society of Washington made “Technology,” defined as “the science of the arts,” one of the four main sections of the society, alongside Somatology, Ethnology, and Philology. In this same time period, however, English-speaking social scientists began to take notice of the German discourse of Technik, especially in the United States, where German universities had become models for higher education. The most suitable translation the central term in German social-science discourse on Technik was “industrial arts,” a phrase that in the process of being appropriated for programs of manual instruction in American secondary schools, U.S. social scientists gravitated instead toward “technique” or “technology” or occasionally “technics” (e.g., [Seligman, 1902]). The result was a terminological muddle. One scholar who played a pre-eminent role in transposing the German discourse into the American context was the iconoclastic social scientist Thorstein Veblen. Veblen took the concept of Technik directly from the works of Schmoller and Sombart and merged it into the existing meaning of “technology” as the science of the industrial arts, thus creating a new concept that transcended received meanings. He frequently equated technology with the “state of the industrial arts,” drawing a parallel with the “state of the art,” a concept used to determine priority in American patent law. In his hands technology came to be understood as a universal characteristic of human cultures and an alternative to the older idea of art, which by that time had become thoroughly aestheticized. Veblen [1906] provided the first explicit discussion of the relationship between technology and science, one that posited the two as fundamentally distinct spheres of human culture linked through a materialist sociology of knowledge. Veblen [1908] undertook the first significant analysis of the economic role of technology, conceptualized as productive knowledge and skills belonging collectively to an entire community. Given its novelty, Veblen’s concept of technology was perhaps a bit too subtle for the time. Although his works were widely read, he had few students and no true disciples. A number of influential American social scientists adopted his usage, but some of the finer points fell away as the term became more common. In particular, a dialectic understanding of the science-technology relationship was replaced by the assumption that technology equaled the application of science and technology, which became firmly linked to a dominant belief in material progress. Such meanings were strongly present in the work of two leading American social scientists who embraced the term before World War II, the historian and political scientist

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Charles A. Beard [1927] and the sociologist William F. Ogburn [1938]. A fairly sophisticated concept of technology did find a home in institutional economics, a dissenting branch of the field that drew on Veblen’s ideas, but the dominant neoclassical paradigm generally viewed technology as exogenous to the economic system [Lower, 1987]. Yet the German discourse on Technik continued to affect American social science, as can be seen in the work of Talcott Parsons, one of the pre-eminent American social theorist of the 20th century. Parsons earned his doctorate from the University of Heidelberg in the late 1920s and in the process absorbed many ideas from European social theorists such as Weber and Sombart. His early publications examined recent German writings on capitalism, many of which dealt significantly with Technik, especially Sombart’s. Parsons’ earliest discussions of technology were framed primarily in terms of “technique,” often translated directly from the German. But he gradually altered his terminology from “technique” to “technology” while importing into English the two major German meanings of Technik as practical arts and as means to an end (e.g., [Parsons, 1935]). His sociologist colleague at Harvard, Robert K. Merton, underwent a similar German-influenced transition from “technique” to “technology” in the late 1930s [Merton, 1935]. This view identified technology primarily with productive machinery, as in the sevenpage entry in the Encyclopaedia of the Social Sciences of 1934, which lacked a clear definition and remained heavily dependent on references to German and French literature (see [Lederer, 1934]). After World War II, “technology” assumed a secure if somewhat marginal place in anthropology, economics, and sociology. The definition of technology as applied science, which was common in postwar science and engineering, had much less currency in the social sciences. Social scientists generally embraced a broad understanding of technology. This broad definition received explicit sanction in the authoritative 17-volume International Encyclopedia of the Social Sciences. The relevant entry, which was twice the length of its predecessor, offered the following explicit definition: Technology in its broad meaning connotes the practical arts. These arts range from hunting, fishing, gathering, agriculture, animal husbandry, and mining through manufacturing, construction, transportation, provision of food, power, heat, light, etc., to means of communication, medicine, and military technology. Technologies are bodies of skills, knowledge, and procedures for making, using and doing useful things. They are techniques, means for accomplishing recognized purposes [Merrill, 1968, pp. 576-577]. Some social scientists have nevertheless continued to contest the boundaries of technology. For example, the short, three-page entry in a second edition of the International Encyclopedia of the Social Sciences retreats to a definition of technology as “the underlying production methodology through which inputs or resources are converted into output (goods and services)” along with the further


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claim that at any point in time “there is one best way to produce a good or service” ([Goel, 2008, p. 302]; see also [Tantoush, 2001, p. 15503]; and [Mokyr, 2008, p. 217]). Others continue to limit it to modern industry or to distinguish between “technics” and “technology,” letting the former stand for prescientific arts and crafts and the latter for more sophisticated engineering (see, e.g., in Spanish, [Medina, 1985]). More characteristic, however, has been an expansive view associated with the history of technology. In history, which tends to bridge the social sciences and the humanities, technology has, from the point at which it became a theme for narrative analysis, been defined broadly. According to the preface to the first comprehensive internalist history, technology would be construed as “how things are commonly done or made,” including “what things are done or made” [Singer et al., 1954, p. vii]. This view was modestly criticized by the editors of a subsequent social history as “so broad and loose [as to encompass] many items that scarcely can be considered as technology” [Kranzberg and Prusell, 1967, p. 5]. Yet a philosophical comparison of definitions in these and basic histories of technology in French and other languages, including various historiological debates, continued to favor an inclusive over a more restricted definition [Mitcham, 1979]. Indeed, one historian and management theorist even defended the idea that technology should include not just “how things are done or made” but “how [the human being] does or makes,” interpreted not so much in terms of human nature trying to control the environment as a cultural extension of the processes of biological evolution oriented toward transcending human limitations [Druker, 1959, p. 28] From this perspective, technology includes not only successful but failed human undertakings, insofar as they are oriented (consciously or not) toward making and using — so that the history of technology includes a history of work, invention, economics, politics, science, and so forth. This is also an approach characteristic of socially oriented philosophers such as Larry Hickman who, building of the philosophy of Dewey, argues for naturalizing technology “as a cognitive activity within the evolutionary history of complex organisms” [Hickman, 2001, p. 21]. 6 CONCLUSION AND IMPLICATIONS By the last quarter of the 20th century, “technology” had become a well established keyword, although one influential treatment of 131 such terms placed it in the bottom rung with regard to analytic attention space [Williams, 1983]. At the same time, the meanings of technology varied significantly across communities of discourse, as revealed by the preceding review of understandings and definitions in science and engineering, in the humanities, and in the social sciences. Such variations suggest the need for context-dependent approaches to the philosophy of technology as understood within each of the three basic scholarly communities. Within and across each community, one definition or definitional strategy may well be more appropriate than another, resulting in complementary philosophies of technology and the engineering sciences.

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With regard to the possibility of complementary philosophies, two points can be argued. First, although phenomena can be parsed in an indefinite number of ways, sorting out, classifying, and defining nevertheless remain fundamental to human acting and thinking. A context dependency of definitions does not mean they can be dispensed with or treated with indifference. Metaphysically, definitional contingency can be interpreted as disclosing the extent to which all presence is limited or partial, but not for that reason unreal (see [Harman, 2005]). Epistemologically, to define and classify are inherently human activities. Second, any act of classifying and sorting has manifold implications, from the practical and cognitive to moral and aesthetic. To paraphrase one argument relative to this point, we live in, on, and around definitions, which reflect and shape our practical, epistemic, ethical, and aesthetic choices and experiences. Both epistemologically and morally, it is important “to produce flexible classifications whose users are aware of their political and organizational dimensions and which explicitly retain traces of their constructions” [Bowker and Star, 1999, p. 326]. Comparison has already been made to the pursuit of definitions in other regionalizations of philosophy such as the philosophy of science, of religion, and of language. In each case, context functions to help specify definitional strategies, which in turn set the stage for regionally distinctive philosophical discussions. In the first case, especially in the view of scientists themselves, science is commonly argued to be distinguished by its method. This argument is based on a self-understanding within the rather tightly coupled scientific community about the importance of method, and the fact that science aspires to be a progressively unfolding cognitive activity. Scientists themselves often see the scientific method as essential and prescriptive, a view endorsed by many philosophers. For example, as interpreted by Karl Popper [1963], science involves the advancement of knowledge claims that are able (in principle if not in current practice) to be falsified by some test and have in fact survived at least one such test (although they may in the future fail to satisfy other tests). Yet debate about the adequacy of any one description of the scientific method has become a basic theme in the philosophy of science, while social scientists have even challenged the self-understanding among scientists (and some philosophers) of science as a method-based community (see [Pinch and Collins, 1993]). In a manner like scientists, some engineers have proposed to define engineering in terms of method [Koen, 2003], leading again to discussions about the adequacy of various proposals — as well as to social science questioning of the extent to which engineering lives up to its own methological ideals. Additionally, there are reasons to question whether the engineering community is as tightly knit or well formed as that of science. In the philosophy of religion, by contrast, there is no equivalent to the scientific or engineering communities. The many religions that scholars so designate fail to see themselves as a well formed or unified community. Within each religion there are groups such as Catholic Christians or Theravada Buddhists that appear more or less well established, but Christians and Buddhists only in a weak sense see themselves as engaged in a common enterprise or dialogue. Indeed, the first


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“Parliament of Religions” did not take place until 1893 in Chicago, and has been much less an on-going enterprise than the transnational “republic of science” that emerged during the 1700s and achieved a relatively stable professional identity during the following century [Harrison, 2008]. As a result, philosophers have been forced to define religion more in terms of a selective set of possible key characteristics or what Wittgenstein called family resemblances than anything like a method. Taking such an approach, Ninian Smart [1998] proposed a set of seven possible features for religion: ritual practices, experience or emotion, doctrines, morality, narratives or myths, social or institutional organization, and material culture or art — all oriented toward some kind of transcendence. In any one religion the set will be present in different forms and balances. In some cases one or more features might be quite attenuated or absent. But in aggregate these dimensions serve to differentiate religious from non-religious phenomena and thus to mark out in rough form that complex which is the subject of critical reflection in the philosophy of religion. In like manner there is considerable difference between, at least, technics and technology understood as the making activities in premodern and modern form, respectively. Insofar as this is the case, it argues for a key features approach to definition, one that has in fact been adopted by a number of philosophers (e.g., [Radder, 1996]). In the philosophy of language there tends to be less debate about the definition of the subject matter than with regard to either science or religion. Because there exists a science of language, linguistics, that has defined the phenomenon, philosophy can take this as a given. There is simply not as much debate about what constitutes language as there is about what constitutes science or religion. Something close to a definition commonly used in the science of linguistics is generally accepted: language is a system of communication using a finite set of arbitrary symbols (semantics) and structuring rules (syntax) for their manipulation. Nevertheless, within linguistics there are debates about such questions as the relation between language and speech, semantics and syntax, meaning and reference, and more, all of which have become issues for philosophical scrutiny. (It is important to note, as well, that the philosophy of language as a distinct regionalization of philosophy is much more prominent in analytic than in phenomenological traditions, where it tends to be subsumed within philosophical anthropology or hermeneutics and semiotics.) Considering analogies between the philosophy of technology and the philosophy of language, is it possible to conceive of engineering or the engineering sciences as like linguistics and thus containing sufficiently well established definitions of subject matter that these could be taken as given? To some extent higher educational institutions of technology would seem to be based on assumptions about technology as constituted by sciences of application — application that differentiates into multiple levels and fields of engineering. Since whatever might be given in linguistics is taken in for scrutiny by the philosophy of language, on this analogy the philosophy of technology could be described as critical reflection on the manifold of engineering. That is, the philosophy of technology would become the

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philosophy of engineering and the engineering sciences. Another possibility would be to take technology in a more etymologically literal sense as a science of technics or technique and divide the philosophy of technology in two: the philosophy of the science of technique and the philosophy of technique. Yet the extent to which technology is sufficiently captured by either engineering or technics remains an open question. A related issue concerns whether any of these three approaches — defining technology in terms of method, a flexible set of key features, as engineering, or as technique — would fit sufficiently with common linguistic use, coordinate with questions of rhetorical influence, and work well for integrating philosophies of technology in engineering, in humanities, and in social science contexts into something that might be thought of as the philosophy of technology in general. This final implication is important. As mentioned at the beginning of the chapter, definition is closely related to a number of basic philosophical questions of more general interest. But definition, when considered at the shallow level of sorting out the phenomenon or phenomena to be named and classified, does not of itself determine in a deeper or interpretative sense. This is at least part of what Martin Heidegger [1954] meant when asserting that the essence of technology is not anything technological. In pursuing any deeper, interpretative definition — whether in philosophies of technology arising within the contexts of science and engineering, the humanities, or the social sciences — it would be appropriate to consider at least the following ten basic but non-ordered and not mutually exclusive questions: 1. Does technology have an inner or essential distinguishing feature? 2. If technology does have an essential or necessary feature or features, how might such be distinguished from accidental or contingent features? 3. What is the relation between technology and nature? 4. What is the relation between technology and human action? (Insofar as technology can be defined as a type of human action, then a host of issues in the philosophy of action, ethics, and political theory become relevant.) 5. Is technology one or many, a unity or plurality? That is, is it more accurate to speak of “technologies” than of “technology”? If a plurality, what are the best ways to understand it as such? 6. What, if any, are the “parts” of (or divisions within) technology (technologies)? 7. Is there historical continuity in the development of technology (or technologies)? 8. What is the relation between technology (technologies) and science (sciences)?


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9. What is the relation between technology (technologies) and engineering (engineerings)? 10. What is the relation between technology (or technologies) and other aspects of human life (culture, language, religion, art, society, politics, economics, etc.)? Possible responses to these questions will be strongly influenced by how technology is sorted out from other aspects of the world. Responses to these questions in turn will have substantive implications for other questions, such as whether or not technology is neutral, autonomous, good, beautiful, and more. Adopting the pragmatist approach to definition implies that any definition will need to be undertaken in dialogue with or through critical reflection on its implications. Definition is not something that can take place independent of context ACKNOWLEDGEMENTS This chapter has drawn on previous and forthcoming work by each co-author. Some sections from Mitcham’s Thinking through Technology (1994) have been revised or extended. Some research from Schatzberg’s work in progress on the history of the term “technology,” supported in part by National Science Foundation grant #0646788, has also been utilized. Interested readers should consult both for complementary expositions of the argument presented here. BIBLIOGRAPHY All references to classical or standard works are handled with standard page references or by means of textual divisions that are independent of particular editions; as a result such works are not listed here and references in the text are in parentheses rather than brackets. All references in brackets are to works listed here. All quotations not originally in English are in our own or highly adapted translations. [Abelson, 1967] R. Abelson. Definition. In Encyclopedia of Philosophy, Paul Edwards, ed., vol. 2, pp. 314-324. New York: Macmillan, 1967. [Armstrong, 1998] T. Armstrong. Modernism, Technology and the Body: A Cultural Study. Cambridge, UK: Cambridge University Press, 1998. [Austin, 1961] J. L. Austin. “A Plea for Excuses.” In Philosophical Papers, J.O. Urmson and G.J. Warnock, eds., pp. 123-152. Oxford: Clarendon Press, 1961. [Beakley et al., 1982] G. C. Beakley, H. W. Leach, J. K. Hedrick, and R. E. Lovell. Engineering: An Introduction to a Creative Profession. New York: Macmillan, 1982. [Beard, 1927] C. Beard. “Time, Technology, and the Creative Spirit in Political Science.” American Political Science Review, vol. 21 (February), pp. 1-11, 1927. [Beaune, 1980] J.-C. Beaune. La technologie introuvable: Recherche sur la d´ efinition et l’unit´ e ecles. Paris: J. Vrin, de la technologie a ` partir de quelques mod` eles du XVIII e et XIX e si` 1980. [Beckmann, 1777] J. Beckmann. Anleitung zur Technologie, oder zur Kentniss der Handwerke, Fabriken und Manufakturen. G¨ ottingen: Vandenhoeckschen, 1777. [Benjamin, 1968] W. Benjamin. “Das Kunstwerk im Zeitalter seiner technischen Reproduzierbarkeit,” in Illuminationen Ausgeweahlte Schriften, pp. 148-184, Frankfurt a.M.: Suhrkamp, 1961. First English version in Illuminations, trans. Harry Zohn, New York: Harcourt, Brace, and World, 1968.

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[Snow, 1959] C. P. Snow. The Two Cultures and the Scientific Revolution. Cambridge: Cambridge University Press, 1959. [Sombart, 1902] W. Sombart. Der Moderne Kapitalismus. 2 vols. Leipzig: Dunker und Humbolt, 1902. Revised and expanded in 1916, 1917, and 1927. [Stratton and Mannix, 2005] J. A. Stratton and L. H. Mannix. Mind and Hand: The Birth of MIT. Cambridge, MA: MIT Press, 2005. [Sussman, 1968] H. Sussman. Victorians and the Machine: The Literary Response to Technology. Cambridge, MA: Harvard University Press, 1968. [Sypher, 1968] W. Sypher. Literature and Technology: The Alien Vision. New York: Random House, 1968. [Tantoush, 2001] T. M. K. Tantoush. “Technology and Organization.” In International Encyclopedia of the Social and Behavioral Sciences, Neil J. Smelser and Paul B. Baltes, eds., vol. 23, pp. 15502-15506. Amsterdam: Elsevier, 2001. [Thurschwell, 2001] P. Thurschwell. Literature, Technology and Magical Thinking, 1880—1920. Cambridge, UK: Cambridge University Press, 2001. [Tichi, 1987] C. Tichi. Shifting Gears: Technology, Literature, Culture in Modernist America. Chapel Hill, NC: University of North Carolina Press, 1987. [Veblen, 1906] T. Veblen. “The Place of Science in Modern Civilization.” American Journal of Sociology, vol. 11 (March), pp. 585-609, 1906. [Veblen, 1908] T. Veblen. “On the Nature of Capital.” Quarterly Journal of Economics, vol. 22 (August), pp. 517-542, 1908. [Weber, 1922] M. Weber. Wirtschaft und Gesellschaft: Grundriss der verstehenden Sozialogie. T¨ ubingen: J.C.B. Mohr, 1922. [West, 1967] T. R. West. Flesh of Steel: Literature and the Machine in American Culture. Nashville, TN: Vanderbilt University Press, 1967. [Williams, 1983] R. Williams. Keywords: A Vocabulary of Culture and Society. Revised edition. New York: Oxford University Press, 1983. [Winner, 1977] L. Winner. Autonomous Technology: Technics-Out-of-Control as a Theme in Political Thought. Cambridge, MA: MIT Press, 1977. [Wright, 2002] P. H. Wright. Introduction to Engineering. Third edition. New York: John Wiley, 2002.


SCIENCE, TECHNOLOGY AND THE SCIENCE–TECHNOLOGY RELATIONSHIP Hans Radder

1

INTRODUCTION

This chapter discusses the principal philosophical questions concerning the relationship between science and technology. As for science, the discussion is meant to cover a variety of disciplines, even if the examples show some emphasis on the natural sciences.1 As for technology, in the present chapter this notion will be used in a broad sense. That is to say, technology is taken to embrace the technological sciences, while the technological sciences include several disciplines in addition to the engineering sciences, such as information science, medical science, and agricultural science. Making such a direct link between technology, more broadly, and the technological sciences makes sense in view of the fact that these sciences aim to contribute towards realizing contemporary or future technologies. Accordingly, the chapter includes discussions and illustrations of a broad range of technological activities, such as research, design, production, use and maintenance. This also fits the comprehensive approach to technology and the engineering sciences that is taken in this Handbook. The prime subject of this chapter is the relationship between science and technology. That science and technology have been, still are, and can be expected to remain, ‘related’ hardly needs to be argued. Rather, the important questions concern, first, the empirical features of this relationship (including its historical development) and, second, its theoretical conceptualization in relation to our philosophical understanding of both science and technology. As will be demonstrated in this and the other chapters in this part of the Handbook, these two questions may receive quite different answers. The layout of the chapter is as follows. Section 2 discusses some important methodological issues that naturally present themselves to a reflexive philosophical approach. Since any account of the science-technology relationship presupposes some characterization of both science and technology, the question is how to acquire a plausible characterization. As to the relationship between science and technology, we face the related methodological question of how to study this relationship. The sections that follow then review several important views of science, 1 For a review of the role of the social sciences in technology and engineering, see Sørensen’s chapter in this Volume, Part I.


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technology and their relationship: the idea of technology as applied science (Section 3); the conception of the technological finalization of science (Section 4); the claim that experimentation constitutes the central link between science and technology (Section 5); and the account of science as technology (Section 6). Section 7 sums up the main conclusions about the science-technology relationship and, in particular, about the uses of science in technology. Overall, I follow the common philosophical practice of presenting both an exposition and a critical assessment of the views discussed. Where appropriate, references to other chapters of this Handbook, both in this and in the other parts, are provided.

2

PRELIMINARY METHODOLOGICAL ISSUES

A reflexive philosophical study of the relationship between science and technology needs to confront some preliminary methodological issues. Since making claims about the nature of this relationship presupposes some characterization of science and technology themselves, there is the question of how to acquire a plausible specification of these notions. Next, there is the closely related question of how to investigate the science-technology relationship itself and how to obtain a fitting account of it. The question of how to characterize science and technology is often addressed through a specification of their respective aims. Many authors claim that the aim of science is epistemic, and in particular, the acquisition of knowledge. The aim of technology, in contrast, is said to be the construction of things or processes with some socially useful function. Many other authors, however, claim that such a conceptual-theoretical specification of science and technology does not do justice to the richness and variety of actual scientific and technological practices. By way of alternative they advocate a nominalistic-empirical approach: go and see, and define science (respectively technology) as the practical activity that is called science (respectively technology). These two points of departure — either a conceptual-theoretical definition or a nominalistic-empirical account of science and technology — differ greatly. Both lead to several further questions. Consider first the view of science as the search for knowledge. Since there is also nonscientific knowledge, some authors add that science is the activity that systematically strives for theoretical and explanatory knowledge. However, a strict application of this definition would exclude many activities that are usually, and rightly, seen as part of science. Quite a few scientists aim at observational or experimental knowledge and scientific knowledge can also be non-explanatory, for instance in the case of taxonomical knowledge. A possible solution might be to distinguish between primary and subsidiary aims. Accordingly, the search for theoretical, explanatory knowledge would be the primary aim of science, while other types of knowledge are always subsidiary to this aim. This solution is rather questionable, however. It is, for instance, difficult to reconcile with the many

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studies that have convincingly shown that experimental practice has an extensive and worthwhile life of its own.2 Furthermore, defining science as the search for theoretical, explanatory knowledge presupposes a specific philosophical interpretation of science, which will not be generally acceptable. Thus, Bas van Fraassen [1980] sees explanation as merely a pragmatic aspect of science and he puts forward the empirical adequacy of theories, instead of their truth, as the aim of science. Patrick Heelan [1983] also emphasizes the primacy of perception, although his notion of perception differs significantly from van Fraassen’s account. Clearly, for these philosophers and their followers a plausible characterization of science, and a fortiori of the contrast between science and technology, cannot be based on the explanatory nature of theoretical science. What about the definition of the aim of technology as the construction of things or processes having some socially useful function? Although this definition seems to be intuitively plausible, two qualifications are in order. First, many authors claim that it is too narrow because technology is not limited to the making of useful material things or processes. Technology, as the etymology of this term suggests, also involves the generation and utilization of knowledge ([Layton, 1974]; see also the chapters on artifact epistemology in Part II of this Handbook). More specifically, it is design knowledge that is claimed to have a prominent place in technology. Moreover, in the engineering or technological sciences, this design knowledge is often of a quite general nature [Kroes, this volume, Part III]. Second, this definition of technology (with or without the addition of design knowledge) is not of much help in clarifying the science-technology relationship. After all, designing and constructing material things or processes, including the generation and utilization of design knowledge, is common business in the practices of observational and experimental science.3 Both the overall observational or experimental setup and their component devices, apparatus or instruments often require an extensive process of design and construction (see, e.g., [Rothbart, 2007]). Such observational and experimental practices constitute a major part of scientific disciplines. Hence, in contrast to what Layton [1974], Kroes [1992] and many others claim, design (knowledge) and construction do not demarcate technology and engineering from science. What to conclude from this discussion of the conceptual-theoretical approach? The only tenable intuitive distinction seems to be the relation to social usefulness. In contrast to science, technology would be oriented towards potential usefulness for society at large. Even this suggestion needs to be qualified, however. First, should this social usefulness be explicit and immediately visible, right at the start of a technological project? In this case, some of the research carried out in industrial laboratories may not qualify as technological. For instance, the research done 2 See, e.g., [Hacking, 1983; Gooding, 1990; Galison, 1997; Lange, 1999; Radder, 2003; Baird, 2004]. 3 Even computational science has a material side and hence it involves some design of material things or processes. See the analysis of the simulation laboratory in [Petersen, 2006, Chap. 2].

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between 1947 and 1972 at the Philips electronic laboratories did not always aim at immediate technological applications [De Vries, 2005]. But if social usefulness is permitted to emerge in the course of a project, then quite a few projects in prima facie scientific research will also count as technological. After all, basic research is often supported by funding agencies because of its contribution to the ‘knowledge base’ of a society, and hence this research can be seen as practically useful in the long run (cf. [Tiles and Oberdiek, 1995, Chap. 4]). For this purpose, present-day applications for basic research projects have to be routinely justified also in terms of their possible technological and societal payoff. Let us now have a closer look at the nominalistic-empirical strategy. This involves the empirical investigation of whichever activities that present themselves as scientific or technological. As will be clear from the preceding comments on the conceptual-theoretical approach, this nominalistic-empirical strategy certainly has its place. In particular, it constitutes a healthy antidote against those philosophers who simply proclaim a specific aim for science or technology, without any further evidence or reflection. Yet, although this strategy may initially seem straightforward, on closer inspection it appears to have its problems as well. First, any empirical identification of either science or technology requires some pre-understanding. Suppose we visit a site called ‘Institute for Biomedical Science’. We may, then, safely conclude that this is a site of scientific activity. But many different activities take place in this institute: the toilets are cleaned, the board of directors holds meetings, the catering service provides lunches and the PhDs write articles. When we focus on the writing of articles in studying science, we apparently apply a certain pre-understanding of what counts as (the core activities of) science. Thus, [Latour and Woolgar, 1979] characterize laboratory science through its production of ‘inscriptions’ (and not, to mention another option, through its catering procedures). More precisely, they focus on a specific subset of the inscriptions produced in the laboratory and disregard other inscriptions, such as the receipts generated by the PhDs through having their lunch in the lab canteen. Hence, the nominalistic-empirical approach presupposes some conceptual-theoretical interpretation of what constitutes science and technology, and the question of whether we can make this pre-understanding more explicit, or even define it, is still with us. A second problem of the nominalistic-empirical approach is that different languages and cultures use different names for activities that might be quite similar. Anglo-Saxons distinguish sciences and humanities, which in Germany are both called Wissenschaft. In earlier centuries, natural philosophy denoted what is now called physical science. And nowadays we speak of computer science and information technology as being roughly equivalent. In order to see whether or not such types of activities might be essentially, basically, or to a large extent similar, we again need a conceptual-theoretical clarification of those activities. My conclusion from this preliminary discussion is that we need both the theoretical and the empirical approach. We have to start from some interpretive perspective on what we take to be basic aspects of science and technology. Next,


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we articulate and test this interpretation on the basis of the empirical study of the activities thus defined. And we try to determine its scope by examining its possible applicability to natural philosophy, humanities, information technology, and the like. Once we have established a plausible interpretation of science and technology, this interpretation will acquire some normative force. Activities that do not conform to the established characterization of science or technology should not be named scientific or technological. We stick to a particular interpretation as long as it enables us to cover (what we take to be) the interesting and important cases and dimensions of both science and technology. Thus, the theoretical and the empirical approach should not be separated. On the one hand, a plausible conceptual model should be backed up by empirical studies of the practice of science and technology. On the other, an empirical investigation presupposes an interpretive pre-understanding of both science and technology, and an appropriate empirical model of the science-technology relationship needs to be based on a plausible interpretive pre-understanding. In this chapter, the emphasis is on conceptual-theoretical accounts of the relationship between science and technology, but I will also pay attention to the empirical support of those accounts and refer to empirical studies of this relationship. David Channell’s contribution [this volume, Part I] provides more detailed discussions of several important aspects of the empirical relationship between science and technology. 3

TECHNOLOGY AS APPLIED SCIENCE

A still current view of the relationship between science and technology is phrased by means of the formula ‘technology is applied science’. A classic account of this view has been presented by Mario Bunge. He makes the following distinction between technology as applied science and pure science. The method and the theories of science can be applied either to increasing our knowledge of the external and the internal reality or to enhancing our welfare and power. If the goal is purely cognitive, pure science is obtained; if primarily practical, applied science. Thus, whereas cytology is a branch of pure science, cancer research is one of applied research. [Bunge, 1966, p. 329] Thus, it is the distinct aims which differentiate (pure) science from technology. In Bunge’s view, these aims pertain to the outlook and motivation of the scientific and technological researchers. Bunge develops this view as follows. Scientists strive for empirically testable and true theoretical laws, which accurately describe (external or internal) reality and which enable us to predict the course of events. The technologist, in contrast, uses scientific laws as the foundation of rules which prescribe effective interventions in, and control of, this reality for the purpose of solving practical problems and realizing social objectives. Taken together, science and technology (the latter in the sense of applied science) should be distinguished from those practical techniques and actions that are not based on scientific theories

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or methods. Thus, in this view, Roman engineering and medieval agriculture are practical arts and crafts rather than technologies. Since experimentation is a basic method for testing scientific theories, Bunge distinguishes experimental action from both technological and purely practical action. Bunge [1966, p. 330] claims that the different aims of science and technology are inferred from alleged differences in outlook and motivation of their practitioners. If this were meant in a literal sense, he should have provided us with the results of empirical investigations, such as surveys, interviews, or other evidence about the attitudes or self-images of scientists and technologists. Apparently, this is not Bunge’s intention. Instead, his discussion suggests that he thinks that these outlooks and motivations can in some way be ‘derived’ or ‘reconstructed’ on the basis of the activities of scientists and technologists. Hence, the discussion in this section focuses on these (alleged) differences in scientific and technological activities. A further characteristic of this account of the science-technology relationship is its hierarchical nature. In particular, Bunge postulates an epistemological hierarchy between science and technology. If true, scientific laws can provide a justification of technological rules. The converse is not possible, however: a working technological rule, which is merely practically effective, can never justify a scientific law. By way of example, he discusses the technology of making an optical instrument, such as the telescope. In designing and constructing such a device we do not exclusively employ wave optics, the most truthful theory of light in this context, but make ample use of the false theory of geometrical optics, which conceives of light as propagating along straight lines. Moreover, usually such construction work requires specific craft skills (such as the grinding of the lenses or mirrors) which do not employ scientific theories but are based on effective practical knowhow and procedures. Bunge concludes that a practically working artifact, such as the telescope, cannot justify the scientific laws employed in its construction. In addition to the epistemological primacy of science over technology, Bunge’s view entails a temporal ordering. If technology is the result of applying science, it follows that temporally prior scientific research constitutes the driving force of technological development and innovation. This idea of ‘science finds — industry applies’ is often called the linear model of the science-technology relationship. More or less similar hierarchical views of the science-technology relationship can also be found outside of philosophy, for instance among scientists, policy-makers, and the public at large. Sometimes such views include an even stronger hierarchical evaluation in that science is seen as an exciting, creative quest for truth, while technology would merely involve the routine application and exploitation of the fruits of this quest. In the remainder of this section, I discuss and evaluate this view of technology as applied science.4 First, several scholars have criticized Bunge’s approach on 4 In doing so, the focus will be on the ‘substantive’ theories of scientists and engineers, that is, theories about the technological objects themselves, thus leaving aside the ‘operative’ theories of social scientists and technologists, that is, the social theories about technological action and


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historical grounds. They claim that historical studies show that many important technological inventions and innovations came about independently from scientific research and scientific theorizing. Well-known illustrations include steam engines, water power devices, mechanical clocks, metallurgical techniques and the like (e.g., [Laudan, 1984]; see also Channell’s chapter in this Volume, Part I). Although these criticisms seem basically correct, they do depend on the precise interpretation of Bunge’s version of the linear model of the science-technology relationship.5 A flexible interpretation of Bunge’s model would permit the following replies. First, many of the historical counterexamples date back long ago, often to the eighteenth century and before. Hence, they need not be taken as a refutation of the account of technology as applied science, but might be seen as limiting the scope of this account. Put differently, Bunge’s account might be construed as a definition of technology and as such it would be immune to empirical counterexamples. If a certain case does not fit the account of technology as applied science, then it is, by definition, not a technology. The remaining issue, then, pertains to the usefulness and relevance of Bunge’s definition. In view of the great significance of modern, science-based technology, the usefulness and relevance of his definition seems obvious enough. Second, one might note that, in Bunge’s view, technology may also result from applying the method of science (see the above quotation) and that one could make a case for the claim that (some of) the counterexamples did apply scientific methods, even if they were not based on available scientific theories. However this may be, I will not pursue this debate any further here but instead develop a different assessment of Bunge’s technology-is-applied-science account. For this purpose, it is important to realize that this account implies two distinct claims. The first is that there is a clear ‘kinship’ between science and technology, in the sense that technology is based on scientific theories and methods. The historical criticisms are aimed at this claim. They seem to accept Bunge’s characterization of science as a quest for true knowledge of laws and theories (e.g., [Layton, 1974]), but they object that technology has often developed independently from these laws and theories. That is to say, they claim that the differences between science and technology are larger than Bunge assumes. Secondly, however, Bunge advocates the claim that science and technology also display essential differences, in the sense that scientists aim at truth and technologists at practical effectiveness and usefulness. I will assess this second claim by analyzing, like Bunge, sciencebased technology and by showing that its contrast to science is much smaller than Bunge assumes. Consider the claim that scientists aim for truth by constructing testable, fundamental theories and by accepting or rejecting these theories according to their match to the empirical data. This account suggests that separate, fundamental theories can be confronted more or less directly with the empirical data. In fact, organization (for the latter, see Sørensen’s chapter in this Volume, Part I]). 5 For extensive, critical discussions of the linear model, see the contributions to [Grandin et al., 2004].

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however, scientific practice is much more complex. Fundamental theories as such, for instance quantum mechanics or the theory of evolution, do not tell us much about empirical reality. To become empirically applicable they first have to be developed and specified with a view to particular domains of empirical phenomena. The point can be illustrated by the case of nonrelativistic quantum mechanics. The basic structure of this theory was developed between 1925 and 1927. Since those days, this theory has been, and is being, ‘tested’ in many different domains, including atomic and nuclear physics, quantum chemistry, solid state physics, and so on. Within each of these domains we find a diversity of subfields, such as the study of electrical conductivity in crystals within solid state physics. Moreover, there are overlapping research areas, such as laser physics which combines insights from both atomic and molecular physics and from quantum electrodynamics. Hence, we are confronted not with two types of activities (theoretical and experimental) but three: the construction of fundamental theories; their development and specification to enable actual empirical tests; and the design and performance of experiments to test the theories. The second type of activity requires the articulation of the fundamental theories, usually through extensive calculation and substantial model building.6 Two aspects of these processes of development and articulation are particularly relevant to the present comparison between science and technology. First, even within one subfield one often finds a large variety of different models and methods of calculation, each of them specific for and appropriate to particular types of experiment. Nancy Cartwright [1983, pp. 78-81] discusses the example of laser physics and documents the use of at least six different models of the natural broadening of spectral lines. She emphasizes that the scope of each of these models is often quite small, namely limited to a few types of experimental phenomena. Moreover, scientists do not see these different models as competing but rather as complementing each other since each serves a specific purpose. Second, a major function of model building is to bridge the large distance between the relatively schematic and simple fundamental theories and the mostly complex experimental world [Morgan and Morrison, 1999]. Because of this distance, bridging cannot succeed on the basis of the fundamental theories alone. Hence, what we see in practice is the use of a diversity of methods and approaches that cannot be rigorously justified from a theoretical perspective. Frequent use is made of convenient rules of thumb, intuitively attractive models, mathematically feasible approximations, and computationally tractable computer simulations. Often the test also depends on other experiments, for instance when the value of parameters that cannot be calculated theoretically, is determined through tuning them to the results of other experiments. Thus, the variety of experimental domains and the large distance between fundamental theories and experimental phenomena require the indispensable use of 6 See [B¨ ohme et al., 1983; Cartwright, 1983; 1999]. For the sake of argument I have, with Bunge, assumed the availability of a fundamental theory. In actual practice, calculation and model building may just as well precede the construction of such a theory.


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‘workable methods’ in testing the theories. Scientific practice includes the regular application of a variety of convenient rules of thumb and intuitive models for solving different problems, the making of approximations based on mathematical or computational feasibility and the blackboxing of (part of) a system through tuning to experimentally determined parameters. The crucial point is that these are exactly the kinds of procedures that are typical of technology, also according to Bunge. Thus, on the basis of an analysis of their testing activities, there is no reason to assume a fundamental contrast in outlook and motivation between scientists and technologists.7 A test of quantum mechanics by a laser physicist is not essentially different from the test of a design of a specific acoustic amplifier by an engineer [Cartwright, 1983, pp. 107-112]. Thus far, I have focused on Bunge’s account of the relationship between science and technology as applied science. Apart from this, there is his claim that both science and technology should be clearly distinguished from skillful, practical action. This claim suggests that practical craft skills play no (or no significant) role in science and in science-based technology. However, if we — in contrast to Bunge — take full account of the practice of scientific and technological observation and experimentation, it is immediately clear that this suggestion makes no sense. After all, as every observer or experimentalist knows, skillful action is an essential aspect of observational and experimental science and technology (just think of the grinding of the lenses in the case of constructing a telescope).8 The reason for the importance of skillful action is straightforward. In contrast to what generations of empiricists have claimed, the typical way of obtaining scientific experience is not through passive sensation but through active observation and experimentation. As we will see in more detail in Section 5, the stability and reproducibility that scientific observers and experimenters try to establish is almost never given by nature, but has to be realized through a difficult and laborious process of intervention and control. For this purpose, skillful practical action is indispensable (see, e.g., [Ravetz, 1973; Collins, 1985; Gooding, 1990; Radder, 1996]). The discussion in this section does not claim to provide an exhaustive assessment of the view of technology as applied science.9 Yet it should suffice to demonstrate that Bunge’s hierarchical approach is questionable. A reconstruction of their cognitive activities does not support the attribution of essentially different aims to scientists and technologists. Consequently, this way of demarcating science from technology proves to be difficult, if not impossible, and the same applies to sub7 Another relevant argument, which I will not pursue here, is that scientists do not aim at truth simpliciter but at significant truths, where the criteria of significance may be both epistemological and social (see [Kitcher, 2001]). 8 In a later publication, Bunge admits that ‘even the scientific inventor is a bit of a tinkerer (bricoleur) and — like the scientist — he possesses some tacit knowledge, or know-how, that cannot be rendered fully explicit’ [Bunge, 1985, p. 220]. In spite of this, he immediately adds that it is only explicit, science-based technology that is philosophically interesting and worth studying. 9 For further discussions and assessments, see [Tiles and Oberdiek, 1995; Cuevas, 2005; Boon, 2006; Koningsveld, 2006]. See also Channell’s chapter in this Volume, Part I.

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stantiating the epistemological subordination of technology to science. To avoid misunderstanding, I should like to emphasize that the argument of this section is not that there are no differences at all between science and technology. But it does imply that, generally speaking, these differences will be a matter of degree and that they do not add up to an unambiguous contrast between science and technology in terms of singular and essentially different aims. In the concluding section of this chapter I will come back to this issue and address the question how science and technology may be distinguished and related on the basis of their similarities and dissimilarities. 4 TECHNOLOGY AS FINALIZED SCIENCE During the 1970s a group of German scholars, also called the Starnberg group, published an impressive series of articles and books about the finalization of science (see [Sch¨ afer, 1983], and further references therein). ‘Finalized science’ denotes a particular stage of scientific development that is, more or less consciously, oriented towards external social goals and interests. The authors themselves see their finalization theory as an improvement of the theory of technology as applied science. Thus, in their account of agricultural chemistry Wolfgang Krohn and Wolf Schäfer state: Our aim here is not to introduce a distinction between agricultural chemistry as a finalized science and applied science, but rather to offer a more precise meaning for the vague notion of ‘applied science’. The term ‘applied science’ gives the misleading impression that goaloriented science simply involves the application of an existing science, rather than the creation of a new theoretical development. This in turn feeds the misconception that pure science is superior to applied science. [Krohn and Sch¨ afer, 1983, p. 46] One of the main aims of the finalization theory is to establish at which stages of scientific development finalization is possible and fruitful. For this purpose, it includes an account of scientific development that makes use of, but also substantially expands on, Thomas Kuhn’s model of scientific development. Although it is not generally realized, Kuhn advocates a strongly internalist view. For a scientist, the solution of a difficult conceptual or instrumental puzzle is a principal goal. His success in that endeavour is rewarded through recognition by other members of his professional group and by them alone. The practical merit of his solution is at best a secondary value, and the approval outside the specialist group is a negative value or none at all. [Kuhn, 1970, p. 21] The finalization theory also starts from a rather strict internal-external distinction, but then goes beyond a Kuhnian internalism by arguing that an interaction


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between external or societal goals and interests and internal or cognitive goals and interests is possible, and even to some extent necessary, at a certain stage of the development of scientific disciplines. The theory focuses on the disciplines of the natural sciences and claims that these sciences pass through three successive stages. First, there is the explorative stage, which bears some resemblance to Kuhn’s preparadigmatic stage. At this stage, a well-developed domain-structuring theory is not (yet) available, and research methods are primarily empirical and classificatory rather than theoretical and explanatory. The next, the paradigmatic stage is guided by a general theory that structures the field of phenomena and directs the way they should be investigated. As in Kuhn’s normal science, the aim is the empirical and conceptual articulation and validation of the central theoretical ideas. These second-stage developments may lead to ‘closed theories’, a notion adapted from physicist Werner Heisenberg and explained as follows: In general three things can be said of a closed theory ...: firstly, it will possess sufficient conceptual material to capture a particular field of phenomena; secondly, its validity will at least be proven for a number of instances; and thirdly, there are good reasons to expect that its validity extends to the whole category of phenomena in question. [B¨ ohme et al., 1983, p. 148] Because these are quite demanding criteria, which will not always be met in actual scientific practice, the authors introduce the weaker notion of theoretical maturity for cases where the theories are not strictly closed but still possess a substantial measure of comprehensiveness and stability. Hence the claim of the finalization theory is that, from an internal-scientific perspective, theoretically mature disciplines are more or less complete. Nevertheless, they can be developed further into a third, or postparadigmatic, stage, in which they become oriented towards external goals and interests through the development of ‘special theories’ (sometimes also called ‘theoretical models’) for the purpose of realizing certain technological applications. It is at this stage that science becomes finalized. In contrast to Kuhn, at this stage the ‘practical merit’ and the ‘approval outside the specialist group’ are primary values, and yet realizing this merit requires the development of genuinely new theoretical knowledge. The finalization theory has been developed in close interaction with case studies of important episodes in several disciplines (see Part I of [Schäfer, 1983]). In physics, the articulation of classical hydrodynamics into a variety of special theories of fluid dynamics for the development of airplanes has been studied. In chemistry, the relationships between nineteenth-century organic chemistry, the special area of agricultural chemistry and the production of artificial fertilizers has been investigated. And in biomedical science, the development of molecular biochemistry into special theories of carcinogenic processes with a view to the production of appropriate drugs has been scrutinized. The authors themselves conclude that their theory applies best to the discipline of physics. Its appropriateness for the other

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disciplines is judged to be (far) less straightforward, the major problem being the applicability of the notion of theoretical maturity. The theory of finalization was proposed more or less simultaneously with, though independently of, the strong program in the sociology of scientific knowledge.10 Although both approaches share an emphasis on the significance of external factors, there are also important differences between the finalization theory and the sociology of scientific knowledge. First of all, the former, in contrast to the latter, does not claim that scientific truth depends on external goals and interests. Furthermore, the finalization theory focuses on conscious or intentional external influences in a science policy context. Hence, the theory includes an explicit evaluative and normative component: although orientation towards external goals and interests is feasible in the explorative and, to some extent, even in the paradigmatic stage, the best and most fruitful way to exploit the technological potential of the sciences is through the finalization of mature scientific theories in their postparadigmatic stage. During the 1970s and early 1980s, the finalization theory sparked an extensive and at times acrimonious debate (see the bibliography in [Schäfer, 1983, pp. 301306]). This debate was both philosophical and political in nature,11 but it was primarily restricted to Germany.12 Thus far, in Anglo-Saxon philosophy of science the relationship between science and technology has been a neglected issue anyway (cf. [Ihde, 1991; 2004]). Within the recently rising philosophy of the technological sciences, however, the theory of finalization constitutes a worthwhile topic for studying the intersections between science, technological science and technology. In the remainder of this section, I will discuss the merits and problems of this theory. A first merit of the theory is that it provides a significant extension of Kuhn’s account of the development of science. It shows that older paradigms are not, or not necessarily, discarded after the advent of a successor, since they may be further developed through processes of finalization. Furthermore, the theory takes into account the obvious importance of external goals and interests, especially since the second half of the nineteenth century, and thus goes beyond Kuhn’s inadequate internalist approach. What is particularly insightful is the subtle way in which these internal and external factors are shown to be interwoven. Even if finalized science is not autonomous, the external goals and interests do not operate as purely extrinsic impositions. Instead, they are transformed and internalized as cognitive constraints on, or specifications of, the special technological theories that need to be developed on the basis of a mature scientific theory. For instance, in nuclear fusion research scientists try to develop a special theory of plasma physics that will ultimately enable the construction of a stable and reproducible nuclear fusion 10 For

a detailed exposition of this program, see [Barnes et al., 1996]. the proponents of the finalization theory were accused of promoting socialist state regulation and criticized for advocating the societal steering of science at the expense of its academic freedom. 12 The philosophical claims of the finalization theory have also been widely discussed in The Netherlands. See, e.g., [Nauta and De Vries, 1979; Zandvoort, 1986]. 11 Politically,


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reactor (see [B¨ ohme et al., 1983, pp. 154-156]). Technically, this means that only such processes are considered for which the product τ of the containment time and the temperature of the plasma exceeds a certain minimum value τ0 . Thus, the external technological goal of providing nuclear fusion energy in a controlled, safe and economically efficient way has been transformed and internalized as a specific guideline for scientific theorizing. It tells the researchers to focus their theoretical work only on such constellations of plasma and container for which τ > τ0 . Furthermore, the finalization theory convincingly demonstrates that technological science develops genuinely original knowledge, a point that is also emphasized in many recent contributions to the philosophy of the technological sciences (see, e.g., [Boon, 2006]). Technological knowledge is not, as seems to be implied in Bunge’s view of technology as applied science, a mere application of existing scientific knowledge. Another important aspect of the finalization theory is the attempt to provide a differentiated account of the relationship between external-societal and internalcognitive factors in the development of the sciences. Whether fully successful or not, the theory at least attempts to make explicit the specific conditions under which external steering of science is possible and fruitful. In this respect, it favorably contrasts to some more recent approaches, in particular to the now fashionable idea of a linear historical succession of a ‘Mode 1’ science, which is largely autonomous and disciplinary, followed by a ‘Mode 2’ science, which is primarily focused on, and guided by, technological, economic and socio-political contexts of use.13 Finally, at least some of the proponents of the finalization theory foster a commitment to a science ‘in the public interest’. Finalized science, they claim, should not evolve in a power-driven, Darwinist way, but be guided by procedures of explicit and democratic deliberation about the rational acceptability of the means and ends of proposed technological developments. Again in contrast to the Mode 1/Mode 2 approach mentioned above, this acknowledgment of normative issues is important, even for those who do not share the specific position of the advocates of the finalization theory. Moreover, given the problematic consequences of the rapidly increasing commercialization of science over the past twenty-five years, the notion of a science in the public interest is still as timely as ever (see, e.g., [Krimsky, 2003]). Next to these merits, however, the finalization theory has several problematic characteristics and implications. As we have seen, the authors themselves already confronted the problem of the definition of a closed theory and especially its application to the history of science. They concluded that the applicability of the theory to disciplines other than physics is unclear. Thus, in the case of nineteenth-century agricultural chemistry, there was no closed theory available and the authors of the case study fall back on watered-down notions such as ‘relative theoretical maturity’ and ‘methodological maturity’ [Krohn and Schäfer, 1983]. But even cases 13 See

[Gibbons et al., 1994]; for a critical review, see [Weingart, 1997].

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from physics are not straightforward. An interesting case would be to investigate the recent ‘finalization’ of climate science in the face of the human-induced greenhouse effect. It is by no means obvious that this research is building on a closed, or mature, theory of the dynamics of the entire climate system (see [Petersen, 2006, Chaps. 5 and 6]). The finalization theory rightly claims that technological science develops genuinely new knowledge. But whether its characterization of this knowledge exhausts the knowledge generated in the technological sciences is another matter. According to the finalization theory, technological knowledge is developed on the basis of closed or mature scientific theories. In general, however, such knowledge will only be a part of the knowledge required for the design, production, use or maintenance of technological artifacts or systems (see also Houkes’ chapter in this Volume, Part II). For instance, a fluid dynamics model of the boundary layer and the concepts of lift and circulation — as discussed in [B¨ ohme, 1983] — does not yet permit the design and manufacture of a real airplane, let alone the realization of the entire technological system of air transportation.14 This obviously limits the value of the finalization theory for a philosophy of technology and the technological sciences. indent Related to this is a theory-dominant view of (natural) science. Although the significance of experimentation is acknowledged in principle, the finalization theorists’ view of the technological sciences is still thoroughly theory-biased. It is theory formation which is seen as the core of scientific development and as the royal road to the fruitful exploitation of science for practical purposes. In the meantime, however, many authors in the philosophy of scientific experimentation (see note 2) have demonstrated that experimentation has a life of its own and is not limited to the testing of pre-existing theories. For this reason, it is also incorrect to identify the notion of a paradigm with that of a theory (see also [Rouse, 1987, Chap. 2]). Moreover, seeing observational and experimental science as merely preparadigmatic overestimates the role of explanatory scientific theories, especially in the technological sciences. Finally, the finalization theory exhibits certain questionable modernist characteristics. It entails a belief in the possibility of a universally valid model of scientific development. As such, it cannot do justice to the diversity and richness of the actual development of the (technological) sciences. Moreover, the theory strongly suggests an overoptimistic belief in social progress through the employment of science. As such, it does not show great awareness of the fact that (technological) science may itself be a source of social problems. One does not need to be a radical postmodernist to see the problematic character of these two beliefs.

14 For

7].

more on the systemic character of technology, see [Hughes, 1987; Radder, 1996, Chap.


5

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EXPERIMENT AND THE SCIENCE-TECHNOLOGY RELATIONSHIP

As we have seen, the finalization approach represents a form of theory-dominant philosophy of science. In fact, however, a focus on experimentation provides a quite natural starting-point for studying the science-technology relationship. To mention just one example: the method of systematic parameter variation pioneered in the eighteenth century by John Smeaton to scrutinize and test the working and efficiency of waterwheels [Channell, this volume, Part I] plays an important part in both experimental science and in engineering and technological research. Hence, in this section I will review some philosophical accounts of experimentation as a crucial link between science and technology. In his early philosophy, J¨ urgen Habermas has discussed the relation between technology and the natural sciences in some detail (see [Habermas, 1971; 1978]). He conceives of these sciences as intrinsically related to technology. Like logical positivism, Habermas sees observation as the basis of science, but he emphasizes that what counts in science is never the single, isolated observation but only the observation that can be reproduced by other scientists. Thus, his actual focus is on reproducible observations and, more generally, on predictive empirical laws. Such laws, Habermas claims, cannot be interpreted as reflecting a human-independent reality, since their universal validity depends on the possibility of active intervention and control of the empirical situation by human beings. Put differently, the epistemic warrant for the empirical law ‘whenever x, then y’ is provided by the practical result that ‘whenever we do x (under controlled conditions c), then we can bring about y’. This intervention and control is enabled through human, instrumental action. In this way, a ‘technical interest in prediction and control’ guides the production of natural scientific knowledge. The very constitution of experience on the basis of instrumental action orients science towards the technological application of the knowledge acquired. Prediction and control through intervention are the essential characteristics of the empirical laws of science and as such these characteristics foreshadow its technological application. In science, instrumental action takes the form of experimental action. Hence, experiment constitutes the basic link between science and technology. Following Charles Peirce, Habermas explains the notion of a scientific experiment as follows: In an experiment we bring about, by means of a controlled succession of events, a relation between at least two empirical variables. This relation satisfies two conditions. It can be expressed grammatically in the form of a conditional prediction that can be deduced from a general lawlike hypothesis with the aid of initial conditions; at the same time it can be exhibited factually in the form of an instrumental action that manipulates the initial conditions such that the success of the operation can be controlled by means of the occurrence of the effect. [Habermas, 1978, p. 126] This quotation clearly expresses the intrinsic relation between predictive scientific knowledge and controlled technological action and production that is char-

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acteristic of Habermas’s early philosophy. In his further development, however, Habermas changed his views on this subject, in particular by incorporating the theory-ladenness of observation and more in general by acknowledging the relative autonomy of theoretical argumentation in science. Thus, the focus of his philosophy shifted to the subjects of argumentation and communication. As a consequence, he did not develop his rather schematic view of experimentation as a significant link between science and technology. Hence, it is worthwhile to take a closer look at this subject on the basis of a more detailed account of scientific experimentation.15 The purpose of this discussion is to employ this account to illuminate important aspects of the relationship between science and technology. A characteristic feature of experimental science is that access to its objects of study is mediated through apparatus (in the form of instruments and/or other equipment or devices).16 In an experiment, we (try to) bring about a correlation between an object of study and some apparatus, and to draw conclusions about that object on the basis of a ‘reading’ of some features of the apparatus. As Habermas correctly argues, scientific experiments are meaningful only to the extent that our intervention and control produces a correlation between object and apparatus which is stable and reproducible. An important necessary condition of experimental stability and reproducibility is the appropriate control of the actual and possible interactions between the experimental (or object-apparatus) system and its environment.17 It is useful to distinguish three types of such interactions: the required interactions, which enable the object-apparatus system to behave according to its design; the forbidden interactions, which might disturb the intended experimental processes; and the allowed interactions, which are neutral with respect to the planned course of the experimental system and thus neither enabling nor disturbing. To realize a stable and reproducible experimental system, the required interactions need to be produced and maintained, the forbidden interactions need to be eliminated or prevented from taking place, while the allowed interactions do no harm and hence do not need to be controlled. For instance, if a particular experimental design requires a low temperature of, say, 100K, then we need to produce a starting temperature of 100K and we need to control the heat flow between experimental system and environment in such a way that the system stays at this temperature during the entire course of the experiment. Furthermore, if an impact of electromagnetic waves could disturb the intended experimental processes, we have to prevent such waves from interfering with the object-apparatus system during all experimental runs. Finally, if the gravitational interaction between system and environment does no harm, we do not have to control for it. The presence of required and allowed interactions 15 The present sketch of this account draws on analyses in [Radder, 1988, Chapter 3; 1996, Chapter 6; 2003]. Additional detail, including a characterization of the implied notion of ‘technology’, can also be found in Radder’s chapter in this Volume, Part V. 16 For discussions and classifications of scientific apparatus, see [Harr´ e, 2003; Baird, 2003; Heidelberger, 2003]. 17 Of course, this control is not sufficient, since the object-apparatus system itself may be internally unstable and irreproducible.


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implies that successful experimentation does not necessitate a completely isolated system, that is, a system that does not at all interact with its environment. Materially realizing such a system would be very difficult and probably even impossible, given the ubiquity of gravitational and/or electromagnetic interactions. Of course, in actual scientific practice we may not always, or not yet, know which interactions are required, forbidden or allowed; or we may be wrong in our assessment of these interactions. Anyway, an important part of the aim of experimentation is to get to know which interactions are enabling, disturbing or neutral. Two features of such processes of acquiring experimental knowledge are directly relevant to the issue of stability and reproducibility. First, what is seen to be required, forbidden or neutral will depend on the theoretical interpretation of the experiment in question. Types of interaction that are claimed to be theoretically impossible (e.g., telepathic influences or signals traveling faster than light) will be irrelevant and do not need to be taken into account. The same applies to interactions that are possible (and may be present) but are claimed to be inconsequential to the plan and aim of the experiment (e.g., the ‘impact’ of daylight in measuring the temperature of a fluid) and hence classified as ‘allowed’. Yet, we should note that such claims may be contested by other experimenters or overthrown by later developments. Second, controlling the relevant interactions is, in practice, not only a matter of exercising the required material control, but it also demands a social discipline and control of all the people that have, or might have, an impact on the material realization of the experiment. After all, it is these people who play, or might play, a critical role in the processes of producing or securing the enabling conditions and eliminating or preventing the disturbing conditions. In addition to these two features, there may also be social or ethical reasons for the need to control further interactions between an experimental system and its environment. For instance, impacts of an experimental system on the environment that could endanger the safety of the experimenters or of other human beings are generally seen to be undesirable and hence they need to be prevented. Thus, the necessary control of the (desirable and undesirable) influences and disturbances between the object-apparatus system and its environment exhibits important theoretical, material and social features of scientific experimentation. Next, this analysis may be used to discuss and assess the science-technology relationship in two different ways. Just like experiments, working technologies need to be stable and reproducible, while the control of the relevant interactions between the technological system and its environment constitutes a necessary condition for achieving this goal. Again, we may distinguish between required, forbidden and allowed interactions. Thus, in a conceptual-theoretical sense, the successful realization of a technological system poses similar requirements as the successful realization of an experimental system. The system-environment interactions that enable the technological system to behave according to its design need to be produced and maintained, the interactions that might disturb the intended technological processes need to be eliminated or prevented from taking place, while

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the interactions that are inconsequential to the stable and reproducible working of the technological system may be ignored. Furthermore, in an empirical sense, materially realized experimental substances, devices or processes may be, and often are, exploited as (part of) technological systems. A particular piece of experimentally developed electrical circuitry may be used to fulfill a certain function as part of a larger technological system, for instance a computer. Or an organism that has been genetically modified in a biology laboratory may get exploited in particular agricultural technologies. As in the case of their scientific counterparts, such ‘experimental technologies’ are supposed to exhibit a certain measure of stability and reproducibility, and hence the relevant system-environment interactions need to be controlled. Materially and socially, however, experimental systems and the corresponding experimental technologies will usually be quite different for two reasons. First, technologies are typically required to remain stable and reproducible for a much longer period and in many more places. That is to say, the technology is supposed to function properly on a much larger spatiotemporal scale than its laboratory counterpart. Second, and related to the first reason, the environments in which the experimental technologies are expected to function may be quite different from the average laboratory environment. For these reasons, we cannot assume that a successfully realized experiment guarantees the success of the corresponding experimental technology.18 A nuclear fusion device that works well in the laboratory by no means provides us with a stable and reproducible fusion reactor that can be effectively exploited for controlled energy production. Similarly, a successful in vitro test of experimental AIDS vaccines does not necessarily entail a successful in vivo therapy for AIDS patients.19 Time and again, however, scientists from all kinds of disciplinary backgrounds have made such unwarranted leaps, either because of their inadequate view of the relation between science and technology or simply to flatter their funding agencies for the purpose of acquiring additional financial support. In this respect, it is interesting to look back briefly at the finalization theory. According to this theory, during the paradigmatic stage so-called ‘transfer research’ is possible. This research includes the systematic ‘scaling-up’ of laboratory experiments into industrial processes. Apparently, this scaling-up is seen as the unproblematic application of existing knowledge and as not requiring specific further research. Hence it is claimed that, in the paradigmatic stage, science policy can only promote research, but it cannot substantially guide it in novel directions [B¨ ohme et al., 1983, pp. 152-153]. As my more detailed examination of the relations between experimental and technological science has shown, however, these ‘scaling-up’ processes are by no means straightforward. They require a 18 Hence, the twofold meaning of ‘experimental technology’ as ‘resulting from experimental research’ and as ‘still being tentative’. See also the notion of ‘society as a laboratory’ in [Krohn and Weyer, 1994]. 19 See [Radder, 1996, Chaps. 6 and 7], where these issues and relevant cases, such as nuclear power production, entomological pest control and agricultural biotechnology, are examined in detail.


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substantial additional study of the processes that will, or may, occur at the larger temporal and spatial scales and of the new environments in which the technologies are expected to function. An important aim of such studies is to generate new knowledge about the stable and reproducible working of these technologies at the required scales and in the intended environments. The account of the science-technology relationship discussed in this section engenders two critical questions, both of which are crucially important regarding the social governance and normative assessment of scientific and technological projects. First, there is the factual question of whether an intended extension of a successful experiment to a stable and reproducible experimental technology can be reasonably believed to be feasible. The larger the spatial or temporal extension of the intended technological system, the more pertinent this question will be. Second, there is the normative question of whether the controlled material and social world that is needed to guarantee the stability and reproducibility of the technological system is a normatively desirable world. If one or both of these questions are answered in the negative, the only reasonable option is not to realize this particular technology. In my chapter in this Volume, Part V I will come back to these questions and discuss them more fully.

6 SCIENCE AS TECHNOLOGY The fruitfulness of seeing experimentation as a central link between science and technology might tempt us to conceptualize science and technology as substantially, basically, or even essentially, similar. And, indeed, philosophical accounts of the science-technology relationship repeatedly advocate such a conception of ‘science as technology’. Illustrations can be found in the work of Martin Heidegger, (the early) J¨ urgen Habermas, Peter Janich and Srd-an Lelas. More recently, comparable views in terms of the notion of technoscience have been developed by Donna Haraway, Bruno Latour, Don Ihde and Karl Rogers, among others. This notion of technoscience is claimed to capture the crucial similarities between science and technology. First, it posits the primacy of practice: both scientists and engineers or technologists are centrally involved in practical processes of intervention, negotiation and construction. Furthermore, in contrast to more traditional accounts of the science-technology relationship (such as Bunge’s applied-science account), a technoscientific approach highlights the importance of materiality — that is, the material artifacts, interactions and procedures — for both science and technology. Finally, this approach emphasizes the fact that, in the course of the twentieth century, science has increasingly become ‘big science’ and as such it has acquired — and it does require — the format of an industrial organization. By way of example, consider Bruno Latour, who rejects any basic distinction between science and technology by emphasizing the constructive and adversarial nature of both.

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It is now understandable why, since the beginning of this book, no distinction has been made between what is called a ‘scientific’ fact and what is called a ‘technical’ object or artefact. The problem of the builder of ‘facts’ is the same as the problem of the builder of ‘objects’: how to convince others, how to control their behaviour, how to gather sufficient resources in one place, how to have the claim or the object spread out in time and space.20 In this section I address the views of Srd-an Lelas [1993; 2000], who has developed the science-as-technology account in more philosophical detail. Lelas opposes his account to contemplative, or theoria, views of science. Such views, he claims, separate epistemology from ontology and semantics. That is to say, observation and experiment may be required for ascertaining the truth of theories but as such they are taken to be mere means. After all, whether or not theories are true is supposed to be exclusively a matter of their correspondence to a human-independent reality. Hence, when theories are true, all traces of the way we have found them, through interacting with and intervening in the world, become irrelevant and should be erased. That is to say, ultimately observation and experiment are eliminable. From his science-as-technology perspective, Lelas raises two kinds of objections to such theoria views of science. First, he argues that experimentation, as the design and production of artifacts, involves an interaction and interference with nature, and he notes that scientific observation shares a number of crucial features with experiment [Lelas, 1993]. Through processes of experimentation and observation, which involve the making of artifacts through implementing an idea, science discovers because it invents. In Lelas’s Heideggerian phrase, ‘nature is at once revealed and produced’. The two sides of this process — revealing and producing nature — cannot be separated, as it is done in the theoria account. Lelas concludes that the productive activity of observing and experimenting, which is essentially technological in nature, constitutes an indispensable element of the ontology of science. For this reason, the significance of observation and experimentation goes far beyond their role as instruments for testing the truth of theories. The second objection to theoria views has to do with the function and meaning of theories. Like Janich and Latour, Lelas claims that the meaning of theories cannot be divorced from their function in experimental or observational processes. Theories should be experimentally testable and this requires that the route from theory to experiment should be mapped out by the theory itself. Theory [cannot] be treated as a mere instrument for calculation and prediction of the experimental outcome. It is much more than that. It is an instrument of design, and being that, it encompasses both ontology and technology. A theory can be considered as a condensed set of instructions of how to build an experimental apparatus, or, better, 20 [Latour, 1987, p. 131]. He does, however, allow for some differences in degree, in the sense that scientists more often focus on new and unexpected procedures or objects, while technologists are more often engaged in coordination and consolidation of existing activities or artifacts.


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how to guide the production of experimental artefacts. [Lelas, 1993, p. 442] Hence, the essence of scientific theories is not to be found in their abstract conceptual or mathematical structures as such, but rather in the translations and interpretations which connect theoretical concepts or statements to the practice of observational and experimental action and production. In his book Science and Modernity, Lelas develops these views about science and technology and embeds them in a comprehensive and (broadly) naturalist theory of the processes of human cognition, of the rise of (modern) science and of the nature of scientific knowledge. For instance, from an evolutionary, biological perspective, humans prove to be ‘prematurely born, retarded and unspecialized mammals’. In order to survive they need to be able to adapt to a large variety of selection environments. For this purpose, technology is seen to be particularly important. Artefact making is not the only component of human existence; it covers only one aspect of the relationship between humans and nature. Mind/brain, language and institutions are the others. Together they constitute what we usually call culture. But technology is the essential part of it; it is the part that completes the physical exchange between humans as living systems and their physical environments. [Lelas, 2003, p. 112] Lelas goes on to explain the rise of science as having been enabled by the ‘urban revolution’ in ancient Egypt, the Middle East, India, China and the Americas. Yet modern science, which emerged from the sixteenth and seventeenth century onwards, required two important further developments: first, the economically motivated doctrine and practice of the human mastery of nature; and second an ever increasing transfer of human activities and functions to technological artifacts. This leads him to the aforementioned claims that experimentation constitutes the most important innovation of modern science and, more specifically, that even scientific theory is, ultimately, about making. In concluding this section, I will briefly assess Lelas’s science-as-technology account. His general theory of science and modernity primarily deals with the natural and cultural preconditions and contexts of (modern) science. The theory is thoughtful and intriguing, and Lelas’s book contains a wealth of interesting discussions, but a more detailed review is really beyond the scope of the present chapter (for this, see [Radder, 2002]). Hence, I will limit myself to some more specific remarks on the relationship between science and technology. On the basis of the discussion in the previous sections, in particular Section 5, we may conclude that Lelas’s emphasis of the significance of the action and production character of experimentation is fully justified. Moreover, extending this account from experimentation to scientific observation has much to recommend it. As we have seen, Lelas endorses the more specific claim that theory plays a role not just in making predictions of experimental results but much more generally

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as an instrument guiding the entire process of the production of experimental artifacts. Although some authors have claimed that theory-free experimentation is possible and regularly occurs in the development of science, a closer look at scientific practices reveals that Lelas’s claim can be maintained, if it is more specifically construed as stating that the performance and understanding of experiments depends on a theoretical interpretation of what happens in materially realizing the experimental process [Radder, 2003]. In spite of this, the general reductionist view that science is, basically, technology cannot be upheld. Consider the claims that there is a ‘full continuity between high scientific theory and the skills of the experimenter’ and that ‘a theory can be considered as a condensed set of instructions of how to build an experimental apparatus’ [Lelas, 1993, pp. 441-442]. In this respect it is important to make a distinction between the ‘high theory’ of the object under study and the theoretical interpretation of the entire experimental process. Generally speaking, the former tells you something about the experimental process, but in no way can it be said to guide the production of experimental artifacts. For instance, as we have seen in Section 3, the high theories of quantum physics do not even suffice to construct and use theoretical models of laser phenomena, let alone tell us how to build such devices. A further problem of Lelas’s science-as-technology account is the fact that scientific theories have a meaning that transcends the meaning of the particular experiments that have thus far been used to test these theories. In as far as this account overlaps with the operationalist theory of meaning, it is vulnerable to the well-known criticism that this theory entails an unfruitful proliferation of theoretical concepts and that it neglects the systematic significance of theoretical frameworks [Hempel, 1966, pp. 88-100]. That theories have such a ‘surplus’ meaning can also be seen by analyzing the notion of experimental reproducibility in more detail. In Section 5 I employed the notion of reproducibility in an undifferentiated way. In fact, however, reproducibility is a rather complex notion. First, it is important to distinguish between the actual reproductions and the (claimed) reproducibility of an experiment; in addition, we need to ask what has been reproduced, or is (claimed to be) reproducible, and by whom? [Radder, 1996, Chaps. 2 and 4]. In the present context, the relevant distinction is that between the (claimed) reproducibility of the entire experimental process and the (claimed) reproducibility of the result of this process. An important point of this distinction is that the latter notion, which is also called replicability, implies the reproducibility of the result through a number of possibly radically different experimental processes. Both notions play an important role in scientific practice. On the one hand, if an entire experimental process is reproducible, this fact will facilitate its technological use. For instance, the reproducible procedures of Justus von Liebig’s experiments in organic chemistry definitely facilitated the technological production of artificial fertilizers (even if the full implementation of this agricultural technology, in line with the discussion in Section 5, required further research and additional knowledge). On the other hand, if the result of an experimental process is replicable, it may be considered


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in abstraction of the original experimental process through which it was produced thus far. This kind of abstraction constitutes a first step towards a wider theoretical treatment and understanding of the meaning and implications of this result. Suppose, for example, that certain reproducible experimental processes in a ruby crystal result in the production of a laser beam. If this result is replicable, it will make sense to abstract it from the specific processes in ruby crystals and to study the phenomenon of lasing from a more general, theoretical perspective. This argument may be summarized by saying that theoretical concepts possess a nonlocal meaning, that is to say, a meaning that essentially transcends the meaning they have as interpretations of the local experimental processes to which they have been applied thus far. I conclude that the meaning and function of theories cannot be reduced to their guiding function in producing particular experimental artifacts. This conclusion undermines the core of Lelas’s science-as-technology view, as well as the similar views of other philosophers, such as Latour, the early Habermas, Heidegger and Janich.21

7

CONCLUSION

In this chapter I have addressed the relationship between science and technology, primarily from a conceptual-theoretical perspective but with a keen eye for their actual practices. As we have seen in Section 2, strict definitions of (the aims of) science and technology, in the sense of one or two characteristics that constitute necessary and sufficient conditions, are hard to come by. All attempts to provide essentialist definitions of science and technology prove to be questionable (cf. [Mitcham, 1994] and Mitcham and Schatzberg’s chapter in this Volume, Part I). What results from the preceding discussions is a more differentiated account in which science and technology exhibit both similarities and dissimilarities. Starting from an intuitive pre-understanding that needs to be qualified or modified by empirical studies, science, technology and their relationship may be characterized by these similarities and dissimilarities, or more precisely by certain patterns that they share and by further patterns that are more typical of the one than of the other. Thus, as explained in Section 2, the intuitive idea that the design of material things and processes might constitute an essential contrast between science and technology needs to be adjusted to a pattern of similarity and dissimilarity: since design is a pervasive characteristic of observational and experimental science, the contrast merely applies to theoretical science. Section 5 shows the significance of controlling the interactions of both experimental and technological systems with their environment. At the same time, the typical dissimilarities in spatiotemporal scale and in the nature of the environment entail a number of important cognitive, 21 For an extensive historical review and an intriguing cultural critique of the science-astechnology interpretation, see [Forman, 2007], who argues that the sudden rise of this interpretation (circa 1980) is a major sign of a general turn from modernity towards postmodernity.

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material and social differences between science and technology. Similarly, Section 6 demonstrates that the notion of reproducibility applies both to science and technology. But again, an important dissimilarity arises as well, since technology focuses primarily on the reproducibility of the entire technological process while scientific practice exhibits an additional emphasis on replicability and abstraction. Thus, this line of reasoning goes against the reduction of science to technology and argues for the legitimacy of a theoretical science that is not, or at least not immediately, technologically useful.22 Section 3 shows that Mario Bunge’s account of technology as applied science is fundamentally flawed. The claimed epistemological subordination of technology to science and the alleged insignificance of practical craft work do not fit exemplary episodes of scientific and technological development. A remaining dissimilarity is a greater emphasis (in technology) on realizing external, societal objectives. Yet, even this claim needs a twofold qualification. First, such objectives are, so to speak, the distal, collective aims that need not have an immediate impact on the proximate aims (and hence on the ‘outlook and motivation’) of the individual technologists. Furthermore, as I emphasized in Section 2, basic science — in particular contemporary basic science — may just as well be oriented towards such distal aims. More generally, in agreement with the finalization theory discussed in Section 4, the notion of ‘application’ has become too closely linked to views similar to those of Bunge. Hence, to keep using this notion seems to be ill-advised. Instead, I suggest the locution ‘the uses of science’. Of course, simply replacing ‘applying science’ by ‘using science’ is not very helpful either. We need to specify this phrase in a fourfold way. That is to say, we need to pose and answer the following questions: which aspects of science are used, with which further means, with which technological results, and for which purposes? As for the different ‘aspects of science’, we have seen that not just fundamental laws may be used, but also more local models, and not only theoretical tools but experimental or observational results and techniques as well. What we have also seen, especially in Sections 4 and 5, is that using science requires ‘further means’ in the form of substantial additional work to bridge the gaps between scientific and technological problems, results and contexts. Major examples of such further means are the development of genuinely new technological knowledge and the substantial research needed to transpose the results of successful laboratory experiments to stable and reproducible technological systems. This immediately implies a differentiation in ‘technological results’, which may be technological knowledge, technological methods and procedures, or technological artifacts and systems, including the social knowledge and social conditions needed for their stable and reproducible realization. Finally, there are the ‘purposes of using science’ in tech22 Since patentable technologies need to be ‘industrially applicable’, that is, technologically useful, the argument has significant implications for the justifiability of current practices of academic patenting [Radder, 2006, Chap. 16]. See also van den Belt’s chapter in this Volume, Part VI.


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nological projects. These purposes may be broad, societal aims, but there may also be more limited, scientific ends. Since the advancement of science is often dependent on the availability of cutting edge technological instrumentation, the end of making new instrumentation may be to feed it immediately back into the development of science itself.23 Of course, science is also often used with a view to ‘broader societal aims’. A satisfactory account of the nature and legitimacy of such aims would require much more differentiation. After all, there is a big difference between the case of a single firm wishing to produce a specific artifact for enhancing its own profit or the case of the World Health Organization urging biomedical scientists to develop more medical knowledge and technology for the purpose of a worldwide struggle against malaria. Thus, philosophical accounts of the relationship between science and technology, as discussed in this chapter, should be complemented by equally differentiated accounts of the social and normative issues that are intrinsic to the uses of science in technology.24

ACKNOWLEDGMENTS In discussions about the topics of this chapter, I have received useful feedback from several audiences. It is a pleasure to thank the participants at the preparatory Eindhoven workshop on the Handbook project (in particular, Peter Kroes), the audiences at the Bielefeld/ZiF seminars of the research group ‘Science in the Context of Application’, and the members of the research group ‘Philosophy of Science and Technology’ at VU University Amsterdam.

BIBLIOGRAPHY [Baird, 2003] D. Baird. Thing Knowledge: Outline of a Materialist Theory of Knowledge. In The Philosophy of Scientific Experimentation, H. Radder, ed., pp. 39-67. University of Pittsburgh Press, 2003. [Baird, 2004] D. Baird. Thing Knowledge. A Philosophy of Scientific Instruments. University of California Press, 2004. [Barnes et al., 1996] B. Barnes, D. Bloor, and J. Henry. Scientific Knowledge. A Sociological Analysis. Athlone Press, 1996. [B¨ ohme, 1983] G. B¨ ohme. Autonomization and Finalization: A Comparison of Fermentation Research and Fluid Dynamics. In Finalization in Science, W. Sch¨ afer, ed., pp. 53-91. Reidel, 1983. [B¨ ohme et al., 1983] G. B¨ ohme, W. van den Daele, and R. Hohlfeld. Finalization Revisited. In Finalization in Science, W. Sch¨ afer, ed., pp. 131-172. Reidel, 1983. [Boon, 2006] M. Boon. How Science is Applied in Technology. International Studies in the Philosophy of Science, 20, 27-47, 2006. [Bunge, 1966] M. Bunge. Technology as Applied Science. Technology and Culture, 7, 329-347, 1966. 23 Just think of the impact of particle accelerators and detectors on the history of twentiethcentury microphysics documented in [Galison, 1997]; other examples are the scientific uses of multi-purpose research technologies, such as the ultra-centrifuge, discussed in [Shinn and Joerges, 2002]. 24 See the chapters on normativity and values in Part V of this Handbook.

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[Bunge, 1985] M. Bunge. Treatise on Basic Philosophy. Volume 7: Philosophy of Science and Technology, Part II. Reidel, 1985. [Cartwright, 1983] N. Cartwright. How the Laws of Physics Lie. Clarendon Press, 1983. [Cartwright, 1999] N. Cartwright. The Dappled World. A Study of the Boundaries of Science. Cambridge University Press, 1999. [Collins, 1985] H. M. Collins. Changing Order. Replication and Induction in Scientific Practice. Sage, 1985. [Cuevas, 2005] A. Cuevas. The Many Faces of Science and Technology Relationships. Essays in Philosophy, 6, no. 1, 2005. Available at http://www.humboldt.edu/∼essays/cuevas.html. [De Vries, 2005] M. J. de Vries. 80 Years of Research at the Philips Natuurkundig Laboratorium (1914-1994). Amsterdam University Press, 2005. [Forman, 2007] P. Forman. The Primacy of Science in Modernity, of Technology in Postmodernity, and of Ideology in the History of Technology. History and Technology, 23, 1-152, 2007. [Galison, 1997] P. Galison. Image and Logic. A Material Culture of Microphysics. University of Chicago Press, 1997. [Gibbons et al., 1994] M. Gibbons, C. Limoges, H. Nowotny, S. Schwartzman, P. Scott, and M. Trow. The New Production of Knowledge. The Dynamics of Science and Research in Contemporary Societies. Sage, 1994. [Gooding, 1990] D. Gooding. Experiment and the Making of Meaning. Kluwer, 1990. [Grandin et al., 2004] K. Grandin, N. Wormbs, and S. Widmalm, eds. The Science-Industry Nexus. History, Policy, Implications. Science History Publications, 2004. [Habermas, 1971] J. Habermas. Toward a Rational Society. Heinemann, 1971. [Habermas, 1978] J. Habermas. Knowledge and Human Interests, 2nd edition. Heinemann, 1978. [Hacking, 1983] I. Hacking. Representing and Intervening. Cambridge University Press, 1983. [Harr´ e, 2003] R. Harr´ e. The Materiality of Instruments in a Metaphysics for Experiments. In The Philosophy of Scientific Experimentation, H. Radder, ed., pp. 19-38. University of Pittsburgh Press, 2003. [Heelan, 1983] P. A. Heelan. Space-Perception and the Philosophy of Science. University of California Press, 1983. [Heidelberger, 2003] M. Heidelberger. Theory-Ladenness and Scientific Instruments in Experimentation. In The Philosophy of Scientific Experimentation, H. Radder, ed., pp. 138-151. University of Pittsburgh Press, 2003. [Hempel, 1966] C. G. Hempel. Philosophy of Natural Science. Prentice-Hall, 1966. [Hughes, 1987] T. P. Hughes. The Evolution of Large Technological Systems. In The Social Construction of Technological Systems, W.E. Bijker, T.P. Hughes, and T. Pinch, eds., pp. 51-82. MIT Press, 1987. [Ihde, 1991] D. Ihde. Instrumental Realism. The Interface between Philosophy of Science and Philosophy of Technology. Indiana University Press, 1991. [Ihde, 2004] D. Ihde. Has the Philosophy of Technology Arrived? A State-of-the-Art Review. Philosophy of Science, 71, 117-131, 2004. [Janich, 1978] P. Janich. Physics — Natural Science or Technology? In The Dynamics of Science and Technology, W. Krohn, E.T. Layton, and P. Weingart, eds., pp. 3-27. Reidel, 1978. [Kitcher, 2001] P. Kitcher. Science, Truth, and Democracy. Oxford University Press, 2001. [Koningsveld, 2006] H. Koningsveld. Het verschijnsel wetenschap, revised and expanded edition. Boom, 2006. [Krimsky, 2003] S. Krimsky. Science in the Private Interest. Rowman & Littlefield, 2003. [Kroes, 1992] P. Kroes. On the Role of Design in Engineering Theories; Pambour’s Theory of the Steam Engine. In Technological Development and Science in the Industrial Age, P. Kroes and M. Bakker, eds., pp. 69-98. Kluwer, 1992. [Krohn and Sch¨ afer, 1983] W. Krohn and W. Sch¨ afer. Agricultural Chemistry. The Origin and Structure of a Finalized Science. In Finalization in Science, W. Sch¨ afer, ed., pp. 17-52. Reidel, 1983. [Krohn and Weyer, 1994] W. Krohn and J. Weyer. Society as a Laboratory: The Social Risks of Experimental Research. Science and Public Policy, 21, 321-334, 1994. [Kuhn, 1970] T. S. Kuhn. Logic of Discovery or Psychology of Research? In Criticism and the Growth of Knowledge, I. Lakatos and A. Musgrave, eds., pp. 1-23. Cambridge University Press, 1970.


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[Lange, 1999] R. Lange. Experimentalwissenschaft Biologie. Methodische Grundlagen und Probleme einer technischen Wissenschaft vom Lebendigen. K¨ onigshausen & Neumann, 1999. [Latour, 1987] B. Latour. Science in Action. How to Follow Scientists and Engineers through Society. Open University Press, 1987. [Latour and Woolgar, 1979] B. Latour and S. Woolgar. Laboratory Life. The Social Construction of Scientific Facts. Sage, 1979. [Laudan, 1984] R. Laudan. Introduction. In The Nature of Technological Knowledge, R. Laudan, ed., pp. 1-26. Reidel, 1984. [Layton, 1974] E. T. Layton Jr. Technology as Knowledge. Technology and Culture, 15, 31-41, 1974. [Lelas, 1993] S. Lelas. Science as Technology. British Journal for the Philosophy of Science, 44, 423-442, 1993. [Lelas, 2000] S. Lelas. Science and Modernity. Toward an Integral Theory of Science. Kluwer, 2000. [Mitcham, 1994] C. Mitcham. Thinking through Technology. The Path between Engineering and Philosophy. University of Chicago Press, 1994. [Morgan and Morrison, 1999] M. S. Morgan and M. Morrison, eds. Models as Mediators. Perspectives on the Natural and Social Sciences. Cambridge University Press, 1999. [Nauta and De Vries, 1979] L. Nauta and G. de Vries, eds. Maatschappij of methode. Bijdragen tot het internalisme/externalisme-debat. Special issue of Kennis en methode, 3, 4-198, 1979. [Petersen, 2006] A. C. Petersen. Simulating Nature. A Philosophical Study of ComputerSimulation Uncertainties and Their Role in Climate Science and Policy Advice. Het Spinhuis, 2006. [Radder, 1988] H. Radder. The Material Realization of Science. Van Gorcum, 1988. [Radder, 1996] H. Radder. In and about the World. State University of New York Press, 1996. [Radder, 2002] H. Radder. The Origin and Nature of Modern Science. International Studies in the Philosophy of Science, 16, 291-295, 2002. [Radder, 2003] H. Radder, ed. The Philosophy of Scientific Experimentation. University of Pittsburgh Press, 2003. [Radder, 2003] H. Radder. Technology and Theory in Experimental Science. In The Philosophy of Scientific Experimentation, H. Radder, ed., pp. 152-173. University of Pittsburgh Press, 2003. [Radder, 2006] H. Radder. The World Observed/The World Conceived. University of Pittsburgh Press, 2006. [Ravetz, 1973] J. R. Ravetz. Scientific Knowledge and its Social Problems. Penguin Books, 1973. [Rothbart, 2007] D. Rothbart. Philosophical Instruments. Minds and Tools at Work. University of Illinois Press, 2007. [Rouse, 1987] J. Rouse. Knowledge and Power. Cornell University Press, 1987. [Sch¨ afer, 1983] W. Sch¨ afer, ed. Finalization in Science. Reidel, 1983. [Shinn and Joerges, 2002] T. Shinn and B. Joerges. The Transverse Science and Technology Culture: Dynamics and Roles of Research-Technology. Social Science Information, 41, 207251, 2002. [Tiles and Oberdiek, 1995] M. Tiles and H. Oberdiek. Living in a Technological Culture. Routledge, 1995. [Van Fraassen, 1980] B. C. van Fraassen. The Scientific Image. Clarendon Press, 1980. [Weingart, 1997] P. Weingart. From ‘Finalization’ to ‘Mode 2’: Old Wine in New Bottles? Social Science Information, 36, 591-613, 1997. [Zandvoort, 1986] H. Zandvoort. Models of Scientific Development and the Case of Nuclear Magnetic Resonance. Reidel, 1986.


THE ROLE OF SOCIAL SCIENCE IN ENGINEERING Knut H. Sørensen In an era that seemingly celebrates interdisciplinarity [Nowotny et al., 2001] where technology is no longer the exclusive preserve of engineers one might imagine that it would be rewarding to review research into the influence that social science has had upon engineering. For a long time many have also argued that social science issues should be given more prominence in engineering curricula. More to the point, social studies of technology have repeatedly observed how important the understanding of the social world is to successful engineering. This emanates especially from the consistent reconceptualisation of technology as seamlessly sociotechnical, as an outcome of combining so-to-speak nature and culture (see, e.g., [Bijker et al., 1987; Latour, 1988]). Such observations raise questions about the modes of appropriation of social science that one expects to find among engineers. Broadly speaking, there seem to be two options. One is to adopt transdisciplinary collaboration so that engineers and social scientists work together as specialists from distinct professional fields. This may take the form of teamwork but it can also give social scientists the role of consultants or advisors. I will term this the transdisciplinary mode of appropriation since it involves combining knowledge from different recognised disciplines or professions. Transdisciplinary modes may also be confrontational in the sense of social scientists representing a critique of engineers’ proposals and vice versa. Exchanges across disciplines and professions bring conflict as well as consensus [Sørensen, 2008]. The second mode of appropriation is where social science knowledge and competence is assimilated by the profession of engineers to become part of an increasingly hybrid form of engineering knowledge. Such appropriation may occur during the education of engineers, through what engineers read, from interaction with social scientists, etc. This I will call the profession-based mode of appropriation, since it takes place within an ecology of knowledge production characterised by the professional autonomy of engineers in which engineers remain the active and controlling party. The relative importance of these two modes, together with details about their features, will be discussed in this chapter. The major barrier when assessing these issues is the fairly limited amount of research on engineers’ appropriation and use of social science. The comprehensive literature on social studies of technology emphasizing social aspects of engineering has not been particularly concerned with the actual sources of knowledge on Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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the social aspects and the way in which such knowledge may be appropriated. Moreover, the field of technology studies have engaged in case studies of specific examples of technological development rather than in a broader examination of the education and work practices of engineers. For example, Trevelyan and Tilli [2007, pp. 305-306] conclude their review on research into engineering work by stating [t]hat there has been no recent, comprehensive investigation into the processes involved in engineering work as it is actually practiced. Few researchers have asked engineers what they do and none has asked where they acquired the skills they use; nor have we found any systematic research on the links between what is taught in engineering institutions, what graduates learn early in their careers, what training engineers undertake while in the workforce, and how any of this contributes to producing competent engineers. While they overstate the problem, the main thrust of their argument is justifiable. The volume of research on engineers and engineering is not extensive and the available information about, and analysis of, the role of social science is even more limited. The dominant focus in the literature is the social status and professionalism of engineers, not their actual practice with respect to technology [Sørensen, 1998]. In addition, we face inherent theoretical and methodological difficulties when studying the role of social science knowledge in engineering. In Section 1, I shall briefly examine some of them because they are important for clarifying how the issue in hand — the influence that social science has on engineering — may be understood and discussed. This will serve as a backdrop to the exploration of the two main areas of information pertaining to the role of social science in engineering: education and work or design. Section 2, on education, will explore the place ascribed to social science in engineering curricula. I shall draw especially on a Norwegian study but will also review some programmatic papers about the kinds of skills engineers require. Then, in Section 3, I will briefly discuss the use of social science in the field of information systems design before turning to engineering work and design more broadly in Section 4. Section 5 summarizes the main arguments.

1 THEORETICAL AND METHODOLOGICAL ISSUES Should we expect to be able to detect social science input in engineering and engineering design? Clearly, this a complex issue. How would we unambiguously identify a fact or an insight that identifies design or engineering as originating from the social sciences? A main achievement of historians of technology has been to show that engineering is an independent science, or rather a set of sciences, and not just applied natural science (see, e.g. [Layton, 1971]) or applied social

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science. Clearly, natural science does have a prominent role in the development of modern technology but new technology or solutions to engineering problems are not usually produced through straightforward applications of established natural science facts, theories and discoveries. The main point has been succinctly summarised in the critique of the so-called linear model of innovation (see Radder and Channell, both in this volume, Part 1, for a more detailed discussion). It is a critique that emphasizes that innovation involves autonomous creative acts directed at assembling a mix of relevant kinds of knowledge. Science may play a part as a supplier of such knowledge but not in every case, and the links between scientific knowledge and design choices have proven to be difficult to trace [Kline and Rosenberg, 1986]. Analysing the role of social science knowledge in innovation and engineering may be an even more complex task than in the case of natural science. Firstly, there are many different ways in which results from social research may be implemented, and such use may not always be constructive. For example, Weiss [1979] distinguishes five ways in which policy-makers make use of social science: (1) instrumental use where research results are used in problem-solving, (2) political, conflict-related use where research is used as an argument or weapon in a political controversy, (3) enlightenment because social science research leads users to conceptually re-orient or change their ways of thinking, (4) interactive use, where research is applied in combination with other information to provide a knowledge base for policy purposes and (5) tactical applications where research is used to create a feeling of change or where it becomes part of an “avoid or delay” strategy. The role of social science in engineering could display a similar variety of practices. Secondly, social science knowledge claims, which are often controversial and unstable, are characterised by disagreement and are thus difficult to apply in a setting where one does not wish to take a stand on social issues. At a more basic level, social science representations may interact with and transform the very phenomena that are to be represented (see, for example, [Suchman, 2007]). This is the problem aptly characterised by Giddens [1976] as the double hermeneutic circle: social scientists interpret the world, but the world also interprets social scientists. Thirdly, in general, the social sciences have not given priority to research that aims to be relevant to engineers and to their efforts to design and innovate. Thus, the availability of off-the-shelf social science knowledge applicable to engineering work, together with a bank of social scientists interested in interacting with engineers may prove to be a greater limitation than in the case of natural science. However, these problems should not be overestimated. Many social science-related issues or questions emerging from engineers’ work may be tackled by drawing upon well-established social science knowledge or skills. For example, many engineers base their work on too simplistic assumptions about human behaviour, assumptions that may be disputed or rectified on the basis of fairly standard knowledge about decision-making, consumption or phenomena like cognitive dissonance. Overall, there is a range of situations related to technological design and other forms of engineering work that raise questions relevant to social scientists;

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some that can be quickly answered and some that make it necessary to co-produce new social science knowledge with new engineering knowledge. Fourthly, social scientists are often perceived by engineers to be critical conversationalists engaging in ‘philosophy’, a thing that is not appreciated in a profession which values, above all else, hands-on problem-solving engagement. From interacting professionally with engineers over a long period of time I know from experience that social scientists may be seen as unhelpful sceptics rather than as constructive team-workers. Similarly, many social scientists are critical of engineers whom they tend to find difficult to communicate with and insufficiently reflexive with respect to the effects of their work. Thus, collaboration is not easy. We could, of course, sidestep the challenges discussed in this section by basing the analysis of the impact of social science on engineers’ own accounts of how and to what extent they make use of such knowledge. However, this would at best tell us about the instances where such use was explicit. It seems more probable to assume that if engineers really used knowledge gained from social science, this fact would tend to be rendered invisible in their accounts of developing new technologies because the social sciences are less prestigious than natural science. Social science contributions might consequently become prone to being overlooked or hidden because acknowledging the value of social science input might ultimately damage reputations. Such acknowledgement might even be thought to endanger the scientific status of engineering. It is possible to have a good intuitive understanding of the social conditions of engineering performance without consciously drawing on social science research. For example, such knowledge may be mediated through the mass media, thus being appropriated from journalists’ reports rather than scientific accounts. Alternatively it may become a part of the standard secondary school curriculum. The point is that engineers may be affected by social science without being aware of that form of appropriation. The underlying problem is aptly summed up in the arrogant claim made by a Norwegian research director after a seminar when he stated: “We are all sociologists. We all read newspapers!” In many accounts of successful inventions and innovation a good understanding of user needs and the social context is vital [Freeman, 1982; Bijker et al., 1987; Latour, 1987]. A classic example is Thomas Edison’s assessment of the competitive situation concerning gas when he embarked on the invention of electrical lighting. The system was designed by optimising the cross-section of copper power cables in relation to the price of gas and copper so that electrical lighting could be made cheaper than gas lighting [Hughes, 1985]. Similarly, the Norwegian effort to develop a technology to extract nitrogen from air in order to produce synthetic fertilizer was based on the engineer-entrepreneur Sam Eyde’s comprehensive study of the international fertilizer market and the decline in supply of guano [Sørensen and Levold, 1992]. One way of interpreting such observations emerges from Callon’s [1987] study of an early French effort to develop fuel cells for cars. He observed how engineers produced fairly complex social scenarios to support their project which led him


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to make the — admittedly polemical and overstated — claim that engineers are better at sociology than sociologists. While Callon correctly reminds us that engineers need to possess an awareness of social needs and interests where social scientists may be unable to offer useable knowledge, it is nevertheless something different from what concerns us here. Callon’s claim raises the question of how engineers may learn to act as competent producers of social scenarios. Is this a skill inherent to engineering or is it a qualification that is developed through a mix of experience and exposure to social science observations? A good place to start studying such issues is in the education of engineers. First of all, engineering curricula provide evidence of the extent to which social science is incorporated in the training of engineers. What is of equal importance is the fact that the education of engineers frequently gives rise to debates about what engineers need to know. Such debates would be interesting topics of study because they represent good opportunities to voice the need for change in the education of engineers. 2

WHAT ENGINEERS NEED TO KNOW

Comparative studies of engineers have shown substantial variations in a multitude of dimensions such as status, professional orientation, placement in industrial hierarchies and the relative importance given to theoretical and practical competence [Maurice et al., 1986; Meiksins and Smith, 1996; Sørensen, 1998]. This reflects differences in the roles of engineers in national division of labour systems as well as in relation to historical traditions. Still, it seems that there are some similarities, like the strained relationship between theory and practice, between the perception of engineering as science-based as opposed to growing out of practical, industrial concerns (see also Banse and Grunwald’s chapter in this Volume, Part I). This issue partly emerges from concerns about the social status of engineering. In most countries, having a scientific background is more prestigious than being versed in practical skills. The matter of establishing what engineers need to know has been a controversial issue. Efforts in the late 19th and early 20th centuries to provide engineers with a professional status resulted in the combined challenge of acquiring scientific standing as well as being recognised as educated. This challenge was met in three main ways. In the French tradition, in which the Ecole Polytechnique is the paradigmatic institution, academic status could be achieved by heavily emphasising science and mathematics. The North-American tradition placed greater weight on making liberal arts courses a compulsory part of the engineering curriculum to achieve recognition as educated, while in Germany endeavours to turn engineering into a science with an academic status were successful [Kranakis, 1989; Lundgreen,1990; Manegold, 1978; Noble, 1979; Shinn, 1984]. The sociology of professions has compared engineering to professions like law and medicine and found engineers wanting with respect to autonomy and social status. It has been asked if engineers really constitute a profession. Within the con-

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text of this chapter another concern emerges from the consistent differentiation of engineers into specialist fields like mechanical engineering, civil engineering, chemical engineering, and so on, specialisations that often have their own professional societies. Moreover, as Bucciarelli [1994] shows, these specialists — trained in distinctly different ways — see technologies and define problems in specific ways that cannot easily be communicated across the educational divides. Nevertheless, for the purposes of this chapter, it seems appropriate to regard engineering as a single profession, at least in a national context, partly because engineers are educated similarly in similar types of institutions and partly because they are also organised into general engineering societies designed to help provide a common engineering identity. What is gained from employing the concept of professions is not just to remind of the phenomenon of education-based enclosures within the labour market but also to point to the existence of a regime of organising knowledge that differs from discipline-based patterns. Professions are characterised by outspoken theory-practice concerns involving efforts to strike a balance between the respective importance of professionals’ experience and research-based knowledge. It may be argued that the education of professionals mixes discipline-based knowledge with specialised professional knowledge. In engineering education, this mix has resulted in curricula that contain mathematics and natural science subjects in combination with several kinds of engineering science. Such broad input has provided what may be called polytechnical competence in individual engineers. The term polytechnical is used to highlight the tradition of giving engineers a fairly broad-based education, involving introduction to the basic competence of several fields of engineering and with finally cultivating an area of specialisation. We may recognise this as a particular form of interdisciplinary education. Such interdisciplinary, polytechnical training of for example mechanical engineers would involve teaching them basic knowledge of civil engineering, electrical engineering, chemical engineering, and so on. This corresponded to the demand for less specialised, broadly competent engineers, which dominated in most countries in the early modern period. Social science and humanities subjects could be part of the broad knowledge base, in which case these forms of knowledge were integrated into the hybrid framework created along the lines of the polytechnical knowledge strategy. The result was an individualised polytechnical type of interdisciplinarity which was innately different from the type of interdisciplinarity characterised by specialists collaborating in teams [Sørensen, 1996]. Science, mathematics and the liberal arts have been used to strengthen the social status of engineers, as outlined above. Insofar as social science subjects were made part of engineering curricula, the underlying reasoning seems to have diverged. It was believed that engineers needed to know something about management and business. Subjects like economics, law and business administration were thus taught at many if not most institutions of engineering education. However, these fields of study were seen as peripheral even though many, if not most, engineers tended to embark on management careers, at least until the end of the 1970s [Sørensen, 1998].


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A recent study on the changes in educational ideology with respect to the training of engineers at the Norwegian University of Science and Technology (NTNU, previously the Norwegian Institute of Technology) that took place between 1910 and 2006, provides detailed insight into these underlying considerations [Amdahl and Sørensen, 2008]. The higher education of engineers in Norway was initially particularly shaped by the German tradition adhered to at the Technische Hochschulen but after 1945 it was the US that was turned to for inspiration. The source material — containing the matriculation speeches of the Presidents of the Institute and the reports made by committees engaged in curricular reform — clearly indicates that there was ongoing dialogue with institutions devoted to the education of engineers in other countries. The observations made in this study should therefore have more general validity, even in view of the fact that Norway is a small country with fairly small industrial enterprises. This undoubtedly explains why the polytechnical ideal was probably dominant here for longer than in most other advanced economies. Large companies make better use of specialised engineers than small companies. However, in Norway just like in most other countries, the polytechnical ideal is in decline. The traditional outlook on specialisation among those responsible for educating engineers in Norway was aptly described by the Institute’s President Olav Heggstad in his matriculation speech of 1932: Here, you [the engineering students] will not be educated as specialists but will receive a comprehensive education in a broader professional field. For a time, there was a strong mood for specialisation across the institutes of technology. But this idea has increasingly been departed from among other things because, after the education has ended, it is not certain that the graduate engineers will find employment in their field of specialisation [Amdahl and Sørensen 2008, p. 55]. Later Presidents and curricular reviews emphasized that Norwegian industry consisted largely of small companies in need of engineers with broad areas of competence — polytechnical as defined above — rather than specialised. It was not until the 1970s that the importance of specialised knowledge among engineers was fully recognised. The notion of the engineer as a general kind of practitioner was an indication that individually based polytechnical interdisciplinarity was the dominant mode of education. If the humanities or social sciences were to become integrated in engineering practice, this would have to involve adding such topics to the engineering curriculum. At the Norwegian Institute of Technology there were some social science topics in the curriculum when the Institute was established in 1910 but the scope was modest and the main focus was on certain aspects of economics and a little bit of law. For a long time, the presidents of the Institute mentioned the need for more such topics in their matriculation speeches. Usually they concluded — occasionally with remorse — that such needs could not be catered for. The need for social science-related subjects was not considered large enough to merit

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curricular space. Obviously the various presidents presented their considerations in different ways. Firstly there was a set of responses that dismissed the need for more social science in the engineering curriculum and emphasized that technology represented a prominent cultural element in itself. President Alfred Getz eloquently formulated this point of view in his 1916 matriculation speech: And nevertheless, technology is truly a means of education. It is pure and ideal. Like the artist, the creative power of accomplishment of engineers resides in inner vision and, like the artist, the engineer also has to grapple with the fabric in order to fulfil the spiritually envisaged reality. From this perspective, engineers could easily cope on their own without professional input from the social sciences. Their own cultural capacity would be sufficient to respond to the need of understanding social issues and concerns. Similar ideas have resurged from time to time as counter-arguments to accusations that engineers are narrow-minded cultural dupes (see, e.g., [Florman, 1976]). Secondly, many presidents emphasized that later in their careers, often as managers, engineers would need additional skills like a knowledge of foreign languages, psychology, organisation theory, etc. Students should therefore seek to acquire such skills, but — unfortunately — they would have to do that in their spare time. There was no room for such enlargement of the engineering curriculum. This was also indicative of the perceived low status of social science. A third set of concerns related to the impact of engineering on society and to the social responsibilities of engineers. Such issues were voiced from time to time from 1910 onwards but the plea became more persistent after 1970, clearly in response to discourse that saw technology as a potential social and environmental threat. In his matriculation speech of 1991 President Karsten Jacobsen went so far as to argue that the future of engineering would be shaped by the tension between technology and human concerns: It is no longer sufficient to know one’s discipline; the technologist of the future has to enter the playing field with quite a different and more general value base and outlook than before, with an ability, will and training to face the consequences of this in practice — to see actions from a broader view — what we could call a holistic perspective — technological-ecological-human-aesthetic-economic. [Amdahl and Sørensen, 2008, p. 61] However, at this point in time, specialisation had become the dominant theme underlying the education of engineers. It was not really believed that Norwegian engineering students should receive a much broader and comprehensive training in the social sciences and humanities to be able to act on the challenges outlined by Jacobsen. Rather than asking for new types of knowledge, Jacobsen and indeed later presidents, spoke about the issue in terms of interdisciplinary collaboration.


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The students needed educational reform but this reform had to focus on instilling new virtues rather than on gaining new knowledge. The new virtues called for included, of course, a broader outlook on engineering and greater sensitivity to the social and environmental impacts of the work of engineers. However, even more prominent in the presidents’ speeches was the ideal that engineering students should be encouraged to become skilled in and willing to engage in interdisciplinary activities that also involved graduates from the humanities and the social sciences, and vice versa. As President Eivind HiisHauge put it in his matriculation speech of 2004: No single person is able, with sufficient depth, to be interdisciplinary alone: it is the capacity to perform constructively and to be committed to working together with others in teams which gives results. It was an appeal to students from all academic fields to be prepared for interdisciplinary collaboration. If we return to the two modes of appropriation of social science in engineering proposed in the introduction, we can see that both are emphasised in the above quotation. For a long time, the presidents’ speeches together with all the curricular reviews voiced the opinion that engineers should be able to cope with a broad spectrum of challenges including certain social science concerns on their own. In that way, they were emphasising what I have termed the profession-based mode of appropriation. Social science input were seen as potentially important by some, but ultimately it was given little curricular space and assimilated into the broad polytechnical education of the individual engineer. The switch to a more collective, heterogeneous kind of interdisciplinarity observed in the last decade has accentuated the transdisciplinary mode of appropriation of social science. In the end it may pave the way for a greater number of social scientists working together with engineers — and thus for social science in a distinct and visible form — within the framework of technological development. The critical question is, of course, whether this latter alternative has been realised? What modes of appropriation are most important to engineering education and the professional development of engineers? The US-based Accreditation Board for Engineering and Technology (ABET) is important when it comes to the world-wide setting of standards for engineering education. Their lists of criteria do emphasize teamwork — with or without the prefix ‘multidisciplinary’ — the social sciences though tend to be referred to in an indirect and imprecise manner, like in the mention of the need for students to acquire ‘a broader outlook’ and understanding of the social, economical and political constraints on engineering work, together with an awareness of the importance of social responsibilities.1 Both the social sciences and the humanities are mainly seen to contribute insight into the ethical, legal and social aspects of engineering — all the so-called ELSA concerns also present in the Norwegian situation. The 1 http://www.abet.org/forms.shtml#Applicable

to All Programs (downloaded 30 August 2008).

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ABET criteria therefore invite both modes of appropriation of social science without placing any great emphasis on either mode. Recent discussions on engineering education include contributions that argue in favour of the importance of profession-based appropriation. For example, Vesilind [2001] claims that the traditional view of the encyclopaedic engineer should be maintained but changed — engineering should not just be seen as ‘applied natural science’ but also as ‘applied social science’. Facets of this view seem fairly widespread in the international literature on the skills that are essential to practising engineers and ideas on how engineering education could be changed. Ravensteijn et al. [2006] suggest, for example, the need for engineers to be more communicative. Nguyen [1998] lobbies for communication skills in combination with many other competences related to the various business demands. Grimson [2002] and Robinson et al. [2005] emphasise that engineers need to possess a broader set of non-technical skills. In line with this, Jones [2003] argues that the image of the renaissance engineer might actually be an appropriate educational reform goal; but the main issue is this: what should be the components of appropriate encyclopaedic knowledge? On the other hand, Russell and Stouffer [2005] show how U.S. undergraduate civil engineering education is overwhelmingly dominated by technical subjects, with little indication that profound changes are taking place. This supports the impression given by the ABET list of criteria for the accreditation of such programmes. The U.S. National Academy of Engineering has carried out extensive reviews into the situation of engineers in 2020. These discussions only relate vaguely to the potential role of social sciences with respect to engineering education and work. Interdisciplinarity is signalled as important and social skills are claimed to be important, which means that both modes of appropriation of knowledge of the social sciences are inherently present. However, the overriding impression made by the reports is the assumption that the engineering profession will continue to be largely self-sufficient, thus demonstrating that the profession-based mode of appropriation is given priority [National Academy of Engineering, 2004; 2005]. A different perspective is provided by Williams [2002; 2003]. She argues that during the last few decades, the former close link between technology and engineering has been broken. Technology is no longer exclusively the domain of engineers; engagement with technology has far outgrown any single professio. Williams’ assertion has important implications, not just for engineers but also for social scientists who need to reflect much more about the implications of this change in relation to their own practices. Probably neither group yet fully grasps where this will lead; they may not even have discovered the ongoing change. If Williams is correct, the future development of technology will include interdisciplinary encounters of many kinds but the present developments, as enumerated above, are not promising in this respect. The education of engineers and of social scientists seems to be well entrenched in the established perception that the technical and the social aspects of modern society are worlds apart.


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Williams identifies as a serious challenge what she perceives as the way in which the engineering profession is currently developing along two main lines. One camp is concerned with doing real engineering by designing and building useful things that actually work. The other camp, she observes, advocates a new emphasis on large technological systems and management: Both the design movement and the systems engineering movement seek to reclaim a distinctive identity for engineering: to proclaim that here is something engineers do that scientists and businessmen do not do. In the end, however, the reclamation efforts only underscore engineering’s loss of identity. In both design and systems work, many people other than engineers are in on the act. In design today, engineering, programming, science, language, and art converge. In dealing with technological systems, it is even more obvious that engineers have to collaborate with political scientists, economists, lawyers and managers [Williams, 2003. p. B12]. On the one hand, it seems that Williams is correct to note that late modern technology is embracing an increasingly wider body of disciplines and professions. Consequently, engineering will become more and more engaged in broad transdisciplinary collaboration, also with social scientists and, for that matter, humanities scholars as well. Engineers seem to put considerable effort into preserving the boundaries of their profession and their professional influence, for example by giving priority to the profession-based rather than to the transdisciplinary appropriation of social science. How may such prioritising of profession-based appropriation be achieved and how may that shape the intake of social science knowledge and skills?

3

MAKING SOCIAL SCIENCE THEIR OWN? THE EXAMPLE OF COMPUTER SYSTEMS DESIGN

The area of computing, in particular the sub fields involving the design of information systems, may provide interesting insight into the way in which social science knowledge may be appropriated in engineering. To begin with, we should note that computing — in particular with respect to areas like information systems design, information systems analysis, and software engineering — does draw on methods as well as research findings from social sciences. It is commonly acknowledged that practitioners in these areas need to possess broader-based knowledge. Early examples are Vitalari [1985] and White and Leifer [1986] who argue that the knowledge base of systems analysis should be broad and should include a variety of technical and non-technical skills. For such reasons, some argue that software or information systems engineering is not really engineering at all, but something quite different (see, e.g. [Davis, 1998, pp. 31-40]). Given the existing diversity within the loosely defined profession of

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engineering, such boundary work seems less fruitful. However, software or information systems engineering probably struggles more explicitly than most other fields of engineering with issues similar to those that concern social scientists. In this respect, engineers working with software and information systems will probably find that they are particularly engaged in the appropriation of knowledge and skills gained from social science, thus allowing us a better understanding of the processes and content of such intake. Recent research confirms the early arguments to the effect that software and information systems engineers need broad skills, both technical and non-technical. Iivari et al. [2004] maintain that the distinctive competence of information system experts lies (1) in their expertise of aligning IT artefacts with the organizational and social context in which the artefact in question is to be used, (2) in identifying and specifying the needs of people who are supposed to use the system, (3) in organizational implementation, and (4) in the evaluation/assessment of these artefacts and related changes (see also Radder in this volume, Part V). While the emphasis may vary, such observations of the need to combine technical and non-technical skills seem commonplace [Goles et al., 2008; Lee, 2005; Litecky et al., 2004; Turley and Bieman, 1995]. In fact, the requirements are seen as quite comprehensive. Lee [2005, p. 90] summarizes this succinctly when he remarks that organizations ‘expect their systems analyst to become all-round athletes who play every corner of the field’. Such ideas are also used to argue that a broad education is required if IT specialists are to be properly prepared to combine technical and non-technical challenges in their professional practice [Brookshire et al. 2007, Dahlbom and Mathiassen 1997]. However, Brookshire et al. end up by proposing a fairly conservative curriculum comprising predominantly technical courses. Dahlbom and Mathiassen, on the other hand, suggest taking a much more radical step. They argue that [s]tudents of computing should develop the ability to ask serious questions about the social impact of computing and to evaluate proposed answers to those questions, and they must be able to anticipate the impact of introducing a given product to a given environment. [1997, p. 84] What we observe in this literature is that there is a clear tendency to want to promote individualised polytechnical interdisciplinarity, which is similar to what we observed in Section 2. Even if these authors present their arguments in different ways, their main thrust is that computer professionals should be self-sufficient in terms of the competencies required to carry out IT work. Social science may be a resource, but it should be presented as something that is integrated into systems design or software engineering. In the final instance, the origin of social science input is rendered unclear through the insistence on profession-based appropriation. This is not, of course, a problem in itself. If indeed attainable, individualised polytechnical interdisciplinarity of such comprehensive scope is extremely demanding and therefore also risky with respect to the quality of the outcome.


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Research areas like Human Computer Interaction (HCI) and Computer Supported Collaborative Work (CSCW) are definitively meeting places for scientists with diverse disciplinary backgrounds, including social science. The shared concern for developing more useful computer systems also produces a shared interest in the ways humans interact with computers, what strategies evolve from such interaction and how greater benefits can be achieved from computers. In early participatory computer system design efforts and trade union involvement, social scientists collaborated with computer scientists to organise participation and to establish methods that were helpful for workers to articulate their needs and requests [Ehn, 1988]. However, a main contribution that social scientists have made to HCI and CSCW has been to underline the complexities of human actions and the deep-rooted problems involved in predicting and stabilising human interaction with machines [Suchman, 1987; 2007]. Such critical interventions seem to have been made notice of, but their actual appropriation is less clear, probably because these kinds of insights are difficult to integrate into computer science methodology which, to some extent, depends on achieving some level of prediction and stability. The reviewed literature about software and information systems engineering shows the presence of both modes of appropriation in the case of social science. However, it is particularly the discussion about the training of such engineers and the emphasis placed on a broad knowledge base that also includes strong non-technical components proving that even in this case where concerns for social issues are so prominent, profession-based appropriation dominates. Furthermore, what is appropriated from social scientists is, above all else, methodology. Some software and information systems engineers seem to want to perform their own social science type of investigations but mainstream approaches to the design of computer systems seem less aware that parts of their work — for example the modelling work practices of customers — could just as well be construed as a job for social scientists. When practitioners are asked about the competences they need to carry out their work, they tend to emphasize skills like communication and the understanding of people’s needs. When asked how they can acquire such skills, they tend to point to their experience — not to any form of social science input [Sørensen et al., 2007]. The potential complexity of the information system designing required to facilitate decision-making and to access the knowledge needed to make proper decisions, is considerable. In the long run, this may make the profession-based appropriation of social science inadequate. However, the challenges involved in establishing good practices based on transdisciplinary appropriation may be substantial, not least because of the fairly tight professional collaboration required to achieve accurate forms of knowledge integration. As Lagesen and Sørensen [2008] demonstrate, it is common to assume that communication practices may be separated from computer-related practices like programming, making the first a task for social scientists and the second a job for software specialists. However, Lagesen and Sørensen found that the claim made by software specialists to the effect that a knowledge of computers and programming is needed if they are to communicate

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properly with customers or product users was convincing. If social scientists are to be part of such a process they, like the software specialists, will need to engage in reciprocal profession-based appropriation. Thus, the issue is not perhaps so much that of replacing one strategy of appropriation with another as that of combining them. However, at present, there is little doubt that the pursuit of profession-based appropriation dominates thanks to the prevailing position of individualised polytechnical knowledge performance. Is this situation more generally characteristic of engineers? Our analysis of engineering education does point in that direction but what may be observed from engineers working to develop technology? 4 WHAT SOCIAL STUDIES OF ENGINEERS AND DESIGN TELL US ABOUT THE RELATIONSHIP WITH SOCIAL SCIENCE Probably the most comprehensive studies of engineers and engineering work are those that have emerged from the history and sociology of professions. A main finding was that the professional behaviour of engineers is characterised by less autonomy and a larger degree of collectivist culture than that which applies, for example, to medical doctors or lawyers [Gerstl and Hutton, 1966; Perruci and Gerstl, 1969; Ritti, 1971; Hutton and Lawrence, 1981; Zussman, 1985; Whalley, 1986]. Still, engineers have considerable autonomy as ‘trusted workers’ [Whalley, 1986], even if they work under managerial control, in particular with respect to resources and deadlines [Meiksins and Watson, 1989]. According to these studies, engineers are mainly engaged in a diversity of technical and non-technical work, unless they move into management, which used to be a common career move [Sørensen, 1998]. Since of the focus of these studies is professional behaviour, there is little substantive discussion of the actual content of engineering knowledge and thus of the modes of appropriation of, for example, social science. This even applies to most ethnographic studies on engineering work (see [Bucciarelli, 1994; Downey, 1998; Forsythe, 2001; Vinck, 2003]). Vincenti [1990], whose main concern is engineering knowledge, does not provide insight into such appropriation processes either (see also [Downey and Lucena, 1995]). As already mentioned, management, business administration and economics are long-standing professional interests of engineers. Some such knowledge is a standard part of engineering curricula and many engineers become further educated in these fields. A combination of engineering knowledge, computer skills and knowledge about finance or management has become the basis of careers in consulting [Williams, 2002]. However, this trajectory substantially extends the idea of what constitutes engineering and more to the point, what is normally taken to constitute social science. Rather than being a good example of how the social sciences affect the work of engineers, it points to the development of a set of practices centred on the construction of mathematical or computer models in which technology and social issues tend to be represented only in a very abstract and oversimplified fashion.


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For the purposes of this chapter, design remains a more interesting topic and one in which social science might be expected to be a potentially useful resource. In the previous section, we observed how some such influences could be traced in information system design. However, we also saw that this influence was largely attributable to computer scientists’ profession-based appropriation of social scientific knowledge and methods. This raises interesting questions about the nature of such processes of appropriation in engineering design as well as about the kind of knowledge designers want. It is tempting to assert that social scientists could present engineers with clearly defined design criteria related to user needs, the cultural conditions of domestication of new products, etc. To counter such ideas, Williams et al. [2005, p. 102] warn us about what they call the design fallacy, ‘the presumption that the primary solution to meeting user needs is to build ever more extensive knowledge about the specific context and purposes of an increasing number and variety of users into technology design’. They base this warning on the problems encountered with linear thinking as discussed in Section 2 of this chapter. Still, designers tend to base their notions on certain ideas about future use and users [Akrich, 1992]. Where do such ideas come from? There are many sources that can provide designers with information about users and with ideas about how products may be shaped [Walsh et al., 1992; Williams et al., 2005]. Arguably, social scientists are skilled at analysing the use and users that could produce insights that would be conducive to the development of new products. Increasingly, companies are using market research to inform their efforts with respect to design and innovation. However, such research seems above all to be used to identify potential groups of users/customers, for example according to gender, age, etc., which means that its impact on the work of engineers tends to be a point of departure in terms of design rather than in terms of informing concrete problem-solving efforts [Cockburn and Ormrod, 1993; Chabaud-Rychter, 1994]. Engineers have to use other resources to interpret what is needed to design technology that can be considered appropriate to targeted groups of users, like kitchen appliances for women or microwave ovens for young men. While there are ways to translate user requirements into design specifications (see the contribution of De Vries in this volume, Part III), these methods tend to be vague when it comes to defining what it is that users want. The most common resource used to interpret user requirements is not social science but the personal experience, knowledge and taste of the designing engineers. For example, engineers involved in design are frequently observed to implicitly model the intended user after themselves or to invoke stereotypes [Berg, 1994; Oudshoorn et al., 2004; Williams et al., 2005]. Usability trials may be employed to test how a given design matches user needs and tastes, but most technologies are developed without such testing. Moreover, usability tests are generally undertaken by engineers. Arguably, the study of users could amount to an area of interaction between social science and engineering knowledge. Experiments in participatory design

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have demonstrated the potential to provide better computer systems [Ehn, 1988] but such approaches are expensive and the results have not been convincing. A leading drawback is the widespread perception among designers that users are conservative, the implication being that participation needs to be limited to the shaping of the user — technology interface, while decisions about technology choice and emerging new practices are seen as the prerogative of designers [Hatling and Sørensen, 1998]. Williams et al. [2005] identify a range of other difficulties, for example the fact that user groups may change during the lifecycle of a product. A major challenge is the inherent instability of needs and tastes — what users want, may change as the technology in question develops. It may thus seem more tempting to employ a kind of trial and error approach than to carry out a comprehensive study of users as the backbone of design. In turn, this may make it less interesting for engineers to collaborate with social scientists, partly because they cannot provide the well-defined answers that engineers are looking for and partly because the experiments with potential users that could be undertaken to obtain some relevant input on the design process appear to be too expensive. Trial and error may actually be cheaper, at least if it can be organised on a small scale. Clearly there are huge challenges attached to translating the — already existing or specially produced — knowledge gained from social science into design criteria, shapes and functions. What does the fact that an artefact is easy to use, that a system is efficient or that a machine is flexible actually mean? The challenge is well illustrated by Cockburn and Ormrod [1993]: how can a microwave oven be made attractive to young men? For example, why should one assume, as did the company, that the colour brown is more ‘gender authentic’ to young men than the colour white? These challenges could be viewed as the appropriate tasks of interdisciplinary teams, combining the skills and competencies required to fulfil the relevant problemsolving. Such interdisciplinary practices combining the social and engineering sciences [Sørensen et al., 2008] are not, however, widespread. Instead, as was observed with computer systems designers in the previous section, engineers seem to prefer a professional mode of operation which enables them to opt to independently access information and knowledge about social and cultural aspects mainly by drawing on experience and only occasionally by delving into material related to social science. For example, it is evident that books like those written by Suchman [1987; 2007] or Norman [1988] are being discussed in various design communities. Probably this has resulted more from engineers adhering to a profession-based appropriation of the work rather than to them engaging in interdisciplinary collaboration to combine ideas. Is that a problem? Social scientists might be inclined to think so but if we try to favour one interpretation over the other we will run into difficulties. In the first place there is often dissent among social scientists themselves about how precisely social scientific research should be interpreted. Secondly, and more importantly, what is really at stake here is: the correctness of the interpretation or the quality of the resulting design? On whose premises do we base our decisions — those of the so-


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cial scientists’ or those of the engineers’ ? Does it matter if social scientists claim that engineers have made an incorrect interpretation? From the point of view of engineers — and probably most people — what lies at the heart of the matter is the quality of the resultant design. To prove that a social scientist’s interpretation is ‘better’ than an engineer’s, we need to show that better interpretation leads to better design. Or, to put it more generally, that transdisciplinary appropriation is a more fruitful mode than the profession-based mode. There is no research available to help us to settle this point, so in a sense social scientists are left to prove their own worth. Perhaps such proving is easier in areas like economics and accounting? Financial constraints and economic motives definitely underlie technological design but it is not easy to unambiguously translate technology into economic potential. To a certain extent costs may be predicted and much research has been done into project management, cost control, etc. inside and outside the field of engineering but this aspect of technological development also involves considerable risk as evidenced by the frequent overspending in many projects. According to Thomas [1994], making cost calculations that are perceived by management to be realistic is a prerequisite to starting projects on the designing of new or improved technology. However, whether or not these calculations prove correct is a different matter; they often do not but by then the engineers who initiated the project, tend to have moved on to other projects. Thomas therefore concludes that the creating of new projects is more fundamentally a politicalrhetorical matter, demanding skills in providing the right arguments and making convincing economic calculations, rather than something shaped by what might be termed the strict application of economic knowledge. While it is not well described in the literature reviewed in this chapter, there is no doubt that many companies and laboratories put considerable effort into achieving cost control as far as technological projects are concerned. There are many different methods and tools available to support such efforts. Most engineers are no doubt concerned with economic issues, but ultimately they prefer a professionbased appropriated version, an engineering economics, to the skills and knowledge represented by economists and MBAs. In engineering stories, economists and MBAs are troublemakers rather than helpful parties. The actual influence on technological design and on the engineering work of professional economics may not therefore be so strong. Costs are important, economists are not. When many engineers perceive economists and MBAs to be too conservative and too control-oriented, this probably reflects the different perceptions of the economic dynamics of technological development. In his study of wind turbine development in Norway, Solli [2007] shows how economists evaluated the economic prospects of wind energy on the basis of the notion that production costs were known and would be similar to the costs measured at any given time. The advice of the economists was therefore to say no to wind energy projects. Engineers involved in this technology favoured a more dynamic approach, arguing from the so-called learning curve effects that a considerable drop in production costs would

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occur because accumulated engineering experience would lead to much cheaper installations. The fact that there has actually been a large drop in the cost of producing energy would suggest that, at least in this case, the engineers had a better grasp of the economic dynamics than the economists. However, the matter is more complicated than that, given the restrictions of the available national economic resources. The case demonstrates that also with respect to economics, engineers prefer profession-based appropriation, where some arguments go in their favour but do not necessarily win the day. For a thorough evaluation of the initial decision not to support wind energy technology, we would probably need the kind of transdisciplinary appropriation that results from engineers and economists debating the issue together and appreciating each other’s arguments — whilst perhaps also taking on board other bodies of knowledge.

5 SOCIAL SHAPING VERSUS SOCIAL SCIENCE SHAPING OF TECHNOLOGY Technology is always a social achievement, a material or mental representation of human activity. In principle, this makes the development of technology as much a challenge to social sciences as to engineering. If anything, social science research constitutes an effort to provide representations of human activities. However, there is no guarantee that such representations are useful or will indeed be used by engineers when they engage in design and technological development. As we have seen in this chapter it is rather the case that the relationship between the social sciences and engineering is problematic and unclear. Most studies of engineering work reveal little about how engineers appropriate and use social science. Engineers are probably not very concerned about this because the use of such knowledge is normally implicit and mediated. Social science is most commonly appropriated in a profession-based way, providing professional self-sufficiency and resulting in what I have termed individualised, polytechnical interdisciplinarity. The acknowledgement that technology is socially shaped raises interesting questions about how the social dimensions are represented and mediated in engineering work and design processes. From accounts found in technology and engineering studies, it seems clear that the dominant form of such mediation is in the experience, knowledge, outlook, etc. of the involved engineers In a sense, it is the engineering body that is the main instrument of observing, learning and mediating of social aspects relevant to engineering work. The social sciences have a subdued and much less visible role which is difficult to assess. Some of the features that produce this somewhat paradoxical situation have already been reviewed. Firstly it should be noted that most engineers show rather little interest in the social sciences, with the exception of the areas of management and economics which are perceived to be career-enhancing types of knowledge. Compared to the natural sciences, with which engineers engage heavily, the social


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sciences have less prestige and, probably, are more difficult to translate into useful design criteria. Secondly, today, engineers and natural scientists tend to professionally dominate the development of technology. As a consequence, engineers (and probably also the natural scientists) tend to prefer to abide by their own profession-based appropriation of the social sciences — including economics — rather than to be involved in interdisciplinary collaboration with social scientists. As noted above, this appropriation process seems to be largely about the accumulation of experience — often from interacting with customers and user communities over a fairly long period of time [Sørensen et al., 2007]. Presumably, there is also a kind of ‘citizen effect’ in the sense that some social science knowledge seeps in from news media and similar sources but this phenomenon is also difficult to access and assess in an empirical fashion. Thirdly, as suggested several times throughout the chapter, the social sciences have not particularly set out to be relevant and useful to engineers. Teaching social science to engineering students never had much status; moreover, such teaching has tended to focus on ethical and other social concerns related to the possible negative effects of new technology. In that way the engagement of social science with technology and engineers has been doubly marginalised. It has remained external to the core social science concerns while possessing a kind of policing role that is not particularly appreciated by most engineers. However, it should also be recognised that substantial efforts have been made by groups of social scientists to actively engage in collaboration with engineers and to produce potentially relevant knowledge about many aspects of technology. Nevertheless, it remains a problem that there is little if any empirical research that actually examines such efforts and investigates the role of social science with respect to engineering and the design and development of technology more generally. There is even a danger that the interpretations made in this chapter may underestimate or misjudge the influence of social science. To some extent, this is a methodological problem attributable to the dominant position of the professionbased mode of appropriation in the enactment of social science knowledge among engineers. This form of appropriation tends to reduce the impact of social science. Another challenge is the argument that to engineers (and probably also to social scientists) the insight gained from social science may appear difficult to apply to the design and development of new technologies. When social scientists accentuate the complexity and instability of human cultures, which is what they tend to do (and with good reason), they provide explanations that engineers do not usually find helpful because they in their activities are more concerned about reducing complexity and constructing stable technological standards that will instigate development and problem-solving. Obviously, there are challenges and opportunities behind finding better ways to co-produce new social science and engineering science that may be integrated. My own experience indicates that there is increasing interest among engineers to collaborate with social scientists to find ways to manage challenges that engi-

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neers experience as problematic and outside their professional area of expertise. New technologies may be rejected or may meet with resistance while their actual effects may be quite different from what was originally intended. When social scientists enter into such collaboration, they will probably discover a range of ways of interacting with engineering knowledge. Some problems, like policy conditions or public views about new technologies may be dealt with in a fairly isolated manner while others, like the need to analyse user requirements, may call for more integrated ways of working. The difficulties involved should not be underestimated. For example, the tendency among engineers to find doing more important than reflection and to demand constructive input rather than critical opposition, may mean that social scientists have to adjust their normal mode of operation or vice versa. To communicate and collaborate, one needs some insight into the knowledge of the other party if one is to acquire what Collins and Evans [2007] call interactional expertise — the capability to interact constructively with the experts with whom you are supposed to collaborate. It may thus prove fruitful — at least for some period — for engineers to pursue a profession-based appropriation of relevant social science, while social scientists concentrate on a profession-based appropriation of relevant engineering science, so paving the way for a productive transdisciplinary appropriation of both kinds of knowledge. BIBLIOGRAPHY [Akrich, 1992] M. Akrich. The Description of Technological Objects. In Shaping Technology/Building Society, W.E. Bijker and J. Law, eds., pp. 205-224. MIT Press, 1992. [Amdahl and Sørensen, 2008] E. Amdahl and K. H. Sørensen. Den polytekniske kunnskapsideologien: Fra viten til dyd. In Vitenskap som dialog - kunnskap i bevegelse. Tverrfaglighet og kunnskapskulturer i forskning, K. H. Sørensen, H. J. Gansmo, V. A. Lagesen and E. Amdahl, eds., pp. 49-69. Tapir Akademiske Forlag, 2008. [Berg, 1994] A.-J. Berg. A Gendered Socio-technical Construction: The Smart House. In Bringing Technology Home. Gender and Technology in a Changing Europe, C. Cockburn and R. F¨ urst-Dili, eds., pp. 165-180. Open University Press, 1994. [Bijker et al., 1987] W. E. Bijker, T.P. Hughes, and T. Pinch, eds., The Social Construction of Technological Systems. MIT Press, 1987. [Brookshire et al., 2007] R. G. Brookshire, R. Yin, S. Hunt and T. B. Crews. An End-User Information System for the 21st century. The Journal of Computer Information Systems, 47 (3), 81-88, 2007. [Bucciarelli, 1994] L. L. Bucciarelli. Designing Engineers. MIT Press, 1994. [Callon, 1987] M. Callon. Society in the Making: The Study of Technology as a Tool for Sociological Analysis. In The Social Construction of Technological Systems, W.E. Bijker, T.P. Hughes, and T. Pinch, eds., pp. 83-106. MIT Press, 1987. [Chabaud-Rychter, 1994] D. Chabaud-Rychter. Women Users in the Design Process of a Food Robot: Innovation in a French Domestic Appliance Company. In Bringing Technology Home. Gender and Technology in a Changing Europe, C. Cockburn and R. F¨ urst-Dili, eds., pp. 77-93. Open University Press, 1994. [Cockburn and Ormrod, 1993] C. Cockburn and S. Ormrod. Gender and Technology in the Making. Sage, 1993. [Collins and Evans, 2007] H. Collins and R. Evans. Rethinking Expertise. University of Chicago Press, 2007. [Committee on the Fundamentals of Computer Science, 2004] Committee on the Fundamentals of Computer Science. Computer Science. Reflections on the Field. Reflections from the Field. National Academies Press, 2004.


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THE EMERGENCE OF THE ENGINEERING SCIENCES: AN HISTORICAL ANALYSIS David F. Channell

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INTRODUCTION

The engineering sciences, which provide a basis of knowledge for the understanding and design of humanly constructed artifacts, emerged during the 18th and 19th centuries when new economic, political, social and intellectual forces were creating new relationships between science and technology [Channell, 1989]. Throughout history three main models have played a role in understanding the relationship between science and technology — the independent model, the dependent model and the interdependent model. According to the independent model, science and technology are independent realms of knowledge with little interaction between them. The dependent model sees either technology to depend upon applications of science or science to depend upon applications of technology. Finally the interdependent model argues that the two areas form a symbiotic relationship so that the distinguishing characteristics of the two areas become blurred. Although examples of all three models of the relationship between science and technology often can be found in any given historical period, the independent model tended to be dominant in the ancient and medieval periods, the dependent model was more dominant from the early modern period through the 19th century, and the interdependent model came into dominance during the 20th century. The changing relationships between science and technology were themselves based on changing definitions of the two areas of activity and the emergence of a more or less modern notion of what we today label as science and technology [Oldenziel, 2006]. As such the history of the engineering sciences reflects the development of the more or less modern concepts of science and technology and their interactions [Laudan, 1984; Kline, 1995]. Throughout much of history the activities that we now label as science and technology followed different social and intellectual traditions and, except for a few instances, they had little impact on each other [Layton, 1974] (for an argument that science and technology are linked in a conceptual-theoretical way, see [Radder, this volume, Part I]). Beginning in ancient times the activity we see today as science was more correctly labeled natural philosophy. As a branch of philosophy it focused on asking and answering questions concerning the ultimate nature of the physical world and the universe. Traditionally natural philosophy Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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placed little emphasis on solving individual or practical problems. Rather, natural philosophy used mathematical, geometrical, and other rational methods in order to idealize problems so their solutions could be universally applied to descriptions of nature as a whole. Since natural philosophy relied primarily on contemplation, it became closely associated with the elites and after the founding of universities during the Middle Ages, most natural philosophers had university educations. Finally, because of natural philosophy’s close association with formal education, knowledge of science was gained and disseminated through a written tradition of treatises, textbooks and journals. Since ancient times, the activity we see today as technology was more often labeled the manual, mechanical or technical arts. As a practical activity it was concerned with practical problems that occurred in specific situations. Mechanics and artisans traditionally used cut-and-try empiricism or rule-of-thumb techniques that were applicable to individual situations but usually could not be generalized to a wider class of problems. The mechanical arts were the province of an artisan class and before the 18th century few mechanics had any formalized education or university training. Since most knowledge of mechanical arts was the result of observation and experience, it was primarily gained through an apprenticeship system and disseminated through direct contact between master and pupil. Very little technical knowledge was recorded and when it was it was usually in a visual rather than a written form. During the later medieval and early modern periods some of the intellectual and social differences dividing natural philosophy from the mechanical arts began to be overcome, and by the 18th and 19th centuries social, economic, political and philosophical changes led to the emergence of the more or less modern concepts of science and technology [Kuhn, 1977, pp. 31-65]. With the development of a conception of science that was based on both a mechanical and experimental philosophy and the development of a concept of technology, or engineering, which was based on a science of the practical arts, the door was opened to a new set of interactions between science and technology [Layton, 1971]. This possibility was brought to fruition during the 18th and 19th centuries by the new demands resulting from industrialization and with it the establishment of new institutions, such as engineering schools, technical institutes, industrial research laboratories and professional engineering societies. These new institutions created new research and methodologies that resulted in a new harmony between theory and practice, which led to the emergence of the engineering sciences which served as an intermediary mode of knowledge that linked together science and technology. As an intermediary mode of knowledge, the engineering sciences facilitated the transfer and transformation of knowledge and methodologies between science and technology. As such, the engineering sciences emerged not simply as applications of science to technology but equally as applications of technology to science. By the late 19th century the rise of science-based industries, particularly the chemical and electrical industries, caused the engineering sciences to take on the character of what some have called industry-based sciences [König, 1996, p. 100]. Finally, dur-

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ing the 20th century the rise of the military-industrial complex began to completely blur the distinctions between science and technology and helped to transform the engineering sciences into technoscience. 2

2.1

THE ANCIENT, MEDIEVAL AND EARLY MODERN PERIODS

The ancient period

Although the engineering sciences did not emerge until the 18th and 19th centuries, earlier developments prepared the way for the establishment of the engineering sciences [Landels, 1978]. The dominant philosophies and social systems that influenced ancient Greece and Rome created an ideology that made it difficult for there to be anything more than very limited interactions between natural philosophy and the mechanical arts. The widespread Platonic philosophy in ancient Greece placed the highest intellectual value on an ideal world that could be grasped with the mind and not with the senses. Because of the influence of such a system it was much more important to apply natural philosophy and mathematics to an understanding of the ideal world of the forms rather than to apply that knowledge to the material world which was considered a mere shadow of the ultimate reality. Some of the limitations of Plato’s philosophy were overcome by Aristotle’s philosophy, which placed more value on the material world and the role of the senses, but Aristotle’s influence on an interaction between natural philosophy and the technical arts was limited by social barriers between the two areas. Ancient Greece was a society built on slavery. As such, activities associated with manual labor or the mechanical arts were seen as vulgar and ignoble while activities associated with philosophy were seen as liberal and enlightening. Although interactions between natural philosophy and the mechanical arts were limited during the ancient period there were a few examples of some interactions that would play a role in the future development of the engineering sciences [Clagett, 1963]. Many of these interactions took place in the Hellenistic period during which time the Alexandrian state began to actively support the study of natural philosophy through the establishment of such institutions as the Museum of Alexandria. With state support natural philosophers began to address some problems that were more practical and which reflected the needs of the state. Some contemporaries claimed that one reason for establishing the Museum was so that natural philosophers could help to improve weapons of war [Hacker, 1997]. Beginning as early as the 4th century B.C., the Greeks were undertaking a study to reduce mechanics to mathematical principles. An early work in this tradition was the Treatise on the Balance (c. 300 B.C.), attributed to Euclid who was associated with the Museum of Alexandria. In this work Euclid put forward a geometrical analysis of the lever, but the most extensive mathematical study of mechanics during the Hellenistic period was found in the works of Archimedes, who may have visited Alexandria as a youth [Drachmann, 1962]. In his work, On the Equilibrium of Planes, Archimedes provided a formal mathematical proof of

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the law of the lever using a framework of postulates and propositions similar to the approach used by Euclid in geometry. An important element of Archimedes’ approach was his idealization of all elements of the lever so that the weights on each end became forces acting at single points and the lever became a geometrical line resting on an idealized fulcrum. Although Archimedes’ approach would play a major role in the later development of the application of science to the study of mechanics, there is evidence that the purpose of Euclid’s and Archimedes’ work was to gain insights into mathematical principles and propositions through the use of physical examples, and it may be more correct to see their approach as an application of the technical arts to natural philosophy rather than the other way around. Another important figure in Hellenistic mechanics was Hero of Alexander (1st century C.E.), who followed in the tradition of Archimedes and probably had some connection to the Museum of Alexandria [Drachmann, 1948]. In his treatise the Mechanica, he provided a geometrical analysis of the so-called five simple machines in terms of how they could move a weight using a given force. By analyzing each of the simple machines in terms of a single principle Hero provided the basis for understanding much more complex machines, which could be seen as made up of combinations of simple machines. In his most famous book, the Pneumatica, Hero used the Aristotelian principle of the impossibility of a vacuum to describe a large number of machines and devices that operated with water, air or steam [Hall, 1973]. Again, the Mechanica and the Pneumatica appear to be examples of the application of science to technology, and they would influence later engineers who helped establish the engineering sciences, but several scholars have argued that Hero’s work, like Archimedes’, was intended to show physical examples designed to provide a concrete understanding of some scientific law, such as the principle that nature abhors a vacuum. As such, the development of Hero’s mechanics may be more representative of the application of technology to science.

2.2 The medieval period The development of natural philosophy and the technical arts during the Middle Ages was almost a mirror image of that of the period of the Greeks. During the period of the ancient Greeks natural philosophy and mathematics flourished and the mechanical arts were neglected, but during the medieval period the technical arts began to flourish while natural philosophy became the “handmaiden to theology” [Clagett, 1959; Lindberg, 1992]. Although natural philosophy and the mechanical arts were, for the most part, still considered distinct independent activities, changes that took place during the period began to break down some of the intellectual and social barriers that divided the two areas. The rise of Christianity, which dominated the social and cultural life of the Middle Ages, brought about significant changes in the attitudes toward natural philosophy and the technical arts. The decline in the population during the chaos that resulted from the fall of Rome and discouragement from the Christian Church


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led to a decline in slavery. Along with this, the rise and spread of the monastic system helped to overcome some of the social barriers that had separated natural philosophy from the mechanical arts. The fact that the monks were literate scholars with high social standing and yet were actively engaged in manual labor established the idea of the dignity of labor. The rise of medieval Christianity also provided a new philosophical framework that helped to overcome the intellectual barriers that divided natural philosophy from the mechanical arts [White, 1978]. A number of scholars began to treat the technical arts more like the liberal arts. During the 12th century Domingo Gundisalvo, in his De divisione philosophiae (c. 1150), introduced an Arabic interpretation of Aristotle which argued that the mechanical arts were simply the operative part of theoretical knowledge. At the same time Hugh of St. Victor, master of the abbey of St. Victor near Paris, wrote the Didascalicon, in which he argued that there were seven mechanical arts — weaving, weapon foundry, navigation, agriculture, hunting, medicine and drama — which paralleled the seven liberal arts [Taylor, 1991]. For Hugh the seven mechanical arts could help restore humans to the physical conditions that existed before the Fall. The fact that the mechanical arts were associated with the restoration of a prelapsarian physical state and that they were essentially imitative meant that the mechanical arts had to have some connection to the world of nature and therefore to natural philosophy. He noted that humans were given the capacity of reason so they could study nature and create what they needed through the act of invention. By the 13th century Robert Kilwardby, in his De ortu scientarum, synthesized the ideas of Gundisalvo and Hugh of St. Victor and questioned the still widely held distinction between theoretical and practical knowledge. He argued that theoretical knowledge could often be practical and practical knowledge could be theoretical. This meant that each of the mechanical arts had some relationship to some specific form of theoretical knowledge, such as the relationship between navigation and astronomy. At the same time that some medieval scholars were arguing that the mechanical arts could be seen as more theoretical, other scholars argued that natural philosophy could be seen as more practical. The shortage of Bibles and literate people to read them led many in the Church to turn to another book — the book of nature [White, 1978]. Such a book could be “read,” using a natural theology, for signs or signatures that indicated God’s divine plan. Eventually people came to believe that nature not only contained signs pointing towards salvation but that nature contained the necessary elements to achieve salvation. This led to a reconceptualization of nature that made it much more practical and open to the mechanical arts. Nature, which had been the focus of natural philosophy was no longer seen as an abstract mathematical ideal; rather it was now seen as a storehouse of potentials that could not only be studied for their own sake, but which could be exploited in order to play out some drama of salvation. Instead of a purely contemplative approach to nature the Middle Ages took a more activist approach.

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The reconceptualization of natural philosophy was also influenced by the battle over faith versus reason. Ironically the medieval Church’s attempt to place faith above reason helped to introduce a new more empirical view of natural philosophy [McEvoy, 1982, p. 206]. During the 13th century the Bishop of Paris issued a condemnation of a number of Aristotelian philosophical propositions, such as the argument that God could not create a vacuum or multiple worlds. While the condemnations have been seen as an attack on natural philosophy, several scholars have argued that they opened up new possibilities for natural philosophy. One important result of the condemnations was a new focus on God’s omnipotence. But if God could create the world any way He wanted, the world that humans experienced was contingent rather than necessary. For some medieval scholars this meant that natural philosophy was useless since nature was unpredictable, but others argued that natural philosophy must examine the world directly and empirically in order to discover what kind of world God actually had created. One of the most influential natural philosophers of the late Middle Ages was Robert Grosseteste, who introduced the concept of experimenta into natural philosophy [McEvoy, 1982, pp. 207-208]. Grosseteste’s concept did not include the modern notion of systematic and controlled experiments, but he did argue that a study of the natural world should be based on experience since knowledge of the actual world could never attain the certainty one could gain through the mathematical study of ideal objects. This new empirical view of natural philosophy brought it closer to the mechanical arts as can be seen in the work of Roger Bacon, who was a great admirer of Grosseteste [Easton, 1952; Lindberg, 1983]. But Bacon went beyond Grosseteste and argued that experiments could not just confirm knowledge gained through reason but could be used to discover some practical uses for that knowledge [Lindberg, 1983, p. 226]. In his Secret Operations of Nature Bacon put forward some ways in which natural philosophy could have been of practical use, such as self-propelled carriages and flying machines. While the medieval period began to overcome some of the social and intellectual barriers that separated natural philosophy from the mechanical arts, there were still other barriers that had to be overcome. Many of the medieval scholars who dealt with the mechanical arts focused only on the intellectual or theoretical aspects of those arts and ignored the actual skills and labor that went into them. In doing so they often continued the distinctions between intellectual activity and the manual crafts. More important was the barrier created by Aristotelian philosophy. While there were some challenges to Aristotle in the late Middle Ages, his philosophy, and particularly his organic ontology, still dominated the medieval period. As long as Aristotelian philosophy dominated natural philosophy it was difficult to create a natural philosophy based on mechanical principles that would provide a common ontological framework that could incorporate both science and technology.


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The early modern period

During the early modern period, which included what some have labeled the Scientific Revolution, the social and intellectual barriers that divided the mechanical arts from what was sometimes being labeled science continued to be overcome. Building on inventions, such as the printing press, gunpowder, and new navigational techniques that had originated in the late Middle Ages, the early modern period saw a dramatic expansion of world trade and commercial activity that some have labeled a commercial revolution. This commercial activity, along with almost constant warfare, led many of the European nobility to patronize engineers because of their potential contributions to the military, the mercantile system, and because many of the engineers also had skills in painting, sculpture and architecture. The emergence of the Renaissance artist-engineer, such as Leonardo Da Vinci, Leon Battista Alberti, Filippo Brunelleschi and Francesco di Giorgio Martini helped to bridge the social and intellectual divide that often separated the mechanical arts from natural philosophy [Prager and Scaglia, 1970]. Most artist-engineers had a background in the manual arts but they usually were not illiterate, and because of their connections to the nobility as patrons they had access to the upper classes of society. The Renaissance artist-engineers helped to transform the mechanical arts into a systematic form of knowledge that could be abstractly studied and shared. During the Renaissance, artists discovered the idea of fixed point perspective. While the techniques provided artists with a new style in which to paint, it provided engineers with a powerful new tool with which they could think about technical devices and a new efficient way to share technical information with other engineers [Ferguson, 1977]. Fixed point perspective provided a method to represent a three dimensional machine or structure in two dimensions. In doing so, the technique provided engineers with a way to visualize and analyze machines and structures without going through the time and expense of building the machine or structure. By drawing and analyzing a wide variety of machines, Leonardo Da Vinci was able to discover the concept of a mechanism [Reti, 1974]. Before Leonardo most engineers thought of each machine as unique and something that had to be designed as a whole. But by analyzing drawings of a wide range of machines, Leonardo appeared to understand that different types of machines have a number of elements, or mechanisms, in common, and that machines and structures could be fundamentally reduced to universal components, such as mechanisms or frames. This opened the possibility of a systematic study of machines and structures. At the same time that the mechanical arts were becoming closer to a science, natural philosophy underwent some significant transformations that brought it closer to the mechanical arts [Jacob, 1988]. Many of the significant developments of the ancient Greeks in natural philosophy and mathematics had not been available to the Latin West during the Middle Ages, but during the 15th and 16th centuries new translations of the works of Plato, Euclid, Archimedes and the Greek atomists began to appear in Europe and led to what some have called the Scientific

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Revolution [Lindberg and Westman, 1990]. During this period three approaches to natural philosophy were combined to transform it into something we might label science. First, in some universities, such as Padua, the medical faculty came to dominance over the divinity faculty. As a result more attention was paid to Aristotle’s natural philosophy than to his logical works. This in turn led to new interests in observation and experimentation. Second, the new translations of Plato and Archimedes led to a new interest in developing mathematical theories of natural philosophy. Third, these two different approaches were brought together by the emergence of a magical, or Hermetic, approach to nature [Yates, 1979]. Hermetic ideas strongly influenced a number of leading natural philosophers and led to the view that a combination of theoretical and experimental knowledge could be used to manipulate the forces of nature for some practical purposes [Newman, 2004]. John Dee, a possible model for Shakespeare’s Prospero, argued that both natural philosophy and such mechanical arts as hydraulics, mechanics and navigation, were all governed by geometry, which he saw as a form of mathematical magic [Dee, 1975]. Religious changes in the early modern period, especially those associated with the Protestant Reformation, also played an important role in establishing a more practical view of natural philosophy. The Puritans’ and Anglican Latitudinarians’ emphasis on good works and a millenarian belief in perfecting the world, led to the idea that knowledge, particularly natural philosophy, had to be seen as useful. The idea of a new practical view of natural philosophy was popularized in the 17th century by Francis Bacon, who argued that the goal of natural philosophy should not be simply knowledge, but rather a form of knowledge that would allow humans to exert power and control over nature [Rossi, 1970; Stewart, 1992]. Bacon put forward his idea of a new practical natural philosophy in his New Atlantis (1628), where he described a utopian society which had an institution, called Salomon’s House, in which groups of researchers used knowledge of natural philosophy to improve engines, machines, cannons, clocks and ships [Farrington, 1951; Martin, 1992]. Although Bacon’s goal of a natural philosophy based on the mechanical arts was not realized in his lifetime, his ideas influenced the founding of the Royal Society of London in 1662 and foreshadowed the emergence of the industrial research laboratory in the 20th century. While Bacon put forward the idea that natural philosophy could be applied to the mechanical arts, others began to argue that the mechanical arts could be used to explain natural philosophy. Beginning in the 16th century with René Descartes, and continuing with Pierre Gassendi, Thomas Hobbes, Robert Boyle, G.W. Leibniz and Isaac Newton, natural philosophers began to develop a mechanical philosophy in which all of nature was seen as functioning like a gigantic machine or clockwork mechanism. The fact that much of the natural philosophy that emerged from the Scientific Revolution was conceived in terms of mechanical analogies with machines or clocks made that natural philosophy appeared to be much closer to the mechanical arts [Dijksterhuis, 1961].


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The systematic study of the mechanical arts brought about by the artistengineers and the development of a new practical view of natural philosophy brought about by the Baconian and mechanical philosophies, resulted in a number of developments that would contribute to the later establishment of the engineering sciences [Drake and Drabkin, 1969]. One of the most important of these developments was the work of Galileo. Although he is best known for his support of the Copernican system which resulted in his subsequent trial, Galileo had gained practical experience working in the Venetian Arsenal, which led to a revolutionary analysis of machines which he put forward in his On Mechanics (1600) [Cardwell, 1995, pp. 83-89]. Before Galileo most machines were judged by mostly qualitative standards, such as the quality of construction, and machines were often seen as ingenious devices that somehow functioned by cheating nature, but Galileo argued that machines simply took some natural force and transformed it for a useful purpose. Using Archimedes’ principle of the lever, Galileo showed that in a perfect, frictionless machine, the forces that set the machine in motion were the same as the forces required to keep it in a state of equilibrium. This geometric approach allowed Galileo to calculate how an ideal machine transformed the forces and motions applied to it, and by comparing an actual machine with this ideal machine, he was able to quantitatively evaluate that actual machine in terms of something that would later be called efficiency. After his trial Galileo returned to the study of mechanics and helped to establish some of the basic principles of what would become the engineering sciences through his work Discourse on the Two New Sciences. In the Discourse, which he set in the Venetian Arsenal, Galileo addressed a number of practical problems. First, he analyzed the scale effect and showed why it was not possible to build a machine or structure twice as big as a given machine or structure by simply doubling all of the dimensions of the original. Galileo’s study of the scale effect led him to the first of his new sciences — the strength of materials. Using Archimedes’ geometrical approach, he calculated the amount of weight a beam fixed at one end to a wall could support if loaded at the other end. He was also able to analyze how the shape of the beam would affect its strength. Galileo also used an Archemedian approach in his second new science — the study of motion. Using geometrical principles he was able to show that a projectile fired from a cannon would follow a parabolic path, and he calculated the comparative range of a projectile fired at different angles of elevation. 3

THE ORIGINS OF THE ENGINEERING SCIENCES IN THE 18th AND 19th CENTURIES.

By the 18th century a number of things were coming together that would allow for the emergence of the engineering sciences. The social and intellectual barriers that had divided natural philosophy from the mechanical arts continued to be overcome. During the beginning of this period there were significant developments in technology, especially in Great Britain, that many have called the Industrial Rev-

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olution. The production of iron using coke rather than charcoal, the invention of the steam engine, the mechanization of the textile industry, the centralization of production in the factory system, and the development of railways changed 18th and early 19th century society. With the development of steam engines, railways, ocean-going iron-hulled ships, and large scale iron bridges, it became impractical and uneconomical for engineers to use traditional rule-of-thumb or trial-and-error techniques. At the same time, scientists who were interested in the practical application of science were learning what engineers already knew, that many of the newly discovered laws of science were not directly applicable to technology. Newtonian mechanics might explain the forces acting between two atoms but it did not help in determining how an iron beam might act under a complex load. Boyle’s law explained the relationship between pressure and volume in an ideal gas but was of little use in describing how steam acted in a working steam engine. The Bernoulli equation of classical fluid mechanics had limited application in describing real fluids under non-laminar flow. In response to the needs of the Industrial Revolution a number of institutions arose whose primary goal was to develop sciences that were more technological. Oftentimes these institutions reflected the intellectual and social values of the cultures in which they arose which led to different approaches to the creation of the engineering sciences. In England the new business and industrial classes encouraged the establishment of a number of institutions inspired by the Baconian ideology of a practical application of science. Through Masonic lodges, coffee house lectures, Dissenting Academies, Mechanics’ Institutes, and local provincial societies, such as the Lunar Society of Birmingham and the Manchester Literary and Philosophical Society, scientific ideas, particularly Newtonian natural philosophy, were disseminated and popularized to the newly emerging business and industrial classes [Schofield, 1963]. England’s more democratic approach to the development of a practical science led to an emphasis on empiricism and experimentation which were seen to be less elitist than mathematics. Bacon’s ideology of a practical application of science also influenced France, but social and political differences between England and France led French institutions to become more strongly shaped and more closely aligned with the State [Artz, 1966; Weiss, 1982]. As early as the 17th century Jean-Baptiste Colbert, chief finance minister to Louis XIV, argued that French manufacturers and mercantilists would benefit from educational reform and he proposed establishing a number of academies to teach the scientific and practical basis of such subjects as navigation, bridge building, and manufacturing. By the 18th century the French government’s involvement in military and mercantile projects led to the creation ´ of the Corps des Ponts et Chaussées in 1716 and the establishment of the Ecole des Ponts et Chaussées in 1747 [Brunot and Coquand, 1982]. Soon after, military ´ schools at La Fère and at Mézières were established along with the Ecole des Mines [Alder, 1997]. It was at the artillery school at La Fère where Bernard Forest de Bélidor first used the term engineering science in his book entitled La science des


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ingénieurs [1729]. One of the most important contributions to the emergence of ´ the engineering sciences was the establishment in Paris of the Ecole Polytechnique in 1794-1795 [Fourcy, 1987; Shinn, 1980]. The school, created by Napoleon to train both military and civil engineers, represented the new recognition that these two branches of engineering rested on the same principles. ´ Since in France science had been closely associated with the elite, the Ecole Polytechnique took a very theoretical and mathematical approach to the development of the application of science to technology. The school also helped to spread the new scientific approach to engineering to other countries. The school’s organization and curriculum influenced the Austrian polytechnics at Vienna and Prague, the German Technische Hochschulen at Karlsruhe, Munich, Dresden, Stuttgart and Hannover, and the Military Academy at West Point and Rensselaer Polytech´ nic Institute in America. Although influenced by the Ecole Polytechnique, the Austrian polytechnics and the German Technische Hochschulen developed their own model of the interaction of science and technology [Gispen, 1989]. Prechtl, the founder of the Vienna Polytechnic Institute, which served as a model for the German Technische Hochschulen, combined the French idea that science and mathematics served as a common basis for the study of technology, with the German university ideal that education should be based on some inner principle or unity of knowledge called Bildung [Fox and Guagnini, 1998-1999, pp. 100-107; Gispen, 1989, p.42]. The result was to go beyond the French idea of the engineering sciences as simply applied science and to develop a true and autonomous engineering science that could synthesize scientific theory with technological practices [Manegold, 1992, p. 142]. Scotland played one of the most significant roles in the emergence of the engineering sciences [Marsden, 1992]. Scotland shared cultural values with both England and France and was able to bring together the empirical/experimental traditions of the English with the theoretical/mathematical traditions of the French. The leading figure in the development of what would be called engineering science was W.J.M. Rankine at Glasgow University [Channell, 1982]. As one of the first professors of engineering at a British university, he faced the challenge of not duplicating what was being taught by the science faculties but not interfering with the practical education given through the apprenticeship system. Rankine’s solution was to create an autonomous branch of knowledge, which he labeled engineering science, which would be an intermediary between pure theory and pure practice. The creation of a new “harmony of theory and practice” would be the result of bringing together the practical observations and experiences of the properties of materials with the theoretical laws governing the action of machines and structures, and treating them as a science. In doing so the harmony of theory and practice would not duplicate existing sciences but instead would establish new engineering sciences. Rankine spread his idea of engineering science through the publication of a series of Manuals that became the standard textbooks for university trained engineers throughout Europe, America and even Japan. On the Continent, Ferdinand Redtenbacher at the Technische Hochschule at Karlsruhe

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played a role similar to Rankine’s by using practice to inform theory so as to create autonomous engineering sciences, which he disseminated through a series of textbooks [B¨ ohme, Van den Daele and Krohn, 1978, pp. 237-238]. During the 19th century there was a struggle in America between the “shop culture” and the “school culture” which wanted to emphasize the links between engineering and science which could be taught through formal education [Calvert, 1967]. By the end of the century the “school culture” came into dominance, and was reflected in the work of the leading American exponent of engineering science, Robert Henry Thurston who taught at the U.S. Naval Academy, Stevens Institute of Technology and Cornell University. Although influenced by Rankine, Thurston developed his own philosophy of engineering science while at Stevens and at Cornell. Rather than apply the laws of science to technology, Thurston argued that the methodology of science, which he saw as essentially Baconian, should be applied to technology. By collecting facts through observation and then inductively developing laws, Thurston believed that new laws of technology could be developed that were independent of scientific laws. Although like Rankine and Redtenbacher, Thurston also produced a number of textbooks; his most significant contribution was the establishment at Stevens of the first mechanical engineering laboratory in America [Durand, 1929, pp. 68-71].

3.1 The strength of materials During the 18th and 19th centuries the emergence of the engineering sciences can be seen in the development of a number of fields. With the demand for larger more complex machines, ships and structures brought about by industrialization and the military’s demand for larger and stronger fortifications, new approaches to the strength of materials and the theory of elasticity were needed [Timoshenko, 1953; Todhunter, 1886-1893]. Some of the earliest work on experimental testing of a variety of materials was carried out by the Dutch natural philosopher Pieter van Musschenbroek and reported in 1729. Using a series of machines he conducted tests on small scale samples to determine when various materials would fail under tension, compression and bending. A number of French engineers argued that small scale tests would be of little use in large scale construction projects. In response Emiland Gauthey, of the Corps de Ponts et Chaussées and Jean Rodolphe ´ Perronet, of the Ecole de Ponts et Chaussées each built machines that tested large scale samples of stone used in bridge building [Kranakis, 1997, pp. 106-107]. In the later part of the 18th century Charles Coulomb, while serving as an engineer on Martinique, conducted a series of experimental studies which allowed him to relate tensile strength to shear strength and to develop a mathematical analysis of bending [Gillmor, 1971; Heyman, 1972]. During the 19th century Peter Barlow in England built a series of machines at the Royal Dockyard in order to test timber for ships. The introduction of iron as a building material stimulated a number of additional experimental studies on the strength of materials. In England Barlow, William Fairbairn and Eaton Hodgkinson all conducted extensive tests on cast


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and wrought iron. On the Continent, Johann Bauschinger established one of the earliest academic materials testing laboratories at the Polytechnic Institute of Munich, while in America Robert Thurston established materials testing laboratories at Stevens and Cornell. This work helped establish what Edward Constant has called a “tradition of testability” [Constant, 1980, p. 20]. While a number of experimental studies were being conducted on the strength of materials, important advancements were taking place in the theory of elasticity. During the late 17th and early 18th century Jacob and John Bernoulli applied Leibniz’s calculus to the study of the elastic deflection of beams, and soon after Jacob’s son Daniel developed differential equations describing vibratory motion in elastic materials. This work was further extended by Leonhard Euler, one of Daniel’s students. During the 19th century Augustin Cauchy, Gabriel Lamé, C.L.M.H. Navier, Siméon Poisson and Adhemar Barré de Saint-Vanant made significant contributions to the theory of elasticity. Particularly important was Cauchy’s formulation in 1822 of the concept of stress, which incorporated elements of both science and technology. In Germany Johann Bauschinger and Hermann Zimmermann applied the technique of graphical analysis to the analysis of stresses. The development of systematic testing, the creation of new concepts, and the use of graphical analysis would all become hallmarks of the emergence of an engineering science.

3.2

The study of structures

Closely connected, and often overlapping with the study of the strength of materials and the theory of elasticity, was the development of new experimental and theoretical approaches to the study of structures. Again, the increase of scale of structures, such as bridges, and the introduction of new building materials, such as iron, led to new demands for ways to analyze such structures [Billington, 1983]. During the 18th century Charles Coulomb used both mathematical theories and practical data to develop theories of retaining walls and arches [Gillmor, 1971; Heyman, 1972]. At the beginning of the 19th century the American James Finley conducted a series of experiments in order to acquire the knowledge that he needed to develop design principles for the construction of some of the earliest suspension bridges [Kranakis, 1997]. Finley’s work influenced British engineers, such as Thomas Telford, who used similar experimental techniques to design some of the first suspension bridges in Britain. About mid-century William Fairbairn, Eaton Hodgkinson and Robert Stephenson conducted experimental studies that contributed to the design of the Conway and the Britannia tubular railway bridges [Vincenti and Rosenberg, 1978]. While the Americans and the British were taking a more experimental approach to the design of structures, the French were developing a more theoreti´ cal approach. C.L.M.H. Navier, who studied at the Ecole Polytechnique and at ´ the Ecole de Ponts et Chaussées, developed a mathematical theory of suspension bridges [Kranakis, 1997]. This theory led engineers to see that a connection existed between the role of the strength of materials and the theory of elasticity and

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that both were essential for understanding the stability of structures. By the mid-19th century, some American, British and German engineers began to develop an approach to the study of structures that was in between a purely empirical and a purely theoretical approach. In Britain W.J.M. Rankine and James Clerk Maxwell developed the idea of parallel projection and reciprocal figures which established a graphical manner in which complex structures were related to simple structures and provided new ways to study the forces acting in these complex structures. In America, Squire Whipple developed a graphical approach to the analysis of truss bridges, while in Germany Karl Culmann, a graduate of the Technische Hochschule at Karlsruhe, developed another graphical method for the analysis of truss bridges [Timoshenko, 1953, pp. 190-197]. Again, by creating a middle ground between the highly mathematical approach of natural philosophers and the purely empirical approach of many mechanics, graphical approaches became one of the characteristics of the engineering sciences.

3.3

The study of machines

During the 18th and 19th centuries, developments in the study of machines contributed to the emergence of the engineering sciences. With the new demands for manufacturing brought about by mercantilism and industrialization, scientists and engineers began to focus their attention on understanding and improving machines [Reynolds, 1983]. Throughout much of the 18th century the study of machines focused on the waterwheel. Building on Galileo’s idea that machines should be analyzed in terms of their ability to apply the forces of nature in the most efficient way, the Frenchman Antoine Parent used the differential calculus to calculate the maximum efficiency of undershot waterwheels in 1704. A few years later Henri Pitot calculated the optimum number of blades an undershot waterwheel of a given size should have and presented his calculations in tabular form. By mid-century a debate arose whether the undershot or overshot waterwheel was the most efficient. Antoine de Parcieux, a member of the French Academy of Sciences, conducted a series of experimental tests using scale models and concluded that the overshot wheel was more efficient. At about the same time, John Smeaton in Britain began conducting an extensive series of experiments in which he systematically varied the type of wheel, the quantity of flowing water, the head of water, and the load on the wheel, and concluded that the overshot wheel was more efficient. His experiments would help establish a new “tradition of testability” [Constant, 1980, p. 20]. Smeaton’s technique would later be called “parameter variation,” and would become an important methodology of the engineering sciences [Vincenti, 1990, pp. 146-151]. The experimental research on waterwheels encouraged new theoretical work. In France, Jean Charles Borda, a member of the Academy of Sciences, analyzed waterwheels in terms of vis viva (mv2 — which is related to the modern concept of kinetic energy) and concluded that the inefficiencies of the undershot wheel were the result of vis viva being lost to turbulence. In 1783 Lazare Carnot, in


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his Essai sur les machines en genéral extended Borda’s analysis of waterwheels and developed a theory that was applicable to all machines [Gillispie, 1971]. By applying the idea of vis viva to machines Carnot concluded that all impacts and shocks needed to be avoided. By the 19th century the combination of experimental and theoretical studies of machines led to new design principles. In 1824 Jean Victor Poncelet designed a new waterwheel based on the work of Borda and Carnot. In America during the middle of the 19th century James B. Francis began a series of extensive experiments on a water turbine designed by Benoit Fourneyron [Layton, 1979]. Combining both the experiments and theoretical work based on Newtonian mechanics, Francis developed a series of design principles for turbines. At about the same time, G.G. Coriolis in France and William Whewell and Henry Moseley in Britain employed the idea of work to analyze machines. By comparing the transmitted work to the wasted work, they analyzed machines in terms of efficiency which would become another fundamental concept of the engineering sciences. Machines like waterwheels and turbines could be seen as devices that transmitted and modified force or work but machines could also be seen as devices that transmitted and modified motion. If one neglected the action of dynamic forces, a machine could be analyzed in terms of what became known as the theory of mechanisms [Ferguson, 1962; Hartenberg and Denavit, 1964], an idea that went back to Leonardo Da Vinci. By the beginning of the 18th century the Swedish natural philosopher Christopher Polhem created a Laboratorium mechanicum by collecting mechanisms from across Sweden. He put forward the idea that all machines could be created from a “mechanical alphabet” with the five ancient machines serving as vowels and other mechanisms serving as consonants. Throughout the 18th and 19th centuries several institutions, such as the Conservatoire des Arts et Métiers in Paris, the Royal Institution in London and the Franklin Institute in Philadelphia, established large collections of mechanisms in order to provide a basis for the understanding and improvement of machinery. At the end of the ´ 18th century and the beginning of the 19th century Gaspard Monge at the Ecole Polytechnique argued that machines could best be understood by analyzing the elements of a machine that converted one type of motion into another. In order to do so, Monge and his followers, Agustin de Betancourt, Pierre Hâchette and Phillipe Lanz, developed a system to classify mechanisms that was similar to Carl Linnaeus’ binomial classification system for plants. This work was extended by André-Marie Ampère, who coined the term cinématique, later called kinematics. By the middle of the 19th century Robert Willis at Cambridge began studying mechanisms in terms of the relationship of motions created by the mechanism, seeing that the action of a mechanism was independent of the given motion that was applied to the mechanism. By the second half of the century, Franz Reuleaux, at Charlottenberg, further extended the theory of mechanisms by moving away from the study of individual mechanisms and toward the analysis of mechanisms as part of an integrated system. Particularly important work was also done by

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James Clerk Maxwell in Britain in 1868 with his analysis of mechanical governors which led to one of the earliest theories of feedback control [Mayr, 1970].

3.4 Thermodynamics One of the most significant developments in the emergence of the engineering sciences was the establishment of thermodynamics [Cardwell, 1971]. With the demand for new sources of power brought about by the Industrial Revolution, and with the invention of the Newcomen, Watt, and high pressure engines to meet some of those new demands, there was an increased interest in improving the efficiency of these engines by gaining a better understanding of the scientific principles which lay behind heat engines. Beginning in 1768 John Smeaton conducted a series of experimental studies on a model steam engine using the same method of parameter variation that he used in his study of waterwheels. At the end of the 18th century James Watt conducted a number of experiments in order to improve his understanding of his engines. The fact that Watt allowed steam to expand in his engines made it difficult to calculate the work done by an engine without knowing how the pressure in the cylinder changed throughout the stroke. In 1796 one of Watt’s assistants developed a simple device that would make an “indicator diagram” which would record the drop in pressure by marking a piece of paper throughout the stroke. During the 19th century the pressure-volume (PV) diagram would become a fundamental element of thermodynamics. The invention in England of the high pressure steam engine at the beginning of the 19th century further stimulated the development of thermodynamics. When French engineers first saw the new high pressure steam engine, they began a program to understand the reasons behind its increased efficiency. The leading figure ´ in this effort was Nicholas Léonard Sadi Carnot, who was trained at the Ecole Polytechnique. Still believing in a material theory of heat, Carnot used the work done on water power by 18th century French engineers to develop an analogous set of conditions for the maximum efficiency of a heat engine. The results, published in his Réflexions sur la Puissance Motrice du Feu (1824), outlined what became known as the Carnot cycle which Emile Clapeyron later showed could be represented as a PV diagram. Carnot showed that his cycle represented the most efficient heat engine and that the cycle was independent of any particular theory as to the nature of heat, making his theory a completely general theory that could be applied to any type of heat engine. The development in the 1840s and 1850s of the mechanical theory of heat by J.P. Joule and others led to attempts by William Thomson (later Lord Kelvin) and W.J.M. Rankine in Britain and Rudolf Clausius in Germany to reconcile Carnot’s theory, which argued that work was produced by the transfer of heat from a higher temperature to a lower one, with Joule’s theory that heat was converted into work [Smith, 1998]. Clausius argued that the two theories could be reconciled if during a Carnot cycle some of the heat was converted into work and another proportion was simply transferred to a lower temperature. By mid-century Thomson and


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Rankine began to reformulate thermodynamics in terms of the new concept of energy. Near the same time Clausius and Rankine independently developed a formula for the heat dissipated in a Carnot cycle. Clausius would later introduce the term entropy to describe the “equivalence value” of this dissipated heat. Using the new concepts of energy and entropy Clausius was able to formulate two laws of thermodynamics: the first law being that the energy in the universe, or in any closed subsystem, was constant and the second law being that the entropy of the universe, or any closed subsystem, always tends towards a maximum. Although the theory of thermodynamics arose out of a study of the steam engine it was soon realized that the concepts of energy and entropy were not limited to the phenomena of heat but were universal concepts that could be applied to a wide range of scientific and technological phenomena, making thermodynamics a true engineering science.

3.5

Fluid mechanics

During the 18th and 19th centuries, developments in fluid mechanics also contributed to the emergence of the engineering sciences [Anderson, 1997; Rouce and Ince, 1957]. New demands to improve water power, to design better ships, and to better understand ballistics led to new theoretical and experimental studies of the behavior of fluids and objects moving through them. As early as 1673, in response to Colbert’s attempt to rationalize shipbuilding, Ignace-Gaston Paridies developed a theoretical study of bodies moving through fluids at varying speeds, and in 1697 Paul Hoste published an analysis of how the form of a ship influenced its speed and stability. In 1738 Daniel Bernoulli published his book Hydrodynamica in which he applied Newtonian mechanics and the Leibnizian concept of vis viva to the study of fluids. The book contained, although somewhat obscurely, what has come to be known as the Bernoulli principle which states that an increase in velocity of a fluid will result in a decrease in pressure of that fluid. The theoretical work done by Bernoulli was continued by Jean d’Alembert, who derived a differential equation that governed the motion of a fluid element of fixed mass. This theoretical work in the 18th century reached its high point with the work of Leonhard Euler, who placed the work of Bernoulli and d’Alembert into a consistent mathematical framework. In three papers published between 1752 and 1755 Euler formulated the basic equations that explained much of the behavior of fluids. Bernoulli, d’Alembert and Euler each used their theoretical studies to analyze the relationship between fluid mechanics and ship design [Pritchard, 1987]. While theoretical studies of fluid mechanics were taking place on the Continent, the British were conducting some important experimental studies, particularly on the movements of bodies through the air. During the middle of the 18th century Benjamin Robins used a pendulum-like device and a whirling arm mechanism to measure how air resistance affected cannon balls [Steele, 1997, pp. 145-180]. Euler would later use Robins’ data to develop a mathematical theory of ballistics. Not long after Robins’ work, John Smeaton conducted a series of experiments on

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windmills. His experiments led him to conclude that air moving over a curved surface created more force than air moving over a flat surface. At the beginning of the 19th century George Caley extended Smeaton’s work and demonstrated how a wing moving through the air could give rise to aerodynamic lift. During the 19th century engineers and scientists extended the work that had been done in the 18th century [Anderson, 1997]. Euler’s work had led to a significant advance in the understanding of the motion of fluids but his work neglected the effects of friction which was a particularly important effect when fluids were moving near a solid surface, like a hull or a wing. Between 1822 and 1845 the Frenchman C.L.M.H. Navier and the Irishman George Gabriel Stokes independently derived equations, known as the Navier-Stokes equations, which took into account the role of friction in the flow of fluids. As in other areas of the engineering sciences, a few 19th century engineers, especially in Britain, began to develop a more graphical approach to the problem of fluid mechanics that would provide a bridge between mathematical theories and practical experimental data. One area where this approach played an important role was in naval architecture. Traditionally ships’ hulls were designed with smooth surfaces but this was based on experience rather than theory. With larger and faster ships, naval architects needed to relate the shape of a ship’s hull to some laws of the motion of fluids in order to be able to rationally design a hull. In the 1840s John Scott Russell developed a graphical approach that based the shape of a hull on the shape of natural waves in water [Emmerson, 1977]. This idea was further developed by W.J.M. Rankine in 1870 when he introduced the new concept of streamlines, which would become a fundamental part of the new conceptual framework of the engineering sciences. 4 THE ENGINEERING SCIENCES IN THE ERA OF INDUSTRIAL RESEARCH: 1850-1925 During the late 18th and early 19th centuries the engineering sciences began to ´ emerge, primarily from academic institutions, such as the Ecole Polytechnique, Glasgow University, Stevens Institute of Technology, and the German Technische Hochschulen. But during the late 19th and early 20th centuries the engineering sciences more and more became associated with industrial research laboratories [Fox and Guagnini, 1998-1999]. This helped to reshape the engineering sciences into what some have labeled industry-based sciences [König, 1996, p. 100]. Beginning in the late 18th century there were significant new advancements in science, particularly in the understanding of chemistry and the understanding of electricity and magnetism. Some have called these developments a second scientific revolution. A result of this new scientific understanding was the emergence of new large-scale science-based industries which some have called a second industrial revolution. The new understanding of chemistry that emerged at the end of the 18th century led to new discoveries, such as coal tar dyes, the Leblanc process, the Solvay process, celluloid and plastics. These served as the basis for companies,


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such as Du Pont, BASF, Bayer, I.G. Farben and Kodak. The discovery of phenomena such as electromagnetism and electromagnetic induction during the early 19th century were quickly developed into inventions, such as the telegraph, the telephone, electric motors, electric lights and dynamos. These inventions in turn served as the basis for new large scale companies, such as Western Union, American Bell Telephone, Edison General Electric, Westinghouse, Telefunken, and Siemens. With the emergence of these science-based industries came the realization that industrialists could no longer rely on the Romantic ideal in which inventions appeared as the result of some flash of inspiration from a lone inventor. Instead there was the recognition that discovery and invention could be the result of a rational and planned process undertaken by a group of researchers. As a result, the establishment of the industrial research laboratory staffed by a multidisciplinary team of scientists and engineers helped to transform the engineering sciences into industry-based sciences. In addition to industrial research laboratories, universities began to establish experimental engineering laboratories and research stations, which often became closely linked to industries [Seely, 1993].

4.1

The chemical industries

Many of the developments in the chemical industries [Clow and Clow, 1952] during the 19th century can be traced to the work of the German chemist Justus ´ von Liebig, who after studying at the Ecole Polytechnique returned to Germany and established a chemical research laboratory at the University of Giessen [Beer, 1959]. The laboratory reflected the dual goals that the German educational system should teach both existing knowledge and create new knowledge. Although the laboratory’s original purpose was to train pharmacists, its emphasis slowly shifted to research in organic chemistry. In the process, Liebig created a number of instruments and techniques that allowed his students to analyze a large number of organic compounds. Most importantly, in 1840 he introduced a new approach to research in his laboratory. He began to organize a significant number of his students into teams to conduct research on fatty acids. This idea of systematic group research focused on a specific problem would become a hallmark of industry-based science. Liebig’s laboratory served to train an entire generation of academic and industrial chemists. One of the most important of Liebig’s students was August Wilhelm von Hofmann, who became professor of chemistry at the Royal College of Chemistry in England in 1845. As part of a systematic search for useful properties of organic compounds, Hofmann’s student, William Perkins in 1856 discovered that aniline, a byproduct of coal tar, had the ability to dye textiles a bright purple. The commercial success of this first chemical dye led to the search for other dyes in England and France. Through a chemical analysis of the first aniline dyes Hofmann provided a method by which chemists could systematically create new color dyes. Although the first chemical dyes were produced in England and France, by the 1870s Germany began to dominate the field by breaking down the social and edu-

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cational barriers that separated the universities from the Technische Hochschulen and thus creating a means by which theoretical knowledge could be combined with systematic experimental work. This allowed a bridging of the gap between academia and the factory. The factory-centered industrial research laboratory emerged in response to the demand for continual innovation which resulted from the commercial success of chemical dyes and the pressure to produce new and cheaper dyes. Some scholars have argued that the roots of industrial research in Germany can be traced to 1868 when BASF hired Heinrich Caro to become technical director of the company. Over the next few years Caro recruited a number of chemists from universities and Technische Hochschulen and created a central laboratory in 1889. Other companies followed BASF’s lead. In 1891 the Bayer Company centralized its research in a new laboratory. A distinguishing characteristic of the industrial research laboratory was group research. The discovery of new azo dyes opened the possibility of more than one hundred million new dyes. With such a large number of possible dyes the era of the lone inventor using trial and error was over. In its place came what Caro labeled “scientific mass work” (wissenschaftliche Massenarbeit) [Meyer-Thurow, 1982, p. 378]. The creation of the industrial research laboratory also led to a new relationship between academia and industry. As industrial research became more autonomous chemical firms began to rely more heavily upon university and Technische Hochschule graduates, especially those with Ph.D.s. In turn the success of industrial research laboratories put pressure on universities and Technische Hochschulen to adapt their curricula to the needs of industry. Wolfgang K¨ onig has argued that since a significant number of the faculty at German universities and Technische Hochschulen had gained their experience in industrial research laboratories, much of the work they produced should be classified as industry-based science [K¨ onig, 1996, p. 87]. The development of industrial research in the chemical dye industry quickly spread to other chemical industries. The pressure for continual innovation in chemical dyes soon led to the development of other organic chemicals, especially pharmaceuticals, celluloids and plastics. During the 19th century the new understanding of chemistry also led to the industrial development of heavy chemicals, such as alkalies, acids, fertilizers and explosives. In all of these areas chemical firms, such as Bayer, Agfa, Kodak and Du Pont established industrial research laboratories modeled after those in the chemical dye industry.

4.2 The electrical industries Along with the chemical industries, the leading area that contributed most to the transformation of the engineering sciences into industry-based sciences was the development of the electrical industries and the emerging field of electrical engineering [Reich, 1985]. The dependence of the electrical industries upon new scientific discoveries led them to follow a path similar to the chemical industries


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and to establish industrial research laboratories [Israel, 1992]. A key figure in this movement was Thomas Edison who established his private research laboratory at Menlo Park, New Jersey in 1876 [Israel, 1998]. Although Menlo Park differed from the typical industrial research laboratory in that it was independent of any specific company, it would serve as a prototype for company-based laboratories. Like the chemical laboratories, a key characteristic of Edison’s laboratory was the use of group research. As with the chemical laboratory, this was because a lone inventor using trial and error could no longer solve the problems that arose in the new science-based industry. The electrical industries were not based on single inventions; rather they were systems of inventions. Edison’s electrical lighting system involved generators, wiring, circuits, bulbs, switches, and meters, all of which had to function together. Developing such a system was not something that could be carried out by a single inventor, but instead it required the efforts of a group of engineers, scientists and entrepreneurs [Hughes, 1983]. By the beginning of the 20th century the intense competition and consolidation that had taken place during the 1880s and 1890s resulted in the domination of the industry by a few firms, such as Western Union, American Telephone and Telegraph, General Electric, Westinghouse, the Marconi Company and National Telephone in Britain, Telefunken and Siemens in Germany, and Philips in the Netherlands [de Vries, 2005]. But many of these firms were facing an uncertain future since many of the original patents were set to expire. In order for the electrical firms to be able to develop continual innovations and to protect their market share through control of patents, they needed to establish a way to control and manage the process of invention and innovation. In response to these needs, several of the leading firms in the electrical industry established industrial research laboratories during the early years of the 20th century [Reich, 1985]. The electrical industries were influenced by the German chemical industry but there were important differences in the industrial research laboratories that arose in the electrical industries. First, the electrical laboratories took a multidisciplinary approach that involved physicists, chemists, metallurgists, mechanical engineers and electrical engineers. Second, while the goal of the chemical laboratories was to discover new products, a significant goal of the electrical laboratories was defensive. That is, much of the research went into establishing a patent position and developing patent interferences in order to give the company a dominant, or almost monopolistic, role in the market. Third, the integration of pure and applied research played an important role in the electrical laboratories. In the German chemical industry much of the fundamental research was still left to the universities, but because of the newness of scientific research in the field, the electrical industries had to carry on both pure and applied research. As such the lines between pure and applied research, and between scientists and engineers, began to disappear. This new type of industrial research laboratory first emerged in 1900 with the establishment of the General Electric Research Laboratory in Schenectady, New York [Reich, 1985]. By this time Edison had left the company, his original patents

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on the light bulb had expired and GE was facing competition from metal filament bulbs invented in Europe. From the beginning, Willis Whitney, the director of the GE laboratory who had a Ph.D. in chemistry from Leipzig, focused the laboratory on the problem of improving the light bulb [Wise, 1985]. Through the work of teams of researchers led by William Coolidge, a Ph.D. from Leipzig, and Irving Langmuir, a Ph.D. from G¨ ottingen, GE developed a new argon filled bulb with a tungsten filament that gave the company a dominant position in the market. It is impossible to classify Coolidge’s and Langmuir’s work as simple applications of science to technology. While both conducted basic scientific research, that research was always done in the context of solving practical problems so that they simultaneously created new scientific knowledge and solved practical problems. During the early 20th century a number of other companies, such as AT&T, Siemens & Haske, Philips, and Westinghouse faced the same market forces that faced GE and they responded in a similar manner by establishing industrial research laboratories [de Vries, 2005]. In these laboratories research did not follow the pure science model. Rather these laboratories developed new technological theories and design methodologies that blurred the distinctions between science and technology and might be better labeled as industry-based engineering science.

5 THE ENGINEERING SCIENCES IN THE ERA OF GOVERNMENT SPONSORED RESEARCH: 1900-1945 Overlapping the emergence of the industrial research laboratories was the establishment of government sponsored research. The interdependence of science and technology was strongly influenced by the creation of industrial research laboratories, but during the first half of the 20th century the role of government — and especially military — directed scientific and technological development would play a significant role in shaping the engineering sciences. While governments have played a role in supporting science and technology ever since the rise of the modern nation-state in the 15th and 16th centuries, what distinguished the governments’ role in the 20th century was the breadth, scale and explicit nature of such support. Also, rather than supporting science for its own sake, governments in the 20th century began to see science as a form of knowledge, similar to technology, that could be manipulated for political power. For many scholars the key factor in establishing a new relationship between knowledge and power was the role played by warfare in the 20th century, especially World Wars I and II. Unlike previous wars, these were total wars which involved much of the globe. By erasing the distinctions between civilian and the military, the total wars of the 20th century required that all elements of society, including science and technology, be harnessed as part of the war effort. The two world wars helped to shape the engineering sciences by helping to establish the roots of what President Dwight Eisenhower called the military-industrial complex (or more correctly the military-industrial-academic complex) [Leslie, 1993].


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During the 19th century the increase in population, the rise of industrialization and the invention of new weapon systems led to the potential for a new scale of warfare. In particular the late 19th century naval arms race that led to the development of steam powered, armor plated ships with large, long-range armaments showed the advantage of having links between the military and industries, such as Krupp in Germany and Elswick Ordinance Company in Britain.

5.1

Government sponsored research in World War I

Soon after World War I began it devolved into a stalemate of trench warfare and great loss of life, especially on the Western Front. In response, governments on both sides of the conflict began to consider ways to break the stalemate and they began to mobilize scientists into the war effort [Hartcup, 1988]. Chemistry played a particularly important role during World War I, which was often called the chemists’ war. The demands for substitutes for blockaded goods, new high explosives, poison gases and the means to defend against them led governments on both sides to establish new relationships between government research institutes, universities, and chemical firms. For example, in Germany Fritz Haber helped to convert the Kaiser Wilhelm Institute into the chemical warfare arm of the military and was able to call on the I.G. Farben cartel to produce the new chemical weapons discovered at the Institute. In Britain, research on chemical warfare was conducted at Imperial College and the chemical firm of Castner-Keller began production of poison gas for the military. In America, the National Research Council established a laboratory at American University in Washington, D.C. to conduct research on chemical warfare. By 1918 President Woodrow Wilson established the Chemical Warfare Service in order to conduct chemical research for military needs and to build factories to produce those chemicals. Although World War I was known as the chemists’ war, governments and the military also encouraged research into other areas of science and technology. Wireless communications played a significant role in World War I, especially in naval operations. The Marconi Company provided equipment for the British Royal Navy as did the Telefunken Company for the German Navy. The American government encouraged research programs into wireless communications and protected companies, such as General Electric, Western Electric and AT&T from patent infringement litigation so that they could spend more money on research and development. Concern over U-boat attacks in the Atlantic led the American Naval Consulting Board to establish an experimental research station which brought together distinguished scientists, such as Irving Langmuir, with companies, such as GE and AT&T, to conduct research on submarine detection. Even before the war governments and the military encouraged research into aeronautics because of the potential use of airplanes as tools of war. Soon after a demonstration flight by the Wright Brothers in 1909 the British government established an Advisory Committee on Aeronautics and the National Physical Laboratory (NPL) built a wind tunnel in order to conduct aeronautical research.

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On the Continent significant work was being done on the theory of aeronautics [Anderson, 1997]. Nikolai Jouwski in Russia and Wilhelm Kutta in Germany developed a new theory of lift, while Ludwig Prandtl in Germany revolutionized the study of drag with his boundary layer theory. At the same time in France Gustave Eiffel began a series of experimental tests on lift using a wind tunnel. In the United States, Congress established the National Advisory Committee for Aeronautics (NACA) which contracted with William Durand and Everett Lesley at Stanford University to conduct wind tunnel tests on airfoils and propellers using parameter variation [Vincenti, 1990, pp. 137-169]. With the exception of Anthony Fokker’s use of Prandtl’s work to design aircraft for the Germans, aeronautical research played little role in aircraft design during World War I, but it would play a much larger role in revolutionizing aircraft design during the inter-war period. Britain’s NPL and NACA’s newly completed Langley Laboratory conducted new experimental tests that led to the streamlining of airplanes. By the 1930s NACA built a large scale wind tunnel which it used to design new cowlings for engines and a new series of airfoils [Anderson, 1997]. The new improvements in aircraft design and advances in aerodynamic theory led some individuals in Britain and Germany to contemplate attaining high speed, high altitude flight [Constant, 1980]. This led Frank Whittle in England and Hans von Ohain and Ernst Heinkel in Germany to begin work on turbojet engines. Much of this work was done with the support of the Aeronautical Research Council in Britain and the Air Ministry in Germany.

5.2 Government sponsored research in World War II While governments began to encourage and direct interactions between science and technology during World War I, governmental and military sponsored research began to take off during World War II [Hartcup, 2000]. While chemical warfare, wireless communications and airplanes had not proved decisive in World War I, a number of officials began to recognize that the outcome of World War II might hinge on weapon systems not yet even invented and therefore there needed to be a way to harness science, technology and industry for military needs. Because most of Europe was under the threat of aerial bombardment, much of the most successful wartime research was conducted in the United States. In 1940, even before America entered the war, a group of academic and industrial scientists and engineers convinced President Franklin Roosevelt to establish the National Defense Research Committee (NDRC) in order to direct wartime research. A year later the government established a broader organization, the Office of Scientific Research and Development (OSRD), to oversee the NDRC and medical research, and to actually develop that research into the production of new weapons. Since time was of the essence, the NDRC decided not to establish its own laboratories but to contract with universities and industry for the use of their laboratories and staff. This new organizational structure linking universities, industry and the


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military produced a large number of weapon systems that helped the Allies win the war — the most important of which were radar and the atomic bomb. It is often said that while the atomic bomb ended the war it was radar that won the war [Brown, 1999; Buderi,1996]. Although some early work on radar had been done in Germany and America during the 1920s, most historians give credit for the invention of radar to Robert Watson-Watt, head of the NPL’s Radio Research Station, whose work led to the first large-scale defensive radar network. Watson-Watt recognized that pulsed signals and short wavelengths were the key to a practical radar system. While British radar helped to win the Battle of Britain, traditional vacuum tube technology limited the development of smaller microwave radar which could be carried on planes and ships for offensive purposes. Work in England at the Clarendon Laboratory at Oxford and at the General Electric Laboratory in Wembley led to the development of the cavity magnetron in 1940 which could produce microwaves. Because of the difficulty producing the devices under wartime conditions in Britain, the new radar technology was shared with the Americans. Soon after, the Microwave Committee of the NDRC established a central Radiation Laboratory (Rad Lab) at M.I.T. With a staff that grew to more than four thousand, annual budget of $43 million and links with Bell Labs, Raytheon Corporation, Western Electric, General Electric and Westinghouse, the Rad Lab developed airborne radar systems that could detect other planes and Uboats and the laboratory created the Long Range Aid to Navigation (LORAN). At the same time, Section T of the NDRC, working with the Sylvania Corporation, developed and produced the proximity fuse which was a miniaturized radar system that could fit inside an anti-aircraft shell, which proved crucial in shooting down planes in the Pacific. All of this work brought together academic and industrial researchers in a way that blurred the distinctions between science and technology and would be better classified as engineering science. One of the most important developments that emerged from the alliance between the military, universities and industry was the atomic bomb [Rhodes, 1986]. Again most of the initial research occurred in Europe, beginning with the discovery of fission by Otto Hahn and Fritz Strassmann in Berlin in late 1938. A key step in developing an atomic bomb was work done by Otto Frisch and Rudolf Peierls in 1940 at the University of Birmingham in England. They recognized that if the isotope Uranium-235 could be separated from the more common Uranium-238 the critical mass for a nuclear chain reaction would be in the order of pounds rather than tons and the material could be used to produce a bomb. Other researchers in America indicated that Plutonium-239, a material that could be produced by bombarding Uranium-238 with neutrons, might also make a suitable material for a bomb. Again wartime conditions in Britain made it unlikely that the necessary research and development could be done there so the British shared their research with the Americans. Between 1940 and 1942 much of the research for the atomic bomb was done under contract from the NDRC at Columbia University, the Metallurgical Laboratory (Met Lab) at the University of Chicago, Iowa State University and the

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University of California at Berkeley. This research culminated in 1942 with the first controlled nuclear chain reaction at the Met Lab under the direction of Enrico Fermi. The nuclear reactor served both as a test-bed for a uranium atomic bomb and as a means by which material could be produced for a plutonium bomb. Shortly before the success of the Chicago reactor the OSRD decided that the Army Corps of Engineers was better suited to oversee the combination of research in industrial production that would be needed to actually create an atomic bomb. The newly created Manhattan Engineering District (or Manhattan Project) established three major research and industrial facilities: one at Oak Ridge, Tennessee to separate U-235 through electromagnetic separation or gaseous diffusion; a second at Hanford, Washington to produce plutonium using large-scale nuclear reactors; and a third at Los Alamos, New Mexico to design the two types of bombs. These facilities brought together academic and industrial researchers. The separation projects at Oak Ridge were run by Tennessee Eastman and Union Carbide based on research done at the University of California at Berkeley, and the Hanford facility was run by Du Pont based on research done at the University of Chicago. Again, the governmental/military directed research that led to the development of the atomic bomb transcended traditional distinctions between basic and applied research, or between science and technology and is better classified as engineering science. 6

THE ENGINEERING SCIENCES IN THE ERA OF TECHNOSCIENCE: 1945-2000

The Allied victory in World War II was also seen as a victory of the concept of the military-industrial-academic complex that produced the winning weapons. When the Cold War soon followed the end of World War II this led to a continuation, and even a dramatic increase of governmental support for science and technology. During the Cold War a number of new governmental agencies were established to support and direct scientific and technological research [Geiger, 1992]. In the United States the Navy established the Office of Naval Research (ONR), the Air Force founded the RAND Corporation, and the Congress created the National Science Foundation (NSF), the National Aeronautics and Space Administration (NASA) and the Atomic Energy Commission (AEC). In this period, these agencies distributed millions of dollars to industry and universities, especially for doctoral research. In doing so, the government helped shape the direction of research in science and technology by focusing funding on research into nuclear weapons, solid state electronics, rocketry, computer science, biotechnology and nanotechnology. At the same time Europe followed a similar path with the establishment of the European Center for Nuclear Research (CERN), the European Space Agency, and the French National Center for Scientific Research (CNRS). The Cold War governmental agencies helped to reshape the relationship between science and technology in such a way that it would become difficult to make meaningful distinctions between the two areas. In fact, a number of the new fields


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of research, such as solid state electronics, computer science and biotechnology, contained one term drawn from science and one from technology [Channell, 1991]. World War II demonstrated that very little research could be considered “pure.” Something as esoteric and seemingly unpractical as nuclear physics led to a weapon that would contribute to the end of World War II and come to define the Cold War. Philosopher Stephen Toulmin has noted that after World War II the basic focus of scientific research was no longer nature itself; rather the focus became some unit of technology, such as a reactor, missile, or computer [Capshew and Rader, 1992, p. 9]. This elimination of any real distinction between science and technology has led to the emergence of the concept of a single integrated realm of knowledge that some have labeled technoscience [Latour, 1987]. In such a realm technology and science are no longer simply dependent upon one another; rather they are interdependent so that technology cannot exist without science and science cannot exist without technology [Sassower, 1995, pp. 4, 24](for an argument that distinctions still exist in a conceptual sense between technology and theoretical science, see [Radder, this volume, Part I]).

6.1 Nuclear weapons One of the first areas to reflect the new concept of technoscience was the postwar development of nuclear weapons and nuclear power [Rhodes, 1986]. The success of the atomic bomb in contributing to the end of World War II and the new tensions of the Cold War led the United States and the Soviet Union to focus on developing more efficient and more powerful nuclear weapons. By the 1950s both countries, using academic, industrial and governmental supported research, had developed the more powerful H-bomb. About the same time new research began on nuclear reactors, especially in the United States. Much of the early research, supported by the AEC, was aimed at breeder reactors that could produce plutonium for weapons, or at compact reactors to power submarines, but by the mid-1950s research was also being done to produce commercial power reactors in response to President Eisenhower’s Atoms for Peace program. In all of this work the distinction between science and technology all but disappeared. Stuart W. Leslie has argued that this new research transformed the nature of physics into something closer to technology [Leslie, 1993, p. 133]. For example, in 1946 Harvard University changed its Department of Engineering Science into the Department of Engineering Science and Applied Physics and shortly after Cornell established a Department of Engineering Physics. Technology began to change nuclear physics in other ways. New experimental equipment, such as particle accelerators emerged from wartime microwave research and new detectors, such as spark and bubble chambers arose from technology originally developed for nuclear weapons or to detect missiles. This dependence upon technology began to influence the development of theories in nuclear physics. Peter Galison has argued that this complicated technology led laboratories to rely on “in house” theorists who developed theories more directly related to a

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specific machine being used in an experiment and outside theorists who developed theories that could only be tested on some specific machine [Galison, 1997, p. 43]. In addition these machines were so large, complex and expensive that the experiments conducted on them could only be managed by teams of researchers at national or even international laboratories.

6.2 The space race Closely related to the work on nuclear weapons was the development of missiles to deliver them. The modern idea of a guided ballistic missile began to emerge in the late 19th and early 20th centuries with the work of the Russians Konstantin Tsiolkovsky and Sergei Korolev and the German Hermann Oberth and the American Robert H. Goddard [Von Braun and Ordway, 1976]. But it was the successful development of the V-2 rocket by the Nazis during World War II that stimulated the postwar development of ballistic missiles and the space race between the United States and the Soviet Union [Neufeld, 1995]. Development of missiles provided the means to deliver nuclear weapons and the ability to launch satellites which could be used to improve communications and to spy on the other side [McDougall, 1985]. In addition the peaceful and scientific exploration of space often functioned as a surrogate for the arms race between the Americans and the Soviets. In both the United States and the Soviet Union the space race brought together government, military, academic and industrial research in such a way that it blurred any distinction between science and technology [Bromberg, 1999]. As with nuclear studies, the space program changed the nature of science, particularly planetary science and astronomy. New planetary probes and space telescopes required an interdisciplinary team of astronomers, physicists, aeronautical engineers, mechanical engineers, electrical engineers and computer scientists. Most of the equipment had to be built by private industry and required government funding. As such the actual research had to be managed by national or international laboratories.

6.3 Solid state electronics A third area of research and development that reflected the new concept of technoscience was the field of solid state electronics [Riordan and Hoddeson, 1997]. During the 1920s and 1930s physicists began to apply quantum mechanics to solid state materials and began to develop theories about how electrons behave in a newly discovered class of materials called semiconductors. Just before the beginning of World War II, Bell Labs began fundamental research on the solid state but the research was put aside during the war in favor of work on radar. Wartime work on radar led to a new understanding of the properties of semiconductors, and with the end of the war, Bell returned to research on semiconductors in hopes of finding a solid state version of the vacuum tube triode. Soon after the war Bell Labs established an interdisciplinary team of researchers, led by William Shockley, a theoretical physicist, Walter Brattain, an experimental physicist, and John


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Bardeen, a theoretical physicist who also had an M.S. in electrical engineering. Combining theory and experiment Brattain and Bardeen demonstrated the first point-contact transistor in December of 1947, and one month later Shockley had the idea for a junction transistor, which would ultimately replace the point-contact transistor. The U.S. military was a major force behind the development of the transistor. The Army Signal Corps was particularly interested in miniaturizing communication devices and the military became a major consumer of transistors at a time when their high cost limited civilian applications. It was also the military that pushed the electronics industry to switch from germanium to silicon, which was more suitable for use in guided missiles and nuclear powered ships. The military also encouraged the dissemination of knowledge about the transistor to industry and universities. With the military market for transistors declining after the end of the Korean War, new civilian markets began to emerge, such as hearing aids and radios. Many of the new civilian applications of transistors originated in Japan which had been forbidden to have a military and therefore sought other applications for transistors. By the second half of the 1950s Shockley left Bell and created a new company in the Stanford Industrial Park in California which had been created to encourage cooperation between the university and private industry. This would become the beginning of what came to be known as Silicon Valley. The success of new markets for transistors led to improvements in the manufacture of transistor circuits. This culminated in the independent invention of the integrated circuit in 1959 by Jack Kilby at Texas Instruments and by Robert Noyce at Fairchild Semiconductor in California.

6.4

Computers and computer science

One of the most significant applications of transistors and integrated circuits was in the computer. Although the idea of a general purpose computer goes back to the first half of the 19th century when Charles Babbage in England conceived of his Analytical Engine, it was not until World War II that the British and the Americans built actual specialized calculating devices [Goldstine, 1972]. In England a team at Bletchley Park under Alan Turing built a machine called Colossus which was designed to help break the German codes. Also during the war Howard Aitken at Harvard and a team at IBM developed the Mark I, which was used to do ballistic tables and to solve problems arising from the development of the atomic bomb. These early computers were limited by the fact that they were electromechanical devices and designed for specific applications. After the war several breakthroughs led to the creation of the general application electronic computer [Ceruzzi, 2003]. Many of the early improvements were helped by wartime research on radar. The first improvement was the Electronic Numerical Integrator and Computer (ENIAC), the first all electronic computer, designed and built for the U.S. Army by John Eckert and John Mauchy in 1945 at

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the University of Pennsylvania. This was followed by the Electronic Delay Storage Automatic Computer (EDSAC), the first full-scale stored program computer that was built in 1949 by Maurice Wilkes at the University of Cambridge, based on a design by John von Neumann. The modern electronic general purpose computer arose with the further improvements brought about by the invention of the random access magnetic core memory by Jay Forrester in 1949 at M.I.T.’s Servomechanisms Laboratory, and Philco’s development of SOLO in 1957, one of the first all transistor computers, and finally by the use of integrated circuits by several firms in the 1960s which resulted in the development of the minicomputer. While the modern computer was the result of a combination of science and technology that could be labeled technoscience, its development also led to the creation of a new technoscience that became known as computer science. At the same time that core memories, transistors and integrated circuits were transforming computer hardware, significant changes were taking place in the development of computer software. A breakthrough came with the realization that computers essentially manipulate symbols and that computer instructions themselves could be “coded” into the machines where they could execute commands [CampbellKelly and Aspray, 1996]. By the late 1950s higher level programming languages, such as FORTRAN and COBOL, began to be developed. In addition researchers began creating operating systems that controlled how computers scheduled tasks when more than one program was running. The combination of developments in hardware and software led to the emergence of what became known in North America as computer science. In 1967 Herbert Simon, Alan Perlis and Allan Newell at Carnegie Institute of Technology in Pittsburgh, argued that computer science was the study of computers in the same way that astronomy was the study of stars, except that computers were humanly constructed artifacts. As such computer science was a “science of the artificial” [Simon, 1969]. By 1968 many in the field of computer science were moving away from focusing on the computer itself and turning toward the notion of computation as the real focus of a science of computing. This led to the study of algorithms by Donald Knuth and others, and in 1968 the Association for Computing Machinery recommended a new curriculum for computer science which replaced courses on computer hardware with courses on logical design, switching theory and algorithms. This new focus on computation further blurred the distinctions between science and technology since computation could be seen as either a human construction, and therefore technological, or as a branch of mathematics, and therefore a foundation of the sciences. By the end of the 20th century the idea of computation was being used to model both physical and biological phenomena, including human intelligence. Work done by Norbert Wiener and Julian Bigelow on anti-aircraft guns during World War II led to the development of mathematical theories of control and feedback in machines, which became the basis of the new field of cybernetics. Beginning in 1950 Alan Turing, influenced by Wiener, proposed the idea that a computer could exhibit intelligent behavior, which helped to create the field of artificial intelligence.


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Near the end of the century physicists and biologists began to model natural phenomena using computers which led to the solution of many unsolved problems. The success of computational physics and biology led some researchers to argue that computation was not simply a methodology for solving scientific problems but that the universe itself was essentially computational and could be best understood in terms of information processing. This of course further blurred the distinctions between science and technology with science being seen more as applied technology [Channell, 2004; Forman, 2007].

6.5

Material science: lasers, superconductivity and nanotechnology

An area also closely associated with solid state electronics but which extended beyond it was material science. The application of quantum mechanics to solids during the 1920s and 1930s led to new knowledge of the relationship between atomic structure and the overall properties of materials. This opened the possibility of designing materials with a given set of properties. After Sputnik the U.S. Department of Defense’s Advanced Research Project Agency (DARPA) became interested in developing materials that could function in the extreme environments of the missile and space programs [Leslie, 1993, pp. 218-219]. Through DARPA the U.S. government funded a number of interdisciplinary materials research laboratories at major universities and funded research into new analytical techniques, such as electron microscopy, X-ray diffraction, and nuclear magnetic resonance. One of the most significant achievements of material science was the development of the laser [Bromberg, 1991; Hecht, 2005]. Like the transistor, the laser had roots in both electronics and material science. After World War II the military became interested in generating radio waves in millimeter range which would be difficult to intercept. During the late 1940s the Army, Navy and Air Force created laboratories at Columbia, M.I.T., Stanford, and Harvard to work on the problem. At Columbia Charles Townes discovered that microwave radiation could be amplified by stimulating electrons to a higher state. By 1954 Townes and his graduate student James Gordon produced the maser which produced amplified microwaves by stimulated emission of radiation in ammonia gas. The maser was independently invented by Nikolai Brasov and Alexander Prochorov in the Soviet Union. During the second half of the 1950s a number of researchers, including Townes, used an idea of Joseph Weber to create a solid state maser using ruby crystals, and by 1957 Townes and R. Gordon Gould at Columbia and Arthur Schawlow at Bell Labs independently began work on the laser which would amplify light by stimulated emission of radiation. Although Bell Labs was eventually granted the patent, all three are usually given credit with the invention of the laser, but it was not until 1959-1960 that Theodore Maiman of the Hughes Laboratory produced the first working laser. Another area of research in material science that reflected technoscience was the field of superconductivity, especially high temperature superconductivity [Matricon and Waysand, 2003; Nowotny and Felt, 1997]. The phenomenon of super-

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conductivity had been discovered at the beginning of the 20th century by Heike Kamerlingh Onnes at the University of Leyden, but the phenomenon only seemed to occur at temperatures below 23 K and could not be explained by classical physics. It was not until 1957 that John Bardeen, Leon Cooper and J.R. Schrieffer developed the BCS theory which used quantum mechanics to explain one type of superconductivity. Although the theory had limited practical applications it did explain the phenomenon of tunneling which led Gerd Binnig and Heinrich Rohrer at IBM’s Zurich Research Laboratory to invent the scanning tunneling microscope in 1985 [Rasmussen, 1997]. Some of the most significant advances in superconductivity came from experimental research. By 1986 Alex M¨ uller and Georg Bednorz at IBM’s Zurich Research Laboratory discovered a ceramic oxide that was superconducting at 35 K. Within a few months Shoji Tanaka, at the University of Tokyo, M.K. Wu, at the University of Alabama-Huntsville, and Paul Chu, at the University of Houston, discovered other materials that were superconducting at 98 K, which could be attained with inexpensive liquid nitrogen rather than the more expensive liquid helium. While the development of high temperature superconductors represents another blurring of science and technology it may also represent a new phase of technoscience. In contrast to the earlier big science projects, most of this work was the result of small laboratories with teams of one or two individuals. But the work does not represent a return to traditional science. The necessity to empirically consider almost an infinite number of materials led to the establishment of alliances and networks of researchers that Michel Callon has labeled the “extended lab” [Nowotny and Felt, 1997, p. 5]. One of the most recent developments in material science is the emergence of nanotechnology [Regis, 1995]. Some historians trace the origin of the idea of nanotechnology to a talk by physicist Richard Feynman in 1959 in which he argued that miniaturization in technology could be pushed much further and he suggested designing a linked series of machines, each smaller that the other, in which the smallest could be capable of manipulating individual atoms. About the same time Arthur von Hippel, an engineer at M.I.T. suggested that new materials could be custom designed by fabricating them one atom at a time. But it would not be until the 1980s that any significant work in the field would begin. It was then that K. Eric Drexler, a graduate student at M.I.T. suggested using genetic engineering techniques to manipulate proteins in order to produce the equivalent of small machines [Drexler, 1987]. In the mid-1980s Carl Pabo, Jay Ponder and Frederic Richards at Yale “inverted” the usual biological approach and were able to find a sequence of amino acids that would produce a protein with a given shape. At the same time that protein engineering was showing some potential to design nanostructures, Binnig and Rohrer, inventors of the scanning tunneling microscope, discovered that if the tip of the instrument got too close to the surface of a sample an atom would stick to the tip and could be moved [Rasmussen, 1997]. Shortly after, groups at Bell Labs and the IBM Almaden Research Center in San Jose, California created a device that could move atoms to specific locations. Other


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researchers at M.I.T. and AT&T used techniques originally developed to manufacture integrated circuits and produced micron-sized machines by etching away silicon from a chip. Although none of these techniques produced any truly practical devices, in 1991 the Japanese Ministry of International Trade Industry (MITI) provided $200 million for the development of nanotechnology. The next year the U.S. Senate Subcommittee on Science, Technology and Space, chaired by Albert Gore, began hearings which led to the National Nanotechnology Initiative (NNI), signed into law in 2000, which would provide billions of dollars for nanotechnology research. Ironically much of the government-funded research followed a different path than that advocated by Feynman or Drexler. Instead of focusing on assembling materials one atom at a time, researchers turned to more traditional chemical techniques. This approach led Richard Smalley, Robert Curl and Harold Kroto at Rice University in Houston to discover a new arrangement of carbon atoms that was similar to the geodesic domes designed by Buckminster Fuller. These new fullerenes, or buckyballs, had a number of interesting applications, such as cagelike structures to deliver medicine to certain parts of the body. By 1991 Sumio Iijima, a researcher at NEC in Japan discovered that fullerenes could exist as long tubes, labeled nanotubes. Such tubes held the promise of use in high voltage electric lines or as textile-like materials that would be stronger than Kevlar. Although still just emerging at the end of the 20th century, nanotechnology reflects many of the characteristics of technoscience. Research into nanotechnology cannot be easily divided into science versus technology or basic versus applied. Work aimed at developing practical nanotechnology has often resulted in new basic scientific discoveries, such as the first artificial proteins or fullerenes. At the same time, basic research, such as work on protein folding or the search for new fullerenes is always done with one eye on practical applications. But, like superconductivity, nanotechnology may represent a new phase of technoscience. While it has some military applications, most of the funding has come from nonmilitary government agencies, such as MITI or NNI. Also, while billions of dollars have been allocated for nanotechnology research, that funding has been divided among a number of projects which has led to alliances and networks that may also reflect Callon’s concept of the “extended lab” [Nowotny and Felt, 1997, p. 5].

6.6

Biotechnology

An area connected to computer science and to material science that reflects the notion of technoscience is biotechnology [Judson, 1979; Cherfas, 1982]. Robert Bud has argued that the modern conception of biotechnology emerged out of the industrialization of fermentation during the late 19th and early 20th centuries, which was used to supply Germany with animal fodder and ingredients for explosives during World War I [Bud, 1993]. But the real advancement of biotechnology took place after World War II when in 1953 Francis Crick and James Watson determined the structure of DNA [Watson, 2003]. The discovery of the structure of DNA led

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to research into the way DNA carried genetic information [Kay, 2000]. Using an idea put forward by Erwin Schr¨ odinger in 1943 that genes store information as a code-script, researchers began to apply World War II code-breaking techniques and computers to the problem of deciphering the genetic code. Although the crypto-analytic technique came close to breaking the code, the problem of the way in which DNA functioned was ultimately solved by more traditional techniques in 1961 by Marshall Nirenberg and Heinrich Matthaei at the National Institutes of Health in Washington, D.C., but researchers still often thought of DNA in terms of codes and information. The breaking of the genetic code opened up a new range of possibilities for biotechnology. With the combination of Stanley Cohen’s discovery in 1971 of the use of plasmids to “infect” E. coli with new genetic properties, and the discovery in 1967 of enzymes, labeled ligase, which could glue strands of DNA, and the discovery in 1970 of restriction enzymes, which cut strands of DNA, the elements to manipulate DNA were in place. By 1973 Cohen and Herbert Boyer, working in the San Francisco Bay area, combined two plasmids, each conferring resistance to a different antibiotic, and created a plasmid with recombinant DNA that conferred resistance to both antibiotics. Boyer and Cohen’s success led to the founding of Genentech in 1976 which became the first biotechnology company based on genetic engineering. This was soon followed by a large number of new companies such as Biogen and Amgen. Since many of these companies were founded by university researchers a number of universities soon began to create laboratories and institutes whose specific purpose was to create new commercial biotech products and processes. This further blurred the lines between industrial and academic research and between pure and applied science. The potential of biotechnology led to a proposal in 1985 by Robert Sinsheimer of the University of California at Santa Cruz to determine the genetic code of the human genome. The plan was made possible by the development during the late 1970s of gene sequencing techniques by Walter Gilbert at Harvard and Fred Sanger at Cambridge. By 1988, with funding from the U.S. Department of Energy and the National Institutes of Health, the Human Genome Project was established. Although the project was conceived in the United States, a significant amount of work was done in Great Britain, France, Germany and Japan. Debates over sequencing technologies led to the establishment of a competing private commercial program to sequence the human genome at Celera Genomics headed by Craig Venter. By 2000 both the government program which primarily funded academic science and the commercially driven program had led to a “rough draft” of the human genome. As a result of this success not only have the distinctions between pure and applied research and between science and technology again become blurred, but so have the distinctions between the organic and the technological [Channell, 1991].


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[Rasmussen, 1997] N. Rasmussen. Picture Control: The Electron Microscope and the Transformation of Biology in America, 1940-1960. Stanford University Press, 1997. [Regis, 1995] E. Regis. Nano: The Emerging Science of Nanotechnology. Little, Brown and Company, 1995. [Reich, 1985] L. Reich. The Making of American Industrial Research: Science and Business at GE and Bell, 1876-1926. Cambridge University Press, 1985. [Reynolds, 1983] T. Reynolds. Stronger than a Hundred Men: A History of the Vertical Water Wheel. Johns Hopkins University Press, 1983. [Reti, 1974] L. Reti, ed. The Unknown Leonardo. McGraw-Hill, 1974. [Riordan and Hoddeson, 1997] M. Riordan and L. Hoddeson. Crystal Fire: The Birth of the Information Age. W.W. Norton, 1997. [Rhodes, 1986] R. Rhodes. The Making of the Atomic Bomb. Simon and Schuster, 1986. [Rossi, 1970] P. Rossi. Philosophy, Technology and the Arts in the Early Modern Era. Harper and Row, 1970. [Rouse and Ince, 1957] H. Rouse and S. Ince. History of Hydraulics. Iowa Institute of Hydraulic Research, 1957. [Sassower, 1995] R. Sassower. Cultural Collisions: Postmodern Technoscience. Routledge, 1995. [Schofield, 1963] R. Schofield. The Lunar Society of Birmingham. Oxford University Press, 1963. [Seely, 1993] B. Seely. “Research, Engineering, and Science in American Engineering Colleges: 1900-1960.” Technology and Culture, 34, 344-386, 1993. ´ [Shinn, 1980] T. Shinn. Savoir Scientifique et Pouvoir Social: L’Ecole Polytechnique, 17941914. Foundation National des Sciences Politiques, 1980. [Simon, 1969] H. Simon. The Sciences of the Artificial. MIT Press, 1969. [Smith, 1998] C. Smith. The Science of Energy: A Cultural History of Energy Physics in Victorian Britain. University of Chicago Press, 1998. [Steele, 1997] B. Steele. Muskets, and Pendulums: Benjamin Robins, Leonhard Euler, and the Ballistics Revolution. In Technology and the West, T. Reynolds and S. Cutcliffe, eds., pp. 145-180. University of Chicago Press, 1997. [Stewart, 1992] L. Stewart. The Rise of Public Science: Rhetoric, Technology, and Natural Philosophy in Newtonian Britain, 1660-1750. Cambridge University Press, 1992. [Taylor, 1991] J. Taylor. The “Didascalicon” of Hugh of St. Victor. Columbia University Press, 1991. [Timoshenko, 1953] S. Timoshenko. History of Strength of Materials. McGraw-Hill, 1953. [Todhunter, 1886–1893] I. Todhunter. A History of the Theory of Elasticity and the Strength of Materials from Galileo to the Present, 2 vols. Cambridge University Press, 1886-1893. [Vincenti, 1990] W. Vincenti. What Engineers Know and How They Know It. Johns Hopkins University Press, 1990. [Vincenti and Rosenberg, 1978] W. Vincenti and N. Rosenberg. The Britannia Bridge: The Generation and Diffusion of Technological Knowledge. MIT Press, 1978. [Von Braun and Ordway, 1976] W. Von Braun and F. Ordway. The Rockets’ Red Glare. Anchor Press, 1976. [Watson, 2003] J. Watson. DNA: The Secret of Life. Alfred A. Knopf, 2003. [Weiss, 1982] J. Weiss. The Making of Technological Man: The Social Origins of French Engineering Education. MIT Press, 1982. [White, 1978] L. White, Jr. Medieval Religion and Technology. University of California Press, 1978. [Wise, 1985] G. Wise. Willis R. Whitney and the Origins of U.S. Industrial Research. Columbia University Press, 1985. [Yates, 1979] F. Yates. Giordano Bruno and the Hermetic Tradition. University of Chicago Press, 1979.

COHERENCE AND DIVERSITY IN THE ENGINEERING SCIENCES Gerhard Banse and Armin Grunwald

1

INTRODUCTION

The philosophy of technology has devoted relatively little attention to the engineering sciences. The central focus has been primarily upon the concept of technology, the facets of technical action, the relationship between humans and technology or between technology and society [Ferré, 1995; Mitcham, 1994], and the relationship between technology and art or between technology and nature (see for example most contributions in [Lenk and Maring, 2001] and in [Mitcham and Mackey, 1983]). By contrast, the theory of science has concerned itself much more predominantly with the classical scientific disciplines — particularly theoretical physics — than with the engineering sciences, despite the fascinating and even specific relationship demonstrated by the latter between theory and practice. Although technical practices are an inextricable aspect of the cultural history of mankind, the engineering sciences emerged at a relatively late stage as a consequence of the industrial revolution and became institutionally anchored in response to the societal need for technical knowledge to be made systematic, for engineers to be well-educated and for technical knowledge and know-how to continue to expand. The coherence and diversity of the engineering sciences cannot therefore be considered in isolation of this history. It is precisely in this relationship between theory and practice that these sciences manifest themselves. Further clarification of this relationship is a task shared by the philosophy of technology and the theory of science. At first sight, the engineering sciences appear to be a conglomeration of a wide variety of scientific disciplines the only apparent common ground being their affiliation to engineering and technology, to technical systems and the way in which they are developed and applied. The systems themselves do, indeed, represent enormous diversity. Upon closer examination uniform features and unifying elements do, however, crystallise within this diversity. It is the aim of this chapter to make these common features clear. To this end, we shall first define the specific nature of engineering sciences and examine their development. This will enable us to lend substance to the coherence and multiplicity of engineering sciences whilst simultaneously taking their historical development into account. This introduction (Section 2) thus provides Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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information on both the perspective from which technological sciences is viewed in this chapter and the options open to us for doing justice to their diversity. Having chosen to view the coherence and diversity of engineering sciences in relation to the specific relationship between theory and practice, it seems only logical to follow through this perspective both methodologically (Section 3) and substantively (Section 4). Not surprisingly, in terms of methodology, we will primarily be forced to deal with the coherence of engineering sciences, for instance, in engineering design, while in terms of substance the focus will be on classification approaches and thus on diversity. In addition to these considerations, we will finally present our “general technology” considerations. That is where we consider at a conceptual level the relationships of coherence and diversity discussed so far (Section 5). 2

COHERENCE AND DIVERSITY — THE SPECIFIC NATURE AND DEVELOPMENT OF THE ENGINEERING SCIENCES

2.1 The specific nature of engineering sciences The collective term “engineering sciences” is used to group together all the scientific disciplines that pertain to technology, mainly in the form of technological systems (“artefacts”), materials, work procedures and technological processes (see also Mitcham and Schatzberg’s chapter in this Volume, Part I). These sciences have to fulfil the following dual function: • First, it is particularly the technical characteristics of existing technical systems that are recorded by observation or measurement and are then analysed empirically and theoretically. These results are presented in terms of both the natural sciences and the engineering sciences and, where possible, are justified mathematically and generalised. This forms the basis for potentially improving technical systems, for example in terms of functionality, effectiveness, user-friendliness, potential applications, reliability and safety, or for better controlling their application. • Secondly, new technical objects and technological procedures (or changes to existing procedures) are anticipated using approaches guided by methodology that is based on theoretical knowledge and inferences as well as existing practical experience. They are evaluated according to external requirements and are drafted (design, engineering design). Two things thus become apparent: (a) technology cannot be reduced to its concrete realizations because it also encompasses the human actions behind technical construction as well as the technological production processes which are based on the use of technical systems and serve to create things of value. (b) The engineering sciences domain does not only incorporate already existing and functioning technical systems but also those not yet created, those which are still to be intellectually anticipated, designed and projected.

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In this context, the engineering sciences deal with specific combinations of possible technical options dictated by the laws of nature and by social objectives, requirements, constraints and needs. These specific combinations are subject to the influence of what is possible in terms of laws of nature, what is technically and technologically realisable, economically feasible, ecologically sensible, socially desirable and enforceable, and humanly acceptable. They can be recognised by the characteristic structures of technical systems and the corresponding external1 functions which, although ultimately enabled by laws of nature, cannot be sufficiently explained within the framework of natural scientific knowledge. Technical characteristics or technical properties have to be characterised both with respect to nature and the field of human activity. “They reflect the objects’ ability to carry out this or that technical function” [Tscheschew and Wolossewitsch, 1980, p. 163]. They express the dialectics of nature and society, of the natural and social components that are characteristic of engineering and technology. These technical characteristics are first and foremost the object of engineering sciences which also set out to investigate the relations between components of the laws of nature (or combinations of these components) and the technical conditions of their realisation or effect. At the forefront of scientific activity in engineering sciences is the systematic anticipation and complex evaluation of the structure and function of technical systems, of the pathways to their realisation and of the procedures underlying their application. It is the process of the intellectual anticipation of the new, that is, of what does not yet exist, or not in a given form. The results include possible solutions to technical tasks and problems, technical ideas, innovations, developments and inventions. Engineering sciences as a whole is equally concerned with cognition and practice. The cognitive goals are to create new knowledge, for instance, in the form of functional and structural rules, technological laws or ecological-sociotechnological systems. The practical goals are to anticipate technology, for example in the form of new or improved technical systems, human-technological interaction or socio-technical structures. This not only defines the practical objective of work in engineering sciences, but it also makes clear that technical systems are created by “thinking, planning and constructing” and that the prerequisite of the “intended purpose is practical effectiveness” [Spur, 1998b, pp. 1ff.; present authors’ emphasis]. By being linked to practical realisation, engineering sciences become what the German physicist Hans-Peter D¨ urr termed “machinations” or “sciences urr, 1988, p. 172, 179].2 In this way more emphasis comes to be of making” [D¨ placed on both the “madeness” of technical systems and the work of humans and thus on human-technical interaction in the production and use of these systems (sociotechnical systems within a cultural “frame”). Engineering sciences are thus 1 As distinct from the “internal” functions which can be explained purely in terms of the natural sciences. 2 In contrast to our own interpretations, D¨ urr saw the creation of technology by engineers and technical scientists as primarily a matter of craftsmanship without any scientific basis.

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sciences of action. They are concerned with technical artefacts, processes, methods of action and procedures and they are designed to support human action in many ways by means of technology or by providing the knowledge and know-how for technology. The specific scientific concern with technical action serves the systematic investigation of the conditions of successful action and the extension of the possibilities for action. Technology and engineering sciences pursue three kinds of targets on this abstract level (see [K¨ onig, 2006]): 1. Practical goals: Engineering sciences should support and want to support and improve technical practice with regard to the usability, economic viability, efficiency, safety, functionality, and so on, of technical procedures, products or systems. The central criterion of success for engineering sciences is their usefulness to society through the supporting of technical practice and the educating of engineers. With regard to their practical aims, the engineering sciences demonstrate a high degree of social relevance. 2. Cognitive Goals: Technical knowledge, in other words, knowledge of technical contexts and procedures, of material processing and exploitable physical or chemical processes should be produced, stabilised, systematised and improved. The central criterion for success here is the truth of the relevant knowledge in conjunction with the achieving of recognition within and outside the sciences. The central concept is therefore scientific excellence. 3. Professional Goals: The scientific community affiliated to these technical disciplines (e.g. of mechanical or civil engineering) should be consolidated through scientific journals, societies, congresses and scientific meetings, through the stabilisation and expansion of the knowledge system and through further specialisation and differentiation. The duality of practical and cognitive goals as found in the first two kinds of targets is crucial to our further discussion while the third kind is of less relevance to the present study. The duality of practical goals (with all the promises of relevance, usefulness and feasibility) and cognitive goals (with all the requirements of excellence and truth) represents a significant, specific element of engineering sciences which contrasts with the natural sciences. For example, physics — at least as it is traditionally understood — lacks the aspect of following “external” goals of societal practice but focuses rather on “internal” goals. Here practice and research are methodically ordered. The duality of practiceorientation and knowledge-creation, of relevance and excellence, of usefulness and truth, of feasibility and reproducibility is organised according to the primacy of practice. Social practice, for which engineering sciences provide technical knowledge, is the ultimate objective of research in the engineering sciences. The guarantee of technical feasibility under “real” world conditions is thus a crucial objective


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of technical action. In contrast, excellence and the claim to truth do not become ends in themselves in engineering science research. They should rather be viewed as instruments, for instance in the hope that excellent and universal knowledge, by means of being transferred to new contexts, will display signs of greater utility and innovative force. Alternatively it may be supposed that the claim to excellence (which is strengthened, e.g. through evaluations and the increasing pressure of competition) will indirectly raise the innovative competence of engineering sciences. The primacy of practice also corresponds to historical development in the engineering sciences. The requirements of practice often lead to the establishment of new directions of research and new professorships (see, e.g., [König, 1995] with regard to electrotechnology). The termination of the requirements from outside science leads, in turn, to the termination or reduction of corresponding activities (e.g. in nuclear technology following the decision made in Germany to phase out atomic energy). This primacy of practice even leaves room for innovation that is driven by research, and it by no means implies that it is “demand-pull” alone that dictates the direction of engineering sciences. New ideas for products, processes and systems often derive from engineering practice thus constituting a technology push. Such ideas are, however, also based on assumptions about the social need for them or for some other type of “utility”. In such cases, primacy of practice implies that a demand or need for new technical solutions is initially accepted or assumed to be valid for reasons that may or may not be good. This occasionally leads to a dead end if social requirements are misinterpreted (such as in the case of the Transrapid monorail in the German transportation network), but it sometimes also results in socially and economically successful lines of technical development (such as in the case of MP3 players). The primacy of practice has certain implications. With regard to social practice, the concrete facet is essential to engineering sciences, as technical applications always take place in very concrete contexts: spatially, temporally and with regard to groups of people. Of course, technical knowledge must satisfy the criteria of universalisation and abstraction and be transferable to new contexts, but as a rule such transferability encounters practical and concrete limits. One general aspect of engineering sciences is, therefore, to reflect on the limits of technical knowledge transferability where concrete solutions are concerned. In the engineering sciences even theoretical work of this kind must ensure that there is always a path from theoretical, that is, abstract knowledge back to concrete practice. The coherence of engineering sciences thus lies in the specific relationship between theory and practice. The objective of engineering sciences is ultimately practical involving the social utilisation of technology and thus concrete realisations. Abstraction and theory are not ends in themselves but function rather as means, for instance, to systematise knowledge and facilitate the transferability of knowledge to other contexts. The natural sciences, by contrast, abstract their knowledge in the form of predicates that are kept as general as possible and

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make use of concretisations (i.e. experiments) only in an instrumental capacity, to test hypotheses, for example.3 Whereas knowledge from the natural sciences thus moves out of the “real world” and into more or less abstract model worlds, the engineering sciences aim directly at practical goals in the real world. This structural coherence within the engineering sciences accommodates two phenomena. One pertains to the diversity of the engineering sciences themselves, ranging from technical mechanics and materials sciences to civil engineering, mechanical engineering and chemical process engineering, and from microelectronics and nanotechnology to technical informatics and all the various biotechnologies. The other phenomenon lies in the great diversity of the objects of engineering sciences. They include micromechanical objects and simple machine components as well as complex chemical syntheses and even worldwide information and communication networks, all of which have to cover an enormous range of application conditions (e.g. with regard to temperature, pressure, speed or radiation). The specific nature of engineering sciences can be summarised according to the following three characteristics: 1. Engineering sciences are target-oriented sciences. A central concept is the application of scientific knowledge and practical experience to satisfy the “technical needs” of society. 2. Engineering sciences have a constructive character. At the forefront are the intellectual anticipation and evaluation of the structure, function and realisation of new technical systems. 3. Engineering sciences are integrating sciences. Since technical systems represent a specific coherence of natural and social components, knowledge is incorporated from the natural sciences, from other technical sciences and from the social sciences.

2.2 The development of the engineering sciences Technical action (in terms of production and/or the use of technical systems) is as old as humanity itself. Engineering sciences, by contrast, arose — via various early forms — much later (see also Channell’s chapter in this Volume, Part I). In the eighteenth and nineteenth centuries, many mathematicians and natural scientists endeavoured to apply mechanical principles to technical problems. This had varying degrees of success, leading to positive results in simple technical cases and failing predominantly in the case of more complex technical problems which taxed the resources of the time when it came to finding solutions on the basis of theory and calculation. However, this was just the beginning of a cross-fertilisation between natural scientific and technical knowledge. 3 Philosophers of experiment, however, have criticised this theory-dominant view of experimentation, putting emphasis on the value of the experimental practices in themselves (see, e.g., [Hacking, 1983; Radder, 2003]).


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Thanks less to technical questions than to organisational and practical administrative matters in the form of the guidance, management and state “supervision” of manufacture and trade, eighteenth century Germany saw technology develop as a science. It was particularly Johann Beckmann (1739–1811), from the University of G¨ ottingen, who systematised the technical knowledge underlying the various work being done in craft areas and factories to make it all more teachable. He did that especially in his book “Anleitung zur Technologie . . . ” (“Instructions on Technology . . . ”), written in 1777, in which the equality or similarity of (technical) procedures was perceived as the basis of organisation [Beckmann, 1780]. He was concerned with the connection between “true basic principles” and “reliable experience”. By “true basic principles” he meant the basic scientific principles for the theoretical elucidation of technological processes, while “reliable experience” was what he saw as the stable element, as what is gained from living in the world, from technical, formal know-how based on handed down experience. In that way, Beckmann strove to rationally explain working procedures. Beckmann’s approach received much acclaim, but it soon became clear that it was not possible to develop a technological science at that time. Nevertheless many attempts were made to apply this concept. To name but two such attempts there was first Karl Karmarsch (1803-1879) “Grundriß der mechanischen Technologie” (“Outline of Mechanical Technology”, 1837/41; later editions were entitled “Handbuch der mechanischen Technologie” (“Textbook of Mechanical Technology”) and then there was Friedrich L. Knapp (1814-1904) and his “Lehrbuch der chemischen Technologie” (“Textbook of Chemical Technology”, 1847). A detailed overview of further approaches to and institutions of technology can be found elsewhere, for example in Spur [Spur, 1998a]. Beckmann’s approach was not taken up again until the mid-twentieth century (for further details, see Section 5). Engineering sciences, as institutionalised sciences, evolved then in the eighteenth century but particularly in the nineteenth century which saw the rise of industrial production, the move from hand to machine operation and the step from manufacture to “big industry” (Marx), which accompanied the invention and further dissemination of labour machines (“Arbeitsmaschinen”), the use of steam power and the tempestuous development of the natural sciences. This process is still ongoing today: new disciplines in the engineering sciences are still emerging (like, for instance, nanotechnology). The accumulation of the technical experience of many generations made it possible to systematise and generalise our empirical knowledge of technical systems, processes and methods for the first time. Knowledge was based on how to organise and manage industrial production on a large scale and on how to exploit natural forces and employ technical systems (more) effectively. Inevitably the developing technical basis in advanced countries (Great Britain, France, USA, Germany) required the conscious use of science. In those days, the laws of mechanics and thermodynamics were therefore applied to machines, and mathematical theorems were used to successfully solve static construction problems and to control processes. In that way constructive-technical knowledge was created. In particular, the development of widely applicable machines (initially

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the steam engine, later the electromotor), a branching system of transfer mechanisms and the tool machine were accompanied by the increasing use of scientific knowledge and methods. Changes in the means of work (steered, for instance, by the various available energy forms), in the basic materials used (metals, non-metals, natural products, “synthetics”, composite materials) and in the processing conditions to be technically controlled (such as in micro and macro areas, temperatures, pressures, precision, handling and processing times) have led and still do lead to continual further developments in the engineering sciences. With the emancipation and consolidation of the engineering sciences came institutionalisation in the form of polytechnics and technical colleges (including the right to obtain a PhD). The same era saw the rise of engineers as a professional group with its own organisational forms and societies, including technical and “polytechnical” journals. This “scientification of technology” unfolded very differently in each country (compare, for instance, France, Germany, Great Britain and the USA) in the areas of scientific organisation and practice (for instance with regard to links between theory and practice, the significance of the natural sciences and mathematics as a conceptual basis and integration into existing universities or colleges), all of which is too much to be presented in any detail here (see, e.g., [Buchheim and Sonnemann, 1990]).

2.3 Debates on the specific nature of the engineering sciences From the very early days, the specific nature of engineering sciences and its status within the system of sciences has remained a subject of discussion, above all else with regard to its relationship to the natural sciences. From the point of view of their substantive nature, intensity and consequences these theoretical reflections have followed very different paths. One might, for example, cite the methodological controversies of the second half of the nineteenth century and the debate on technology as an applied science aired in Technology & Culture in the 1960s. In some form or another, the subject always revolved around people’s image of the engineering sciences, the actors involved (engineers, technical scientists), and the relevant teaching and research institutions (polytechnics, technical colleges, and technical universities). In the first half of the nineteenth century, Ferdinand Jakob Redtenbacher (1809– 1863) attempted to provide students of mechanical engineering with the practical tools they needed to learn about construction. His goal was to create his own approach in order to transcend both the English empirical orientation and the French school of engineering training approach with its method that was strongly rooted in mathematical theory. His aim was to unite people’s knowledge of physicalmechanical nature with their experience, capabilities and skills, none of which could be entirely gained through theoretical education. He appealed to engineers to link scientific knowledge to technical ability, “calculation” and “feeling” [Schneider, 1987, p. 174], and cognition (knowledge) to creation (making). However, he


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did not consider their relations (cognitive, methodological and so on) to each other, or their — indisputably historically variable — relative importance to, for example, engineering training courses of the day. He thus provided a starting point for opposing lines of discussion on systematic ways of finding solutions to technical problems. One strand of thought emphasised the machine-theoretically oriented direction whilst the other dwelt on the creative character of construction engineering. Ultimately it was the development of science, technology and industry that was to provide the solution to this methodological controversy that had been so vehement. Both approaches proved to be unconditionally necessary, complementary and elucidatory. This led to a synthesis within engineering training; to the combining of theoretical lectures, drawing board draughtsmanship, laboratory experiments and experimentation with life-size machines under real conditions. In the 1960s discussion launched in Technology & Culture that was initiated by the article “Toward a Philosophy of Technology” [Technology, 1966] the emphasis was somewhat different. There the primary focus was on a philosophy of technology or discussion in terms of scientific theory and it was particularly conducted by non-experts in the engineering sciences. Proceeding from the contributions made to a symposium, it was also concerned with the specific nature of engineering sciences in terms of cognition and method, the relationships to the (natural) sciences (as independent sciences or as “applied natural science”) and the concept of “technology” (e.g. as knowledge or know-how). The opinions emphasise “technology as knowledge” on the one hand and “technology as activity” on the other hand (for more details, see [Mitcham, 1994, pp. 192ff.]). Subsequent considerations pertaining to the philosophy of scientific and technical theory illustrated the fact that these two sides are related to each other and are inseparable (e.g. as units of knowledge and as design — see [Banse et al., 2006]). Since the end of the 1960s, the engineering sciences and their specific nature have once again been at the centre of intellectual debate, particularly regarding the philosophy of technology and scientific theory, but new interest also arose in the sociology of technology, in economics and in the history of technology. At the moment, from the perspective of a philosophy of technology alone — and that is the focus here — the current themes embrace the relationships between the natural sciences and the engineering sciences, forms of knowledge in engineering sciences and the establishment of a general technology (see, e.g., [Lenk and Maring, 2001]).

2.4

Perspectives on coherence and diversity

The relationship between the engineering sciences and practice is the point of departure when considering the above-mentioned coherence of engineering sciences (see Section 2.1). At this level, mechanical engineering, process engineering, civil engineering and production engineering, to give but a few examples, do indeed display a common structure. However, even at first sight, clear differences can be recognised between the disciplines of the engineering sciences with regard to the relevant subject areas, materials and tasks. When it comes to the coherence

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and diversity of the engineering sciences, one must thus seek further levels of differentiation in order to pursue assumptions about coherence and, in a more concrete sense, detect the facets of diversity in the engineering sciences. This can be done in various ways: • Methods in the engineering sciences presented as approaches and procedures in order to gain a knowledge of the subject areas and the technical problem solving methods (practice and design, cf. Section 3): the examples that can be cited here include methods of analysis, design and construction, heuristic models, experimental methods, and modelling and simulation. The activities of the engineers in the various fields are also considered, such as in research, development, production, distribution, marketing, maintenance and quality control. • Substance in the engineering sciences: this can be carried out according to the classical tripartite division into matter, energy and information. Differentiation may be made according to areas of application (e.g. energy provision, material research, space technology or robotics), whereby in each case the conditions valid for different contexts are considered. Alternatively the basis could be to adopt approaches to systematising or abstracting technical knowledge (cf. Section 4 for such divisions according to content). The substance issue also comprises a look at the products of engineering sciences in the form of the available technical knowledge and know-how as well as its materialisation in products, processes and systems. • Generalities in the different methodical or substantial directions which can be interpreted within the framework of a general technology (cf. Section 5). The identification of aspects of coherence or diversity between the sub-disciplines of the engineering sciences has its implications. The topics and questions that can generally be attributed to engineering sciences can be addressed both at research and at theoretical level and in an interdisciplinary manner. Courses offered at universities, for instance, for engineers in training can be conceived in such a way that they cross faculty and disciplinary borders. However, where diversity is dominant, these activities will always be the responsibility of sub-disciplines such as process engineering, construction engineering or materials science. 3 COHERENCE AND DIVERSITY IN TERMS OF METHOD The central theme of this section is the fact that the engineering sciences display strong common features in terms of method and these common features correlate with the specific theory-practice relationship mentioned above. The diversity which is indeed present in terms of method subordinates itself structurally to coherence in the characterisation of technical design as a problem-solving strategy with the use of technical knowledge (see, e.g., [Hubka and Eder, 1996]).


3.1

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The methodology of the engineering sciences

Methods are target-orientated procedures aimed at the intersubjectivisation (objectivisation) of information and knowledge. Using methods, the production and examination of knowledge can be conducted systematically and in such a fashion that it becomes learnable as well as teachable. Methods are also the central element of scientific quality assurance, of education and of scientific progress. Engineering sciences have developed a plethora of methods which are in part intrinsic and have, in part, been taken over or adapted from other disciplines. These can be classified as follows — albeit with much overlap — (see [Banse et al., 2006]): • Methods of design: If technological design is thought to solve problems by means of technical knowledge and know-how it is possible, depending on the phase division of the processes of technological design chosen in each case, to distinguish between various classes of methods. Intuitive-heuristic methods proceed from the plurality of available knowledge forms and attempt to deploy creativity techniques to find new solutions. By contrast, rationalsystematic methods start from an analysis of the system under consideration in terms of problem-solving and infer appropriate solutions top-down. In both directions, evaluations and selections from the possible options must ultimately be made (both according to the criteria of technical functionality and criteria external to technology such as economic viability, customer acceptance or ecological compatibility). For this reason, evaluation and selection methods are also included in the spectrum of design-orientated methods included in the engineering sciences (for more details, see Section 4). • Methods of research: The provision and examination of the technical knowledge necessary for the technical design goals aimed at societal practice (see Section 3.2) require their own methods. Here too, heuristic models play a role and serve above all else to structure the further search for knowledge. Of special significance is the distinction — familiar from the classical natural sciences — between theoretical-deductive and empirical-inductive procedures. The theoretical-deductive procedures chiefly emphasise the mathematisation of functional connections and the use of mathematical processes. Empiricalinductive procedures, by contrast, are laboratory orientated. They revolve around appropriate measuring techniques and experimental procedures such as materials processing or process regulation. • Methods of implementation: The technological designing of products, processes or systems is something that must be implemented by engineering as well. Implementation methods comprise various approaches to creating prototypes, to demonstrating technical feasibility and finally to creating the physical hardware that can provide the required capabilities. In all areas, the introduction of computer-aided methods has led to many changes. To a certain extent mathematical modelling and simulation have now replaced

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laboratory experimentation which, in turn, often permits considerable cost-saving (see for more details Part IV of this Handbook). At the same time, however, new challenges arise when it comes to validating model-simulated knowledge in the real world.

3.2

The methodological characterisation of technical knowledge

The products of the engineering sciences are inherent in the systematised and quality-assured knowledge of technical processes and systems and they are, to some extent, anchored in technical artefacts. This knowledge, which has been gathered, extended and refined over the course of decades and centuries — and which in each case includes knowledge of the necessary know-how — constitutes the starting point for technical design plans. Technical knowledge is knowledge for action. If such knowledge is to be teachable and learnable it must be or become explicit. In technical practice, however, implicit knowledge (tacit knowledge) plays an important role. 3.2.1

Technical rules as a form of technical knowledge

The origin of the engineering sciences in the nineteenth century can be traced back to an increasing demand for explicit, systematic technical knowledge, for the systematic production of new technical knowledge and for well-educated engineers. If technical knowledge is to be acknowledged as true knowledge it has to satisfy certain demands of invariance in different situations and transcend the subjective. This implies that this type of knowledge can be taught and learned or, in other words, systematically passed on in education. Together with other systematisations that are thus made possible, for example in the form of text books for sub-disciplines within the engineering sciences, a process of systematic accumulation of technical knowledge is therefore initiated. The basis of this process, which has proved exceptionally successful in the last 200 years, is the validity of technical knowledge. This validity extends beyond individual persons and historically coincidental constellations and can be understood methodologically since technical knowledge is characterised as a set of technical rules. These technical rules provide statements about means–ends relationships. A technical rule describes knowledge about how something can be caused. It describes the relationship between something that is to be achieved and the means required to achieve it (see [Bunge, 1974; 1983; Kornwachs, 2001]). This obviously represents a cause-and-effect relationship which is constructive in nature — in contrast to the natural sciences where the nature of things tends to be explanatory. In other words, it aims to achieve something, and is used in a practical context. Technical rules are valid with regard to the observance of certain conditions of applicability, such as those made by the requirements of a technical process on temperature, pressure or humidity. The justification of technical rules thus demands the designation of the corresponding attribute of the area of applicability.


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The character of technical rules consists precisely in the fact that an area of applicability can be given, within which the technical rule is valid, independent of individuals and situations (as described in greater detail in [Grunwald, 2002a]). It is precisely this fact that satisfies the requirements of being teachable and learnable: first, the rule itself is taught as a means-end relationship and then its area of applicability is taught. The totality of such explicit technical knowledge can be understood in this respect as a set of technical rules. The reproducibility of technical rules, regardless of the situation, is the precondition for being able to teach them and learn them in practice. It must be possible to prove and validate technical rules over and over again, and thus to recreate the initial situation, at least with regard to the relevant issues laid down by the rules’ area of applicability. This makes it possible to confirm any rule experimentally by repeating tests, like in a laboratory. To a high degree the reliability of technology is based on the reproducibility of technical rules that emanate from the regularity of technical processes, like in the engine of a car. Technology no longer functions if the conditions of validity are no longer satisfied, such as if diesel is put into a petrol engine or a non-waterproof watch is immersed in water. Technical knowledge does not consist solely of technical rules that can be explicitly described. A foreman knows how to handle “his” machine so that it functions properly although this knowledge may not satisfy the clear, logical criteria for technical rules (see [Vincenti, 1990]). Implicit knowledge is a part of technical practice and it also plays a role in the engineering sciences (see [Baumard, 1999; Collins, 1974; Polanyi, 1966]). Implicit knowledge is, however, personal knowledge [Polanyi, 1962] and, as such, can only be taught or learned to a very limited extent. Thus, the task of all the engineering sciences includes making the implicit explicit, because only then does technical knowledge become teachable, systematic and open to direct improvement. In that way the engineering sciences may also be said to be involved in demystifying implicit and personal knowledge and transforming tacit knowledge as far as possible into teachable knowledge about technical actions. The way in which this knowledge is teachable becomes the formulation of this knowledge as a set of technical rules. This feature is common to all disciplines in engineering sciences.

3.2.2

The properties of technical rules

Although technical rules do not represent universal laws of nature, with regard to generalisability, they do cover a wide spectrum ranging from a high degree of precision to rather heuristic rules of experience. The crucial factor in evaluating the degree of generalisability is knowledge of the area of a rule’s validity both with regard to the direct area of validity and the quality and reliability of such knowledge. The generalisability of technical rules is often the object of research in itself in that the areas of validity are, for instance, systematically tested in laboratory experiments.

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The characterisation of knowledge in engineering sciences given here is abstract. At this level of abstraction, technical rules are characteristic of the coherence of engineering sciences. The diversity of the various technological sciences can be seen when their concrete forms of knowledge are considered more closely, depending on the nature of the tasks, the type of objects, the materials used, etc. Beyond the level at which they can be formulated as technical rules, classifications can be introduced that are content-motivated and empirically strong (for more details, see Section 4). A relationship can be described between technical rules and the fundamentals of technical progress since such rules show a specific ambivalence. On the one hand, they express knowledge of what is technically feasible. For situations inside the rule’s area of applicability they provide knowledge that can be used for good reason in technical applications or handed down in teaching. On the other hand, however, they also contain knowledge of what is not yet feasible: the assumption that a rule will not function outside the area of a rule’s applicability is well-founded. Thus technical rules display a facet that points to certain knowledge and another facet in which technical knowledge is obviously insufficient. The latter also functions as an impetus to transform the boundaries of knowledge and know-how through research that extends the areas of a rule’s applicability. Technical rules do not generally permit specific measures to be identified as essential to the achieving of desired effects as there are frequently different technical rules that will lead to the same effect (e.g., there is often a choice between various materials when components are first conceived). Technical design thus precedes the need to choose between various options. This opens up the market to optimisation according to specific criteria (minimisation of development time, optimisation of the cost-benefit balance etc.). Technical rules are options for achieving a certain effect but usually there is no requirement that a particular technical rule must be abided by to achieve a particular end. 3.2.3

The origin and validation of technical rules

The production of new technical knowledge or know-how is partly deliberate. One might think, for instance, of systematically varying parameters or material properties in a laboratory so that the scope of a given rule’s applicability can be experimentally determined. Such good experimental practice procedures are a part of the methodological inventory of engineering sciences (see [Banse et al., 2006]). The sources of new ideas for technical solutions (typically in relation to inventions), include trying something out in a tentative and creative fashion. One might term this an intuitively driven heuristic procedure based on many years of experience and even rather random and spontaneous impulses (see [Banse and M¨ uller, 2001; Ferguson, 1992]). The “tacit” knowledge (see Nightingale’s chapter in this Volume, Part II) and intuition of experienced engineers that reflects rich experience but is hard to explain plays an important part in the practical progress of engineering sciences, see [Vincenti, 1990], namely with regard to the process according to which technical rules are created.


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Such knowledge that is intuitive or gained by unconventional means must then be validated according to the established procedures and scientific testing criteria. Only then can the knowledge and know-how gained be intuitively regarded as certified and reliable according to the standards of engineering sciences (and, for example, used or taught for the purposes of technical application). One task of the engineering sciences is thus to make intuitively gained knowledge and know-how systematic to ultimately formulate it as technical rules, see [Grunwald, 2002a]. Technical rules arise during the systematic process of making repeatable an action that has succeeded once but was perhaps intuitively conducted (e.g. the realisation of a technical process in a laboratory) and then going on to examine the conditions under which it will remain successful. In contrast to the practice of craftsmen, the scientific character of the engineering sciences is based on the systematically tested characteristic rules of technical activity. The methodological coherence of the engineering sciences includes the role of tacit knowledge and extends to efforts to achieve systematisation and explanation in terms of “objective” technical rules. 3.2.4

Methodological problems

The methodological coherence of the engineering sciences is also evident from the fact that they share certain fundamental problems linked to the production, validation and limitations of technical rules. This particularly includes the following: (a) Temporal inversion of the direction of inference: Technical rules are formulated retrodictively: they describe relations between effects that are to be realised and actions which are regarded as appropriate in the realising of such effects. What is to be achieved lies in the future and that is how inferences are drawn as to what can be done to get there, see [Bunge, 1974]. The corresponding technical knowledge, however, is produced and substantiated in the other time direction. For instance, during an experimental set-up in a laboratory, tests are conducted and the cause/effect-relations which are to be certified in the course of the experiment relate the causes to later effects. Temporal inversions of the direction of inference from cause/effect-relations to effect/action-relations are not, however, trivial either logically or pragmatically, see [Kornwachs, 1995]. If cause C reproducibly causes effect E and a request for achieving effect E is received, knowledge of the cause/effect relation between C and E will not imply that C should be done in order to achieve E for several reasons. One reason is that C may be a cause which does not result from human action. Another reason is that even if C were a human action, there might be other actions that would be more appropriate for achieving E in the specific context, see [Grunwald, 2002b]. (b) The difference between the tested area of applicability and the area of practical application (see Radder’s chapter in this Volume, Part I): The areas of applicability of technical rules are usually determined inductively, by means of trial and error. Certain parameters (e.g., temperature, pressure) are changed

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repeatedly until the rule no longer works. In technical practice, however, technical rules are often implemented in contexts that differ from those in which the rule was originally tested for correctness. Facilitated especially by the possibilities offered by computer simulation, it has thus become common practice in many areas of technical planning to use a simulation in order to test a technical rule and its area of applicability. A rule of such a kind that has been substantiated under artificial conditions is then realised in practice but under real manufacturing or implementation conditions. Laboratory experiments and simulations reduce the real conditions to the modelled form. The removal of these reductions when transferring the technical rule from the laboratory or simulator to practice thus represents a non-trivial operation. In reality, the “real-case” situation is thus often also the testing situation. This leads to questions regarding the scope of (computer) models, their adequacy in practice with regard to their ends and the transferability and validity of simulation results. (c) The need for pragmatic organisation: It is not just a question of using the right technical rules but also of ensuring their expedient conjunction to a sensible whole. Pragmatic rules regulate the temporal or logical organisation of solutions to complex technical problems on the basis of detailed technical rules (this is particularly relevant in the field of civil engineering; one just needs to think of questions of logistics on a major construction site). As a rule, disregarding the pragmatic organisation usually leads to failure to achieve the desired solution. (d) Dealing with lack of knowledge: Since many if not all problems in the “real world” can be regarded as “wicked problems” [Simon, 1973], the attempt to solve them by technical means is inherently fraught with uncertainty (see [Banse, 2003; Rittel and Webber, 1973]). Strategies for dealing with these uncertainties and for developing “robust” technical solutions form part of the spectrum of activities of all engineering sciences.

3.3 Methodological coherence in technical design In the engineering sciences, design has to do with solving problems with the help of technical products, systems or procedures (see also the chapters on design in Part III of this Handbook). This can only be achieved with the dedicated implementation of knowledge and know-how drawn from the engineering sciences. Technical constructing and drafting form the procedure in which knowledge and design come together. Drafting is thus the methodological location at which the specific relationship of theory and practice in the engineering sciences is most clearly apparent (see, e.g., [Cross, 1989; Gregory, 1966; Hubka et al., 1988; Hubka and Eder, 1996]). Most general descriptions of how technical solutions come about distinguish between the design process, which results in a executable draft of the solution and the execution itself, see [Hubka and Eder, 1996]. In this respect the concept “design


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process” designates the operationalisation — in terms of the engineering sciences — of the drafting or planning of technical solutions. In the following section we describe the procedural characteristics of the design process and the demands placed on results. The methodological coherence of technical planning is evident from these steps. One way of going about this is by establishing the relationship between the requirements for solving specific problems and the available technical knowledge (see above). One might call this: selecting an appropriate combination from the host of available technical rules and presenting that as a solution (in the process, new technical knowledge may also have to be developed). Various ways of structuring the design process have been developed. Although their terminology may differ, in terms of content they have much in common. They emphasise different answers to the question of whether the design process can be characterised as a linear process or whether it primarily consists of feedback loops. The current trend favours feedback loops and iterations because of the opportunities they provide for sequential phases of learning. This is even true of graphic representations (see Fig. 1), in which a linear presentation is frequently preferred for didactic reasons (see [Banse and Friedrich, 2000; Hubka and Eder, 1996]). The design process accordingly consists of a series of steps which are repeated with an increasing degree of detail and greater approximation to technical action (see, e.g., [Eder, 2000, p. 217; Ropohl, 1999, pp. 258ff.]). Problem analysis and planning involves formulating the requirements placed on the technical task in such a way that their fulfillment leads to a solution. This then leads to the formulation of a consistent specification. After the target system has been sufficiently clarified, a lack of knowledge is often encountered in the conception phase with regard to feasibility, either intrinsically or in relation to the given boundaries (e.g. the cost framework). In planning practice it is feasibility or pilot studies that are often used to clarify this problem; a rough preliminary evaluation of a project’s risk or the structure of a possible solution concept is made when there is insufficient information to make a precise evaluation. The main task during the conception phase is to break down, in system-analytic terms, the desired overall function of the technical solution into sub-functions; it then becomes possible to ascertain solution principles and the relevant basic structures. If possible, this should be based on the available scientific and technical knowledge and know-how; otherwise, conception leads to the identification of gaps in knowledge and know-how and thus, in turn, to a need for technical research. The options for solving the sub-functions are integrated into one overall concept or several variants suitable for solving the overall function (see [Ropohl, 1999]). In the draft phase (when a technical system is being developed), this concept forms the basis for the creation of a scale draft (invariably a digital model). Such models are evaluated according to various technical criteria such as feasibility, security and function fulfilment in conjunction with the realisation of necessary preconditions, material, and energy and data flow. At this juncture it is usually already possible to consider criteria from outside the technical field (cf. Section 3.4). At the very least such criteria will include economic aspects and perhaps also

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Problem analysis Planning

Task selection

Determine development task

Conception Clarify and specify problem definition

Divide overall function into subparts

Ascertain solution principles and structures to fulfil subparts

Combine solution principles and structures to fulfil overall function

Elaborate conceptual variants for principle combination

Draft Create a scaled draft

Technical and economic evaluation of the draft

Create improved draft and fix corrected draft

Elaborate Design and optimisation of individual components

Elaborate explanatory documentation

Check costs

Realisation

Figure 1. Procedure in the design process. Source: modified after [VDI, 1977]


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further social, legal or ethical aspects. It is on this basis that an improved draft or model is drawn up. For temporal sequences the time structures are elaborated and in each case milestones with verifiable requirements are included. These milestones constitute the starting point for projections and project management. The elaboration phase covers further concretisation and detailing while optimising the individual components of the draft and its composition. The systematic connections between the necessary resources are determined and finally the documentation for carrying out the work is produced (e.g. design instructions for a technical system). Rough cost estimates are refined and are made as realistic as possible. The results of any design process are the elaborated drafts of a technical solution. The degree of elaboration can differ depending on the knowledge available and the adequacy and depth of the planning. When drafting a technical solution, one must distinguish between an internal technical side and an external side, each of which is treated differently in terms of method and processed by different disciplines or groups in the further implementation of the solution: • The internal side of the technical drafts serves to guarantee the required technical functionality. What characterises this facet is the requirements placed on materials by energy considerations and by the guidance of data and information flows, security and risk considerations and, subsequently, the provisions made for production, utilisation and disposal. The guaranteeing of these aspects is a task of quality assurance in the strictest sense. • The external side concerns aspects of the technical solution which influence the social context (cf. also Section 3d2.4). These include costs, the resources necessary for research and development, the duration of realisation, the possible inclusion of users, the operational knowledge needed by future users, and the prevention of obvious misuse and predictable abuse. The task of quality assurance in its broadest sense is to oversee these external interfaces to technical models, since success is as significantly dependent on them as it is on the realisation of internal functionalities (see also Radder’s chapter in this Volume, Part V). The significance of the external side of technical solution proposals has increased considerably over the past decades. One can cite examples of technical solutions ultimately becoming a social failure though initially they might have seemed to be exceedingly appropriate in terms of standards of internal rationality (examples are fast-breeder technology in the nuclear energy field and the magnetic high-speed train Transrapid). Once the design process has led to results and a decision has been made about a technical solution, the realisation phase can begin. This is not actually a task of engineering sciences but rather of technical practice. It does, however, remain the task of engineering sciences to support this practice by further developing existing procedures (e.g. conceptions, drafts or quality management), by taking up and

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managing new challenges (e.g. support for these processes through new computeraided or internet-aided modelling and simulation procedures), and by backing up and evaluating current practice. The process outlined here for creating technical solutions through design and planning on the basis of technical knowledge perfectly illustrates the coherence found in the engineering sciences which emanates from a specific relationship between theory and practice. Insofar as research in the engineering sciences should support technical practice and the level at which technical details should be included, it is all a question of introducing systematisations, improvements and extensions. At the level of overall technical solution processes there is also the matter of considering systems and improving efficiency at procedural level. Engineering sciences operate on various levels. The upper level of drafting and design illustrated here demonstrates a coherent structure, while at the technical level increasing diversity (e.g. in the form of various tasks, application areas and subject areas) is visible and differentiation is necessary (cf. Section 4).

3.4 Technology and engineering sciences within the context of society The successful use of technology is subject to numerous conditions linked to different goals, subject areas and methods in the respective fields (e.g. in automobile technology, power plant technology, refrigeration technology, computer technology or construction). The success of technical endeavours depends both on the satisfactory overcoming of technical challenges and on non-technical factors such as acceptance, the relative advantages over other potential options for solving a problem and user wishes. Examples in Germany such as the breeder reactor in Kalkar, the Wankel motor and the Transrapid train show that technically successful developments by no means automatically become successful applications. Non-technical factors such as success in the market, customer acceptance, and “striking the right note” with regard to lifestyle expectations (such as in the cell phones market) are important factors affecting the success of technical developments. Technical quality is often a necessary but by no means sufficient precondition for social and, particularly, economic success. Technical innovations must also fit into the context of social practice. Since the engineering sciences not only claim to satisfy inner scientific knowledge criteria but also to be socially relevant, the non-technical factors given above as relevant to technology must also constitute an object of technological reflection. The specific relationship between theory and practice in the engineering sciences entails concern not only with technical matters but also with the social context. This places methodological requirements on methods of selection and evaluation whilst also underlining the specific responsibility of the engineering sciences. This is a necessity that is shared by all the disciplines in the engineering sciences [Schaub et al., 1983] and is supported by technology assessment and related activities (see Grunwald’s chapter in this Volume, part V). The need to include non-technical factors finds methodological expression in


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various of the evaluation, selection and decision-making procedures in which both technical criteria and non-technical aspects play a role. Selection and decisionmaking necessities pervade the entire technical problem-solving process and the available room for manoeuvre is utilised to achieve the optimal solution in each context. The selection and decision-making necessities come into play early on with the formulation of the specifications and the first steps towards drafting and the drawing up of concept papers and extend to the phases of elaboration, development and production. They take place at very different levels and range from decisions on details in the laboratory to far-reaching strategic decisions on the general direction to be taken by projects. When preparing a decision for the carrying out of a proposal the drafts, concept papers and plans for solving the technical problems must examine and evaluate quality aspects such as the reasonably assumable technical feasibility, the likelihood of achieving the goal, the possible side effects, costs, efficiency and acceptance. This is often done in a comparative way which means that competing drafts are considered. The following methodological requirements concern the engineering sciences in general: • The Link with Practice: There must be links with the social problems for which engineering sciences aim to provide solutions if their relevance is to be ensured. Even if the majority of the engineering sciences are more oriented to technology push, i.e. to supply rather than to the problem or demand facet, they still have to keep an eye on social responsibilities. • The Establishing of Priorities: From the large number of possible research directions and topics priorities have to be set and choices have to be made, for instance according to the urgency of the problems in hand or the extent of the predicted research. Internal scientific criteria also play a role. One consequence of the demand for technical products, procedures and systems to be economical and competitive is that methods of calculating prospective efficiency attract special attention. • Anticipating User Behaviour : The intended success of the technical scientific results in later societal practice also involves considering social questions such as expected or possible user behaviour and, for example, preventing the obvious misuse or intentional abuse of technical products. • Considering Adverse Consequences: Ultimately in the engineering sciences responsibility also includes considering the possible unintended side effects of developments and participating in technology assessment projects, e.g. in relation to competence and evaluation potential (see, e.g., [Grunwald, 2002b; Rader, 2002; Rip et al., 1995]; see also Grunwald’s chapter in this Volume, Part V). Thus, in the context of a wider society, the methodological coherence of the engineering sciences resides in the fact that — as a result of the specific relationship between theory and practice — they represent coherence in terms of social responsibility which is something that can be realised by means of methods and

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procedures. Although such responsibility assumes different forms in different contexts, the engineering sciences share the obligation to cautiously observe social practice and to reflect on the way in which their own particular work relates to that practice. When dealing appropriately with this kind of responsibility it is important to critically analyse power relations, to question the predominant technology push approach and to ethically reflect on engineering (see [Pater and Van Gils, 2003; Schaub et al., 1983]; on the special relevance of ethics in engineering design, see [Van de Poel, 2001; Van Gorp, 2005]). In the guidelines laid down by the German Union of Engineers [VDI, 1991] the eight core areas of values identified as criteria for selection and decision-making processes with regard to technical options are these: functionality, safety, economy, prosperity, health, environmental quality, personality development and societal quality. These very general criteria must be made more concrete and operational before they can be implemented to solve technical problems. They must be established in relation to parameters that are concretely measurable or can be evaluated during decision-making processes such as costs, utility, likelihood of failure, use of natural resources, risk of accidents, emissions, time consumption and the demands placed on human time. The solutions to technical problems are usually presented with an abundance of goals that have to be achieved simultaneously: they are supposed to function well, be safe, efficient, economical and environmentally friendly, to name but a few. This complex collection of criteria provides a good idea of the heterogeneous demands placed on finding “good” solutions to technical problems but also of the fact that their quality only partly depends on satisfying technical criteria as it also depends, in part, on the realisation of non-technical expectations. These expectation profiles reveal the complexity of the problems of selection and decision-making and thus of the social factors that the engineering sciences have to take into account and methodologically resolve. Particularly in the technical field, but even more so regarding non-technical criteria, there are often competitive relationships and conflicts between the goals behind different demands. For example, a car body should be as light as possible to reduce emissions but at the same time it also has to satisfy safety requirements in collisions. Similarly, bridges are supposed to be economical whilst also being as safe as possible (see, e.g., [Van Gorp, 2005] for some examples]. Thus, judgements frequently have to be made from evaluations based on very diverse criteria that must be integrated into an overall assessment. In these situations the methods adopted for selecting and decision-making provide help and support decisions that are as transparent and as logical as possible. These methods include quantitative procedures such as risk analysis, material flow analyses, life cycle analyses, environmental balance, cost-benefit analyses and multi-criteria decision-making procedures, as well as qualitative and discourse-orientated procedures (see [Joss and Belucci, 2002]; see also Grunwald’s chapter in this Volume, Part V). All these methodological and conceptual approaches that link developments in the engineering sciences to their social context are common to all engineering sciences and represent the expression of a specific theory-practice relationship. Thus,


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to the extent that these tasks and the methods and procedures employed to solve them (such as cost-benefit analyses, risk assessment, multi-criteria decision-making analyses or technology assessment) are an expression of coherence within the engineering sciences, they can be taught in university courses across and beyond the borders of faculties and disciplines. Their application and the forms they take vary, however, in the different engineering sciences disciplines. 4 COHERENCE AND DIVERSITY IN THE ENGINEERING SCIENCES DISCIPLINES Engineering sciences are subdivided into a large number of disciplines corresponding to the diversity of technical systems and the objectives of technical and scientific-technical activity. There is, however, no recognisable hierarchy in this organisation of the disciplines. We maintain that the reason for this is because the internal structure of the engineering sciences reflects a lengthy historical development process marked by pragmatic classification principles revealing the status of technological developments and their level of scientific inquiry. Scientific discipline pertains here to both a specific system of knowledge (cognitive; statements about a subject area, i.e., about existing and future technical systems) and to a specific community of scientists (social; institutionally organised researchers and teachers affiliated to subject areas with specific technical language, scientific standards, publications, scientific events, etc.). The formation and development of the disciplines in the engineering sciences (genesis of disciplines) depends on the coherence and interaction between these cognitive and social factors. The dynamics which are currently clearly prominent in the development of theories and methods in engineering sciences (differentiation and integration!) have refocused the discussion more sharply on the problems encountered in classifying the engineering sciences. It is understandable that as the tasks confronting the engineering sciences become more complex, so too does their processing and thus also the interdisciplinary interaction of a whole range of internal disciplines. Current knowledge of the structure and classification of the engineering sciences does not give a comprehensive picture but it does allow us to identify certain results and problems which contribute to understanding the nature and development of the field. In addition, it must be borne in mind that any classification — and this includes the engineering sciences — is pragmatic, that is to say, specific and related to a particular end. Such an end could include the organisation of faculties and institutes within a technical college, a library system, the organisation and comparable education of professional groups affiliated to technology (i.e. engineers), the structure of databases, etc. For this reason, the following discussion constitutes only one approach to coherence and diversity in the content side of the engineering sciences (see [Jobst, 1986]). We should first emphasise that probably the “roughest” but at the same time most basic way to classify the engineering sciences might be by making distinctions

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according to whether the dominant features are technological (manufacturing, processing or procedural techniques), constructive, or materials science-linked tasks. This differentiation, which touches on a large group of disciplines and is justified on the basis of the specific aspects of both the subject and the activity aspects of the engineering sciences, will continue to remain important in the future. In the second place an organisational principle has emerged in the engineering sciences system which in some ways represents a further differentiation of the first point and which is objectively determined by the technical means and processes that are scientifically processed. This principle distinguishes between those disciplines that are usually concerned with the more constructional (structural) and the more technological (procedural) side of groups of products and procedures. Within these disciplines there is often — but not always — a subdivision in line with constructive and technological orientations. This pertains to the quantitative and qualitative status of development of the subject area in question and to the specific nature of the historical development of particular disciplines, to personnel questions, to the size of the scientific institutions, etc. The disciplines which fall under this organisational principle include — from the field of general mechanical engineering — machine tool construction, textile machines and polygraphs. Disciplines from the fields of electrotechnics, electronics and computer technology are also included. In concrete terms these disciplines may, of course, be further subdivided. In the field of machine tool construction one may take, for instance, such areas as production means development, component manufacture, process design, jointing techniques and assembly. This does not, however, contravene the principle that the structure of the disciplines is determined by examining real technical means and processes. Within the engineering sciences system there is, in the third place, the matter of the disciplines that deal with the structural and functional problems occurring in all or many technical means and processes. One could term these cross-sectional disciplines. They include measuring techniques, automation techniques, construction science, standardisation, security engineering and tribo techniques. It is, however, impossible at present to even come close to drawing any definite conclusions on classification in precisely these disciplines for the simple reason that their theoretical and practical significance is still growing in new directions and in terms of their scientific profile (e.g. measuring techniques and quality assurance), they are currently in a crucial phase of theoretical generalisation. The increasing significance of these cross-sectional disciplines is above all an expression of the process-orientated complexity of technical systems and corresponding solutions in the engineering sciences. In their investigations they reflect important moments of integration in the engineering sciences system. This is particularly true of systems that deal with the theoretical control of functions and structures, in other words, with whole classes of technical means and processes or those marked by strong similarity which has to be attributed to different classes. In the fourth place it should be stressed that the engineering sciences do embrace basic disciplines. Their specific nature is particularly present in the gaining


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of mainly theoretically-based knowledge and the developing of models, computational specifications, diagrams, flow charts, etc. These are the basic prerequisites for intellectual anticipation, constructive and technological design, evaluation, monitoring, and the perfection of whole classes of technical means and processes. What is still specific to these disciplines is the fact that in their investigations knowledge of the natural and mathematical sciences is very markedly transformed, combined, modified and applied to arrive at generally applicable theoretical statements on fundamental technical structures and functions. To cite a few typical examples, the following areas can be included in the basic disciplines of engineering sciences: technical mechanics, technical thermodynamics, technical cybernetics, the theory of electrical engineering and information technology theory. It is to be expected that in the future the number and more to the point the significance of such basic disciplines in the engineering sciences will grow as the natural and mathematical sciences increasingly pervade many areas of engineering sciences thus, in the process, expanding their theoretical basis. In short we may conclude that with regard to the coherence and diversity of the content of the engineering sciences there are many different and opposing tendencies associated firstly with content difference (types, functions, dimensions, the input and output of technological systems, the operating conditions such as forms of energy, temperatures, pressures, and so on) and secondly with technological development, research and teaching. 5 GENERAL TECHNOLOGY In addition to the tendencies towards differentiation, opposing tendencies that emphasise common features have always been present in the area of technical knowledge and engineering sciences, both towards a “general heuristics of invention” (see [Nickles, 1980]) and in the direction of a general technology. The term “invention heuristics” refers to all the intellectual efforts which, in a more organisationally systematic way (more or less independent of subject area), are suitable or capable of contributing to the production of something new (i.e. also to innovations in the technical field such as inventions, improvements or optimisations). These intellectual efforts can be traced back a long way to before the establishment of the engineering sciences in the nineteenth century. Examples include the work of Socrates (469–399 BC), Aristotle (384–322 BC), Archimedes of Syracuse (ca. 287–212 BC), Pappus of Alexandria (ca. 320 AD), Ramon Llull (1232/33–1316), Francis Bacon (1561–1626), René Descartes (1596–1650), Christopher Polhem (1661–1751) and Gottfried Wilhelm Leibniz (1646–1716). What is notable in this regard is also the cinematics of Robert Willis (1800– 1875), the “Theoretical Cinematics” by Franz Reuleaux (1829–1905) from 1875, the combinatorics of Wilhelm Ostwald (1853–1932) and — more recently — the “morphological method” of Fritz Zwicky (1898–1974). However, the ideal of acquiring a logically unambiguous understanding was always hindered by insurmountable difficulties in practice: the number of elementary

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or at least preliminary elementary concepts has always been and still is much larger than was originally supposed which means that the number of possible combinations is enormous. In 1806, Johann Beckmann (cf. Section 2.2) published the third part of his “Vorrat kleiner Anmerkungen u ¨ber mancherley gelehrte Gegenst¨ ande” (“Collection of Short Notes on Various Learned Subjects”) which included a short text that could be termed the “birth certificate” of a general technology: i.e. the “Entwurf der algemeinen Technologie” (“Draft on General Technology”). This signified a new approach to the understanding, comparing and inventing of technical systems. We have to briefly recapitulate if we are to contextualise the relevant threads of argument. In his work “Anleitung zur Technologie oder zur Kenntnis der Handwerke, Fabriken und Manufakturen” (“Instructions on Technology, or on the Knowledge of Trades, Crafts and Manufacture”), first published in 1777, Beckmann views technology not — as was usual up until then — as the teaching of skills but rather as “the science that teaches the processing of natural things or the knowledge of crafts”, as a science “which explains completely, properly and clearly all works, their results, and their reasons” [Beckmann, 1780, p. 17]. In this first step on the path to a more strongly theoretical basis for engineering, his initial intention was to systematise the various works of crafts and factories by going beyond a mere descriptive list of means and procedures. To that end he called for a stronger contemplation of knowledge from the natural sciences and a unified — and thus also unifying — terminology. While in his “Guide to Technology” Beckmann presents “a justification of the science of technology”, in his 1806 work “Entwurf der algemeinen Technologie” (“Draft on General Technology”), he goes a step further. Initially he divides technology into two parts by distinguishing special technology (the “particular part”) from general technology (the “first or general part of technology”): “Now I desire a directory of all the different purposes which craftsmen and artists have in their various works and together with it a directory of all the means by which they will know how to achieve these ends. I would give a directory of this kind the name of general technology, or the first or general part of technology” [Beckmann, 1806, p. 465]. The “description of the individual crafts” is the function of the special technology in the sense called for in 1777. With general technology, Beckmann then goes beyond a simply comparative systematisation of the exploitable technical means to consider realistic (technical) ends. This general technology should “reveal the common and special intentions of the works and means, explain the principles on which they are based and briefly teach what could serve the understanding and evaluation of the individual means and their selection in transference to other objects”. “For the artists and craftsmen”, he continues “this would facilitate thorough and general concepts of the objects they work on and the customary procedures here, and indeed, it would provide an overview which could lead inventive minds to new, useful improvements [ibid., pp. 465, 480]. As a scientific “reason” for all of this


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— i.e. as the “interest-guiding knowledge”, he emphasises that “[a]nyone who has tried to learn about many crafts and arts and anyone who has tried to gather many of them together in one view must notice that very many crafts, although their materials and goods are different, still use many works to achieve similar ends or that these crafts can achieve the same end in very different ways” [ibid., pp. 464]. Beckmann’s intention — as becomes clear from his considerations — is first to systematise all the technical and technological knowledge collected up until then before going on to give it a secure theoretical basis and justify the methodological programme of a heuristic of invention. In the years that followed, the general technological approach to outlining engineering in its entirety lost its importance. However, efforts were always made to undertake more or less comprehensive generalisations for individual fields of engineering or engineering sciences. An example is the more strongly (constructional) methodological approach adopted by Franz Reuleaux in the second half of the nineteenth century and the studies on “unit operations” and “unit processes” conducted in the 1920s and 1930s, which can be seen as the foundation or grounding of a general chemical technology. In the past 30 years, the general technology discussion has once again been revived: questions on generalisation and approaches to general technology were presented by various scientists from the perspective of different scientific disciplines. Today general technology is still more of a program than a concept that has been elaborated. In these various approaches to general technology, the general nature of technical objects and processes are recorded in terms of technical principles, basic and guiding rules, regularities, statements about effective settings, and so on. For more far-reaching generalisations towards a general technology (above all else as part of the “theoretical justification of the engineering sciences”), one must take into account that there are various, not yet sufficiently debated positions here (see, e.g., [Banse and Reher, 2002; 2004; Koen, 2003; Ropohl, 1999; Simon, 1981; Spur, 1998a; Wolffgramm, 1994/95]). The development of technical knowledge is thus on the one hand marked by embellishment with details and on the other by the promotion of systematising, comparative, integrating and generalising knowledge bases. 6

CONCLUDING REMARKS

The engineering sciences represent an interesting subject of investigation for philosophy and scientific theory. Many cognitive, methodological, normative and — in the strictest sense — science-theoretical questions differ from those in many other sciences. This comes first and foremost from the fact that engineering sciences must be understood as sciences of “doing” (as aim-related and producing activity) and that their practical, final result is real technology. This factor must be borne in mind at all times. In this case “real” technology must be taken to mean different things. First it is used to emphasise the fact that technical systems

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must function in a real world situation for a particular duration, not in an ideal or laboratory environment in which disturbances can be intellectually or practically isolated. This is also true of the production connection which — though it begins with an idea — only sees completion with the objectivised product. The real “environment” with its manifold influences, interactions and changes must always be remembered — although often it cannot be comprehensively registered in its entirety, nor its effects completely gauged or forecast from the point of view of impact. Ultimately, the application of technical systems relates to real people and to real institutions. Technology must be saleable, purchasable, acceptable, and usable. BIBLIOGRAPHY [Banse, 2003] G. Banse. Technikgestaltung im Spannungsfeld von Plan und Lebenswelt [Design of Technology: The Tension between Plan and Life-World]. In Technikgestaltung zwischen Wunsch und Wirklichkeit [Design of Technology between Desire and Reality], A. Grunwald, ed., pp. 71-87. Springer, 2003. [Banse and Friedrich, 2000] G. Banse and K. Friedrich, eds. Konstruieren zwischen Kunst und Wissenschaft: Idee — Entwurf — Gestaltung [Designing between Art and Science: Idea – Concept — Design]. Edition Sigma, 2000. [Banse et al., 2006] G. Banse, A. Grunwald, W. K¨ onig, and G. Ropohl, eds. Erkennen und Gestalten. Eine Theorie der Technikwissenschaften [Knowing and Designing. A Theory of Engineering Sciences]. Edition Sigma, 2006. [Banse and M¨ uller, 2001] G. Banse and H.-P. M¨ uller, eds. Johann Beckmann und die Folgen. Erfindungen — Versuch der historischen, theoretischen und empirischen Ann¨ aherung an einen vielschichtigen Begriff [Johann Beckmann and his Impact. Inventions — An Attempt at a Historical, Theoretical and Empirical Approximation to a Complex Term]. WaxmannVerlag, 2001. [Banse and Reher, 2002] G. Banse and E.-O. Reher, eds. Allgemeine Technologie — Vergangenheit, Gegenwart, Zukunft [General Technology — Past, Present, Future]. Trafo Verlag, 2002. [Banse and Reher, 2004] G. Banse and E.-O. Reher, eds. Fortschritte bei der Herausbildung der Allgemeinen Technologie [Advances in the Development of General Technology]. Trafo Verlag, 2004. [Baumard, 1999] P. Baumard. Tacit Knowledge in Organisations. Sage Publications, 1999. [Beckmann, 1780] J. Beckmann. Anleitung zur Technologie, oder zur Kenntnis der Handwerke, Fabriken und Manufakturen... [Instructions on Technology, or on the Knowledge of Trades, Crafts and Manufactures . . . ]. 2nd ed. Verlag Vandenhoeck, 1780. [Beckmann, 1806] J. Beckmann. Entwurf der algemeinen Technologie [Draft on General Technology]. In Vorrath kleiner Anmerkungen u ¨ber mancherley gelehrte Gegenst¨ ande. Drittes St¨ uck [Collection of Short Notes on Various Learned Subjects. Third Part], pp. 463-533. Verlag Johann Friedrich R¨ ower, 1806. [Buchheim and Sonnemann, 1990] G. Buchheim and R. Sonnemann, eds. Geschichte der Technikwissenschaften [History of Engineering Sciences]. Verlag Edition Leipzig, 1990. [Bunge, 1974] M. Bunge. Technology as Applied Science. In Contributions to a Philosophy of Technology, F. Rapp, ed., pp. 19-39. Reidel, 1974. [Bunge, 1983] M. Bunge. Towards a Philosophy of Technology. In Philosophy of Technology. Readings in the Philosophical Problems of Technology, C. Mitcham and R. Mackey, eds., pp. 62-76. Free Press, 1983. [Collins, 1974] H. M. Collins. The TEA Set: Tacit Knowledge and Scientific Networks. In Science Studies, 2, 165-186, 1974. [Cross, 1989] N. Cross. Engineering Design Methods. John Wiley & Sons Ltd, 1989. [D¨ urr, 1988] H.-P. D¨ urr. Das Netz des Physikers [The Net of the Physicist]. Carl Hanser Verlag, 1988.


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[Eder, 2000] W. E. Eder. Konstruieren aus der Sicht eines Konstruktionswissenschaftlers [Designing from a Viewpoint of a Designer]. In Konstruieren zwischen Kunst und Wissenschaft: Idee — Entwurf — Gestaltung [Designing between Art and Science: Idea — Concept — Design], G. Banse and K. Friedrich, eds., pp. 193-218. Edition Sigma, 2000. [Ferguson, 1992] E. S. Ferguson. Engineering and the Mind’s Eye. MIT Press, 1992. [Ferr´ e, 1995] F. Ferr´ e. Philosophy of Technology. The University of Georgia Press, 1995. [Gregory, 1966] S. A. Gregory. The Design Method. Butterworths, 1966. [Grunwald, 2000] A. Grunwald. Against Over-estimating the Role of Ethics in Technology. In Science and Engineering Ethics, 6, 181-196, 2000. [Grunwald, 2000a] A. Grunwald. Philosophy and the Concept of Technology. On the Anthropological Significance of Technology. In On Human Nature. Anthropological, Philosophical and Biological Foundations, A. Grunwald, M. Gutmann and E. Neumann-Held, eds., pp. 173-188. Springer-Verlag, 2002. [Grunwald, 2002b] A. Grunwald. Technikfolgenabsch¨ atzung. Eine Einf¨ uhrung [Technology Assessment. An Introduction]. Edition Sigma, 2002. [Hacking, 1983] I. Hacking. Representing and Intervening: Introductory Topics in the Philosophy of Natural Science. Cambridge University Press, 1983. [Hubka et al., 1988] V. Hubka, M. Andraesen, W. E. Eder, and J. P. Hills. Practical Studies in Systematic Design. Butterworths, 1988. [Hubka and Eder, 1996] V. Hubka and W. E. Eder. Design Science. Introduction to Needs, Scope and Organization of Engineering Design Knowledge. Springer-Verlag, 1996. [Jobst, 1986] E. Jobst. Besonderheiten von Klassen von Technikwissenschaften [Specialities of Groups of Engineering Sciences]. In Erkenntnismethoden in den Technikwissenschaften [Methods of Cognition in the Engineering Sciences], G. Banse and H. Wendt, eds., pp. 15-18. Verlag Technik, 1986. [Joss and Belucci, 2002] S. Joss and S. Belucci, eds. Participatory Technology Assessment — European Perspectives. Centre for the Study of Democracy (CSD) at University of Westminster in Association with TA Swiss, 2002. [Koen, 2003] B. V. Koen. Discussion of the Method: Conducting the Engineer’s Approach to Problem Solving. Oxford University Press, 2003. [K¨ onig. Technikwissenschaften [Engineering Sciences]. Verlag Fakultas, 1995. onig, 1995] W. K¨ [K¨ onig, 2006] W. K¨ onig. Geschichte der Technikwissenschaften [History of Engineering Sciences]. In Erkennen und Gestalten. Eine Theorie der Technikwissenschaften [Knowing and Designing. A Theory of Engineering Sciences], G. Banse, A. Grunwald, W. K¨ onig and G. Ropohl, eds., pp. 24-37. Edition Sigma, 2006. [Kornwachs, 1995] K. Kornwachs. Theorie der Technik? [Theory of Technology?]. In Forum der Forschung. Wissenschaftsmagazin der Brandenburgischen Technischen Universit¨ at Cottbus, 1, 11-22, 1995. [Kornwachs, 2001] K. Kornwachs. A Formal Theory of Technology? In Advances in the Philosophy of Technology, H. Lenk and M. Maring, eds., pp. 51-69. Lit-Verlag, 2001. [Lenk and Maring, 2001] H. Lenk and M. Maring, eds. Advances in the Philosophy of Technology. Lit-Verlag, 2001. [Mitcham, 1994] C. Mitcham. Thinking through Technology. The Path between Engineering and Philosophy. University of Chicago Press, 1994. [Mitcham and Mackey, 1983] C. Mitcham and R. Mackey, eds. Philosophy and Technology. Readings in the Philosophical Problems of Technology. Free Press, 1983. [Nickles, 1980] T. Nickles. Scientific Discovery, Logic and Rationality. Reidel, 1980. [Pater and van Gils, 2003] A. Pater and A. van Gils. Stimulating Ethical Decision-Making in a Business Context. Effects of Ethical and Professional Codes. European Management Journal, 21, 762-772, 2003. [Polanyi, 1962] M. Polanyi. Personal Knowledge. University of Chicago Press, 1962. [Polanyi, 1966] M. Polanyi. The Tacit Dimension. Doubleday & Co, 1966. [Radder, 2003] H. Radder. Technology and Theory in Experimental Science. In The Philosophy of Scientific Experimentation, H. Radder, ed., pp. 152-173. University of Pittsburgh Press, 2003. [Rader, 2002] M. Rader. Synthesis of Technology Assessment. In Strategic Policy Intelligence: Current Trends, the State of Play and Perspectives, A. T¨ ubke, K. Ducatel, J. P. Gavigan and P. Moncada-Patern` o-Castello, eds., pp. 27-37. Institute for Prospective Technological Studies (IPTS), 2002.

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[Rip et al., 1995] A. Rip, T. Misa, and J. Schot, eds. Managing Technology in Society. Cengage Learning, 1995. [Rittel and Webber, 1973] H. W. J. Rittel and M. M. Webber. Dilemmas in a General Theory of Planning. Policy Sciences, 2, 155-169, 1973. [Ropohl, 1999] G. Ropohl. Allgemeine Technologie. Eine Systemtheorie der Technik [General Technology. A Systems Theory of Technology]. 2nd ed. Carl Hanser Verlag, 1999. [Schaub et al., 1983] J. H. Schaub, K. Pavlovic, and M. D. Morris, eds. Engineering Professionalism and Ethics. John Wiley & Sons, 1983. [Schneider, 1987] J. Schneider. Franz Reuleaux und die Theorie der Maschinen [Franz Reuleaux and the Theory of Machines]. In Wissenschaften in Berlin [Sciences in Berlin], vol. 3, T. Buddensieg, K. D¨ uwell and K.-J. Sembach, eds., pp. 173-177. Gebr¨ uder Mann Verlag, 1987. [Simon, 1973] H. A. Simon. The Structure of Ill-structured Problems. Artificial Intelligence, 4, 181-201, 1973. [Simon, 1981] H. A. Simon. The Sciences of the Artificial. MIT Press, 1981. [Spur, 1998a] G. Spur. Deutung der Technologie als Lehre vom Wandel der Technik [The Interpretation of Technology as a Theory of Technological Change]. ZWF — Zeitschrift f¨ ur wirtschaftliche Fertigung, 93, 235-239, 1998a. [Spur, 1998b] G. Spur. Technologie und Management [Technology and Management]. Carl Hanser Verlag, 1998b. [Technology, 1966] Technology. Toward a Philosophy of Technology. Technology & Culture, 7, 301-390, 1966 (with contributions by L. Mumford, J. K. Feibleman, M. Bunge, J. Agassi, J. O. Wisdom, H. Skolimowski and I. C. Jarvie). [Tscheschew and Wolossewitsch, 1980] W. W. Tscheschew and O. M. Wolossewitsch. Gegenstand und Aufgaben der technischen Wissenschaften [Subject and Tasks of Engineering Sciences]. In Spezifik der technischen Wissenschaften [Specifics of Engineering Sciences], pp. 163-192. Fachbuchverlag, 1980. [van de Peol, 2001] I. R. van de Poel. Investigating Ethical Issues in Engineering Design. Science and Engineering Ethics, 7, 429-446, 2001. [van Gorp, 2005] A. C. van Gorp. Ethical Issues in Engineering Design; Safety and Sustainability. Delft University of Technology, 2005. [VDI, 1977] VDI — Verein Deutscher Ingenieure. Richtlinie 2222 “Konstruktionsmethodik: Konzipieren technischer Produkte [Guideline 2222 “Methods of Design: Prepairing of Technical Products”]. VDI, 1977. [VDI, 1991] VDI — Verein Deutscher Ingenieure. Richtlinie 3780 “Technikbewertung — Begriffe und Grundlagen” [Guideline 3780 “Technology Assessment — Terms and Foundations”]. VDI, 1991 (English version available at www.vdi.de). [Vincenti, 1990] W. G. Vincenti. What Engineers Know and How They Know It. Johns Hopkins University Press, 1990. [Wolffgramm, 1994/95] H. Wolffgramm. Allgemeine Technologie [General Technology]. 2 vols. 2nd ed. Verlag Franzbecker, 1994/95.

Part II

Ontology and Epistemology of Artifacts


INTRODUCTION TO PART II

Wybo Houkes, associate editor Ontology and epistemology are two of the oldest and best-established branches of philosophy. Yet in the long histories of these fields, artifacts and technology have seldom been topics of inquiry. Likewise, engineers have traditionally ignored questions regarding the nature and categorization of the items they produce and regarding the status of the knowledge they produce. Still, it seems obvious that engineering activities change the world by adding objects, such as combustion engines, and also add to our understanding of this changed world, by acquiring knowledge of, for instance, combustion processes. Indeed, technology can be characterized in at least two ways: as a system of artificial objects, and as a system of (practical) knowledge [Mitcham, 1978]. Surprisingly, these intuitions and suppositions have not given rise to more thorough analysis. A short reflection on the reasons for this lack of interest serves to introduce the background of the various contributions to this part. Mutual distrust partly explains the lack of mutual interest. Most philosophers have, ever since Plato and Aristotle, been suspicious of technological advances, and have subordinated the efforts of engineers to those of natural scientists. In the works of Heidegger and Ellul, which shaped much of what is now called the philosophy of technology, these anti-technological sentiments have culminated in deeply pessimistic, essentialist evaluations of technology and of societies that have become irredeemably technological. On their part, engineers largely equate philosophical reflection with empty speculation. The metaphysical engineer, it seems, only exists in poetry.1 More importantly, two standard ideas about artifacts make further philosophical analysis seem useless. The first, ontological idea is that artifacts do not, properly speaking, exist. Engineers cleverly rearrange physical stuff into suspension bridges and combustion engines, but in doing so, they add nothing to the ultimate inventory of the world. The second, epistemological idea is that technology is nothing but applied (natural) science. Both in engineering design and in engineering science, people solve practical problems, but in doing so, they add nothing – or at least nothing fundamental – to the total body of human knowledge. This idea is not exclusive to philosophy. There is a continuous, albeit not constant, tendency among engineers to model their work after the natural sciences. Curricula at engineering schools have become filled with courses in which students are taught to 1 “The metaphysical engineer” is one of the self-descriptions of Alvaro ´ de Campos, a heteronym of the Portugese poet Fernando Pessoa.


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apply scientific theories such as classical mechanics and thermodynamics to practical problems; and in practice, engineers have frequently attempted to “ground” their designs with the experimental method. Alternative (e.g., design-oriented) conceptions of engineering exist, especially since the 1960s, but they are typically prominent countermovements instead of the new orthodoxy. The two standard ideas are probably related, and may root in deeper convictions, e.g., about the reducibility of all objects to physical matter and about the subordination of practical reasoning to theoretical reasoning. What matters here is that, in combination, the ideas entail that, ontologically and epistemologically, engineering is a zero-revenue activity. While this view is clear and provocative, and may be assumed as a null hypothesis, it should only be accepted, if at all, after ample reflection. Such reflection is mostly clearly shown by recent work on artifact ontology. Several philosophers have argued against the ontological null hypothesis, and have concluded that artifacts are a significant class of real objects. These arguments, of which an overview is given in the chapter by Amie Thomasson, pave the way for analyses of the nature of artifacts. A feature of artifacts that has commanded particular attention is their functionality. Many artifacts come in functional kinds, and all appear to be “for” doing something. Analyses of artifact function typically focus on its relation with the intentions of designers and users, but nonintentionalist alternatives are possible. In her chapter, Beth Preston reviews these accounts, with the aid of a set of analysanda. Engineers also show an increasing interest in the nature and categorization of artifacts, albeit from a different perspective. Two areas that illustrate possible convergences between philosophy and engineering are presented in this part — both by, necessarily, explorative papers. Firstly, the chapter by Pieter E. Vermaas and Pawel Garbacz explores functional decomposition and function modelling. These engineering practices revolve around artifact functionality, and its relation to the structural or physical properties of artifacts. The practical value of these practices depends partly on conceptual precision, meaning that philosophy and engineering can strike up a mutually profitable relation. The same goes for the second area, that of formal ontology. For various engineering applications, categorizations of objects and their parts are vital. This has led to a veritable industry of domainspecific ontologies. The chapter by Stefano Borgo and Laure Vieu provides an overview of several ontologies and explores how, for one ontology in particular, characterizations of artifacts and their properties may be included. The last three contributions deal with epistemological issues. For these, the blockade on artifacts and technology was lifted in the 1970s, as the applied-science thesis grew ever more unfashionable. This led to some reflections of the nature of technological knowledge, which often stress its autonomy from scientific knowledge. Indeed, the knowledge produced by engineers and, more generally, knowledge regarding artifacts appears to have some distinctive features, such as its tacit dimension and its relation to practical, rather than purely theoretical interests. An overview of the work on technological knowledge, as provided in the chapter by

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Wybo Houkes, may lead to doubt whether these features are sufficiently distinctive, but this should not prevent — and has not prevented — philosophers and other researchers from analyzing them. Outside philosophy, the tacit dimension of technology and engineering design has attracted substantial attention. Paul Nightingale reviews the results of this attention and their relevance for a better understanding of engineering. Inside philosophy, such a better understanding may be created by analyzing the role of practical reasoning in engineering. In his chapter, Jesse Hughes explores ways in which this role may be conceptualized. Research on artifact epistemology does not, at the moment, show the vitality of research on artifact ontology, neither in philosophy nor in engineering. However, the contributions make clear that there is room for substantial growth. All papers make clear that philosophers and engineers have only begun to develop an appropriate ontology and epistemology for the realm of artifacts. All papers make specific suggestions for further research, from developing an account of artifact functions that is fully adequate to the complicated phenomenology of use and design to analyzing the role of specificationism in the reasoning of engineers. Indirectly, they shed light on issues that have to remain unexplored in this part, such as the relation between structural part-whole relations and functional decomposition, and the difference between natural and artificial objects. In combination, they also show the need for a richer account of what counts as a real object and of our standards for knowledge and its relation to action – questions that lie at the roots of the disciplines of ontology and epistemology. BIBLIOGRAPHY [Mitcham, 1978] C. Mitcham. Types of technology, Research in Philosophy and Technology 1, 229-274, 1978.


ARTIFACTS IN METAPHYSICS Amie L. Thomasson Perhaps the most striking feature about discussions of artifacts in recent metaphysics is their paucity. While attention has focused on explicating the basic metaphysical concepts at work in the physical sciences, such as space, time, property, event, cause, and law of nature, artifacts and other common sense objects have been relatively neglected by metaphysicians.1 Where artifacts have been discussed, they are often mentioned only in the contexts of arguments that we should deny that there are any such things. In short, where they are not neglected, artifacts have most often simply been rejected from metaphysical studies of what there is. The failure to study the metaphysics of common sense objects such as artifacts is unfortunate, since understanding the ontological status of artifacts is crucial to understanding the objects of concern in the social, human and technological sciences and to investigating the proper methodologies of these sciences. If that were the only problem, metaphysicians might simply be accused of benignly neglecting an important possible application of their work. I will argue, however, that the problems run much deeper than that: failure to adequately consider the problems presented by artifacts has led to important blindspots within metaphysics itself. Engaging with the problems artifacts present for metaphysics, I will argue, may lead us to rethink some of the most central problems in metaphysics and beyond, including whether various classic metaphysical problems (including problems of colocation, vagueness, and the like) require solution, how we should handle existence questions and conceive of realism, how we should understand the relation between modality and human concepts, and how the social and human sciences differ from their natural science counterparts. While consideration of artifacts is not always the only route to discovering the need to rethink these issues, it is at least a sufficient route — and that is enough to secure the significance of artifacts for metaphysics. 1

ROOTS OF NEGLECT AND REASONS FOR REJECTION

The reasons artifacts have largely been neglected in analytic metaphysics over the past century or so are not difficult to unearth. The small portion of metaphysics 1 An important exception is Randall Dipert’s [1993] detailed study of artifacts. There are also recent signs of an emerging interest in artifacts, e.g., in the work of Lynne Baker [2007] and Crawford Elder [2004], as well as my [2003; 2007a; 2007b].


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that survived the positivist assault was that dedicated to serving as the handmaiden of the natural sciences by explicating their fundamental concepts — a role that left little room for undertaking a metaphysics of artifacts and other objects of the social and human sciences. The idea that metaphysics could provide insight to what exists — not merely to our language and concepts — has, of course, been revived in post-Quinean metaphysics. But here again, metaphysics is conceived of explicitly as of a piece with the natural sciences. Following Quine, moreover, those seeking to revive metaphysics have embraced the idea that the proper methods of determining an ontology involve determining what our best scientific theories (with physics as the paradigm) must quantify over. As long the ‘best scientific theories’ are considered to include only those of the natural sciences, this method provides a more explicit ground for justifying the neglect by holding that we need not accept artifacts and other social and cultural objects in our ontology. (I will have more to say about the Quinean approach to existence questions in §2 below.) Even if we broaden our view to include the theories of the social and technological sciences, as long as one assumes that their claims may ultimately be rewritten in terms that need not quantify over artifacts, one also assumes that artifacts may be safely ignored by metaphysics. (Of course, little has been done to justify this assumption by showing how such claims might be rewritten, and the prospects for doing so across the board are dim.) Recent discussions in metaphysics, however, have done more than tacitly neglect artifacts. A large number of contemporary metaphysicians have argued, on various grounds, that we have positive reason to deny that artifacts exist. These arguments are often based on claims that accepting the existence of artifacts (or other medium-sized composite objects) would violate certain independently plausible general metaphysical principles. Thus, e.g., general prohibitions against colocation: that no two objects may be in the same place at the same time, or may share all of the same parts, are sometimes taken as grounds for denying the existence of artifacts, since an artifact and its constitutive lump of matter apparently do occupy the same place at the same time, and are made of the same parts (at some level of decomposition).2 Others have rejected them for violating Alexander’s Dictum that ‘to be real is to have causal powers’, since all the work commonly supposed to be done by artifacts allegedly may be better attributed to their microscopic parts [Merricks, 2001]. Still others have held that we must deny the existence of artifacts to maintain the metaphysical view that there is no vagueness in the world (since it seems that, if our artifact concepts refer to any objects, they must refer to vague objects);3 or to preserve the idea that there is a uniform principle of composition, determining (in any situation) when various objects compose 2 Indeed Merricks [2001, pp. 40–42] goes further, urging that troubles with colocation give us reason to deny the existence of artifacts and of their constitutive lumps of matter. 3 Horgan [1994], for example, uses this approach to argue that — at least where strict semantic standards are in place — we have reason to deny the existence of artifacts and many other ordinary objects.

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some larger object [van Inwagen, 1990; Horgan and Potrc, 2000]. Even where these metaphysical difficulties aren’t taken as direct grounds for denying the existence of artifacts, avoiding such difficulties is commonly seen as an important attraction of eliminativist views. For, as Peter van Inwagen writes, “if there are no artifacts, then there are no philosophical problems about artifacts” [1990, p. 128]. Avoiding these problems simply by denying the existence of artifacts (and their kin) prevents metaphysicians from really engaging with the most central problems that arise for making sense of our common-sense world view, and for making sense of the objects of the social and technological sciences. Moreover, if (as I argue below) we have good reason to accept the existence of artifacts, it seems that we must actually try to confront these classic metaphysical problems, giving us reason to reopen these issues and find ways to solve or dissolve — rather than merely side-step — the problems, making the serious study of artifacts again of broader relevance for metaphysics. Unfortunately, there is not space here to discuss all of these problems directly. Moreover, since these are problems that arise equally for inanimate composite natural objects (such as sticks and stones), they are not unique problems for artifacts. I have discussed these and other alleged metaphysical problems for ordinary objects in depth elsewhere [2007a]. There, I argue that we can diagnose the problems behind all of the various arguments against artifacts and other ordinary objects by accepting certain basic principles about meaning and modality. These principles in turn lead to important conclusions about the proper methods and limits of metaphysics, making a serious study of artifacts (and other ordinary objects) once again centrally relevant to our understanding of metaphysics itself. Rather than reviewing these replies in detail, I refer the reader to the detailed discussions there. Here instead, I will focus on those metaphysical problems that arise more uniquely for artifacts — or at least, for artifacts and other cultural and social objects. On the face of it, artifacts are distinguished from common-sense natural objects in that they are apparently mind-dependent, at least in the sense that (unlike rocks and trees) they would not exist were it not for the beliefs, practices, and/or intentions of the human beings who make and use them. But the apparent mind-dependence of artifacts raises distinctive metaphysical suspicions against them. Some resist the idea that there could be any mind-dependent objects, on grounds that accepting them requires positing ‘magical modes of creation’ that allow that — at least in these ‘special’ cases — human thought or intentions may bring new entities into existence. I will begin in §2 by trying to clarify the claim that artifacts are existentially mind-dependent, and showing why accepting that there are objects that, like artifacts, are existentially mind dependent does not require invoking any ‘magical modes of creation’. The purely descriptive approach to existence questions that I will recommend provides the basis for a direct argument that we should accept the existence of artifacts, and also gives reason for abandoning the standard Quinean approach to existence questions.

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Others have objected not to the idea that artifacts would depend on human intentionality for their existence, but rather to the idea that their natures might be mind-dependent. For, it is often assumed, any object that is a genuine part of our world must have a fully mind-independent nature available for discovery. I will address these arguments in §4, arguing that we have no reason to deny that the natures of artifacts are — in a sense — determined by human intentions in favor of the problematic view that they have mind-independent natures. Accepting that the natures of these kinds are determined by human intentions has important implications for understanding the differences in our epistemic relationship to the objects of the social and technological sciences on the one hand, and the natural sciences on the other, and for understanding differences in the proper methodology for each area of inquiry.

2 DEPENDENCE FOR EXISTENCE It is fairly uncontroversial that artifacts — in contrast with natural objects such as sticks and stones — are mind-dependent objects, in some sense of the term. But appeals to mind-dependence in philosophical discussions often lead more to obfuscation than to clarity, so we must tread carefully here to distinguish the sense(s) of mind-dependence that might rightly be claimed for artifacts. The most obvious sense in which artifacts may be said to be mind-dependent is that artifacts would not exist were it not for the (mental and physical) activities of humans; they are the products of human work. But even this simple point admits of at least two interpretations: there is the causal point, that the intentional activities of humans are causally responsible for the production of tables and chairs, ships and sirens. If this were the only sense of dependence at issue, it might not be of great philosophical interest, since the intentional activities of humans are also causally responsible for the production of a great many natural objects, such as the plants and animals reared in agriculture. The philosophically interesting sense in which artifacts seem to be mind-dependent is not the simple causal sense, but rather the conceptual sense: that is, the very idea of an artifact (as opposed to, say, a cow or a cabbage) is the idea of something produced by intentional human activity. So we can say that artifacts are not just causally but existentially dependent on minds, in the sense that it is metaphysically necessary for something to be an artifact that there be intentional human activities (cf. [Baker, 2007]). This distinction, however, still does not go far enough to distinguish the class of artifacts, for not just anything existentially dependent on intentional human activities counts as an artifact — for something to be garbage or pollution, it seems, it must be produced by humans (in the course of their intentional activities), but these do not — in the strict sense under discussion here — count as artifacts.4 (I leave to one side here the other use of the term ‘artifact’ to refer to unintended 4 In

Dipert’s terms, they are artificial but are not artifacts [1993, p. 33].


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byproducts of designs and the like as, e.g., spandrels are said to be artifacts of certain forms of ceiling design.) Unlike garbage and pollution, artifacts proper must be not merely the products of human activities, but the intended products of intentional human activities [Hilpinen, 1992, p. 60].5 Thus, as I have argued elsewhere, we can say that essentially artifactual concepts (as opposed to other concepts that might accidentally include artifacts in their extension), are those for which any member of the kind to be referred to must be the product of an intention to create that very sort of object [Thomasson, 2003; 2007b]. And we can begin to distinguish artifacts from other human products by way of these two features: artifacts are not merely causally but existentially dependent on human intentions; moreover, they are existentially dependent not merely on some intentional human activities or others, but rather on intentions to create that very kind of object. But the very idea that artifacts are existentially mind-dependent leads many metaphysicians to be suspicious of them, or to deny that they really exist or may be real parts of our world. Concerns about accepting the existence of minddependent objects arise from the feeling, as it is often put, that it seems too much like a ‘conjuring trick’ to suppose that our mental activities could bring new entities into existence, as if by thought or proclamation we could (shazam!) add to the inventory of being. This line of thought may be partially behind van Inwagen’s insistence that “Artisans do not create; at least not in the sense of causing things to exist” [1990, p. 127]: that is the thought that, although artisans may intentionally shove the stuff of the world about, surely it takes more than that to add to the ‘ontological inventory’ of the world.6 John Searle similarly notes qualms about accepting the existence of institutional facts, given a pervasive “sense that there is an element of magic, a conjuring trick, a sleight of hand in the creation of institutional facts out of brute facts” [1995, p. 45]. Clearly it would be a problem, and invoke ‘magical modes of creation’ if we held that thought or intentions alone could bring into existence rabbits in hats, or pink elephants on parade. And it is this kind of worry that lies behind the objections to artifacts on grounds of their (alleged) mind-dependence. But this problem does not in the least affect the claim that human intentions may be metaphysically necessary for the existence of artifacts, nor does the mind-dependence of artifacts give us reason to deny their existence.7 Why not? 5 Dipert makes a similar point in noting that artifactuality does not ‘consist in any present physical qualities of a thing’, but must in some sense appeal to its history (as having been intentionally modified) [1993, p. 15]. He also adds an additional condition for being an artifact proper: that the entity be intended to be recognized as an artifact [1993, p. 16]. 6 Van Inwagen’s central justification for this view relies on his argument that any acceptable answer to the special composition question requires us to reject artifacts. (I critically analyze this argument in my [2007, Chapter 7].) Nonetheless, he presents the view that artisans don’t really create as also intuitively plausible, and the contrary view that they do as committed to consequences about when objects are created that are ‘incredible’ [1990, p. 127-9]. 7 Baker [2007] also argues (on somewhat different grounds) that the differences between artifacts and natural objects (including the mind-dependence of the former) do not imply that artifacts are in any sense ‘ontologically inferior’.

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First, to say that artifacts are existentially dependent on human intentionality, of course, is not to say that human intentions, practices, beliefs or desires alone are sufficient to bring artifacts into existence. That would be a crazy view. While some (e.g., [Collingwood, 1938, pp. 134-5]) have defended the idea that works of art may be created merely ‘in the artist’s head’, even this implausible claim was presented to contrast works of art with the products of engineering and other ‘crafts’. For standard artifacts, especially the products of serious works of engineering like bridges, it is clear that their production requires not only an intention to make a bridge, but also some raw materials (steel, concrete), and needs to perform not only mental but also physical activities (of pouring concrete, joining spans, etc.). So, first, there is no risk on this view of considering human intentions and desires alone to be sufficient for the production of artifacts.8 Secondly and still more importantly, the trouble with the clearly problematic purported cases of ‘magical modes of creation’ (of rabbits out of hats, or pink elephants, etc.) is precisely that there are certain substantive criteria for the existence of rabbits or elephants that are not met merely by performing intentional acts, whether of imagination, invocation, desire, etc. Indeed in these cases, what any human believes, thinks, desires, or intends is quite irrelevant to the question of whether there really is a rabbit or elephant in a situation — human intentions are not even plausibly held to be a necessary condition for the existence of such natural entities. So it requires positing some form of ‘magic’ to imagine that human intentionality is in any way relevant to whether or not the application criteria for terms such as ‘rabbit’ or ‘elephant’ are fulfilled [cf. Thomasson 2007a, Chapter 9]. But the same is not the case for artifacts. If the analysis provided above is correct, then the very idea of an artifact is the idea of something that could not exist were it not for certain human intentions and practices. Thus the very idea of an artifact is the idea of something mind-dependent. If the existence of human intentions and practices of certain sorts are at least a necessary condition for a term like ‘paperweight’ (unlike ‘rabbit’) to apply, it requires invoking no magic to think that if a rock is not only physically modified (by some force or other) in certain ways, but also intentionally modified in order to serve as a paperweight, that makes the difference as to whether or not there is a paperweight in that situation. For, according to the standard use of the term ‘paperweight’, that is all it takes for a paperweight (as opposed to just a modified rock) to exist. There is a general lesson to be drawn here about the proper methods for handling existence questions.9 Metaphysicians have often proposed or assumed certain general, across-the-board, criteria for existence, e.g., that anything that exists must contribute novel causal powers [Merricks, 2001], or must be mind-independent in some sense [Lakoff, 1987, p. 184]. In each case, these are criteria that are suitable 8 By contrast, human intentionality may, as I have argued elsewhere [1999], be sufficient to produce things of other sorts, such as imaginary or hallucinatory entities — but that is an issue that need not be pressed here. 9 I argue on independent grounds in favor of this approach to existence questions in my [2007a].


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to what it takes to be a realist about things of a certain kind: e.g., it might be a legitimate requirement for there to be a basic physical entity that it contribute (otherwise unaccounted for) causal powers and be mind-independent. By considering too narrow a range of examples, these criteria are illegitimately generalized and adopted across the board as criteria for the existence of ‘anything whatsoever’. But the very idea of such entities as artifacts, institutional entities such as money or marriages, fictional characters, etc., is the idea of entities that — if there are any — are mind-dependent. We cannot use such general substantive criteria for what it takes to exist to argue against the existence of entities that (according to the very idea of such things) are not supposed to be distinctive causal contributors (e.g., beyond the causal contributions of their parts), mind-independent beings, or otherwise characterized by the preferred features. General prohibitions that would deny that anything mind-dependent can ‘really’ exist simply beg the question against the existence of artifacts (and other existentially mind-dependent objects), and do not provide any reasons for their rejection. Careful consideration of a broader range of examples suggests that there are different existence conditions for things of different kinds — with, e.g., mindindependence serving as a relevant criterion for rabbits but not for tables or fictional characters. This does not mean, however, that we cannot formulate a general understanding of how existence questions work. As I have argued elsewhere [2007a], we can gain a more neutral, and non-question-begging approach to existence questions by holding a purely formal criterion for existence: for any term ‘K’, things of kind K exist just in case the application conditions criterially associated with proper use of the term are met. On this model, existence questions (whether specific kind questions or general category questions) are to be answered in two steps: by combining an analysis of the basic application conditions for the term in question with empirical inquiry into whether or not these conditions are (ever) fulfilled. The relevant conditions may vary for different types of entity, so, e.g., the conditions under which there is a rabbit will be very different from those under which there is a paperweight, a dollar bill, or a story. Those conditions appeal to human intentionality in some cases, but not in others: so human intentionality of certain forms may be necessary and sufficient for the existence of a story or an imaginary object, merely necessary for the existence of an artifact, and completely irrelevant to the existence of a rabbit.10 But in each case, provided the relevant criteria are met, we have no reason to deny the existence of the relevant objects. In the case of artifacts, the application conditions for terms like ‘table’ are apparently are satisfied by the circumstances in my dining room and millions of others around the world. So if we combine the basic facts about meaning with obvious empirical facts, we can conclude that there are tables [Thomasson, 2001; 2007a, Chapter 9].

10 In my [1999] I lay out a system of categories, divided according to whether and in what sense(s) purported entities of various kinds would depend on spatio-temporal entities and on mental states.

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But this way of establishing existence claims is again often criticized as invoking a form of magic. Thus, e.g., Stephen Yablo writes of attempts to prove the existence of abstracta from a priori or empirically obvious premises, that such “arguments are put forward with a palpable sense of daring, as though a rabbit were about to be pulled out of a hat” [Yablo, 2000, p. 197]. “Our feeling of hocus-pocus about the ‘easy’ proof of numbers (etc.) is really very strong and has got to be respected” [Yablo, 2000, p. 199]. Such name-calling does not get us very far, however. Why think that there is anything ‘magical’ about the idea that existence questions are to be answered by way of determining what (according to the basic meanings of the terms in question) it would take for there to be an entity of the relevant kind (establishing the a priori premises), and then determining whether those conditions are fulfilled (adding the empirically obvious premises)? Instead of invoking ‘magic’, this seems like a perfectly flat-footed assessment of the truth conditions for existence claims uttered in English. As Michael Beaney notes in another context: “Rabbits can only be pulled out of hats if they are already there” [2007, p. 203]. The conditions it takes, for example, for there to be tables (which are perfectly well satisfied by there being stuff intentionally arranged into a certain firmness and shape, suitable for supporting plates and tea-cups, and for average sized humans to use for dining) are obviously satisfied — and given those conditions, it requires no magic, but only minimal observation and grasp of the application conditions for the English word ‘table’ to conclude that there are tables. This conclusion is of relevance to the study of artifacts, since it gives us reason to hold (against a multitude of recent metaphysical arguments) that there are artifacts. But it also suggests how considering the case of artifacts may be of much broader relevance to metaphysics. For, first, if there are artifacts, we cannot simply sidestep the various metaphysical problems of artifacts (to do with colocation, causal redundancy, vagueness, etc.), but must face these upfront. Second, it involves an approach to existence questions that is far different from the familiar Quinean paradigm, as well as from other approaches that take fulfillment of certain standard substantive conditions — be they mind-independence, causal efficacy, or whatever — as across the board conditions for things of any kind to exist.

3 REAL ESSENCES AND THE NATURES OF ARTIFACTUAL KINDS Quite a different sense of mind-dependence from the existential mind-dependence discussed above has recently come to play a central role in debates about artifacts: the sense in which the natures of artifacts (rather than their mere existence) might turn out to be dependent on human concepts (or thought, or language). But what is meant by this, and what is its relevance for debates about artifacts and broader issues in metaphysics?


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The basic idea comes from the direct reference approach to issues about reference and necessity developed by Kripke [1980] and Putnam [1975; 1977]. On that approach, natural kind terms are held to refer directly (via a causal and/or historical relation to a sample) to ‘that kind of thing’, so that we may first refer to a genuine kind in the world, and then go on to investigate its nature. On this view, the essential features of gold, say, are features we may discover only by empirical inquiry; they are not features that our very concept of gold establishes as necessary for anything to count as belonging to that kind. As a result, whatever beliefs competent speakers may have about the nature of gold — about what it takes for there to be gold, or when gold is created or destroyed, etc. — may all turn out to be false; and indeed there may be truths about the nature of gold even if everyone is ignorant of them. At least at first glance, however, artifacts do not seem to have real natures in this sense. Even if we may discover what it takes for something to be gold or to be a tiger, it seems to be our concepts or use of language that determine what counts as a pencil, a coffee table, or a split-level. As Stephen Schwartz put it “What makes something a pencil are superficial characteristics such as a certain form and function. . . They are analytically associated with the term ‘pencil’, not disclosed by scientific investigation” [1978, p. 571]. Considerations such as these led some (e.g., [Schwartz, 1978; 1980]) to hold that, however compelling direct reference theories and their consequences for a conception of ‘real’ essences’ might be for natural kinds, such theories cannot hold across the board as they, e.g., do not apply to artifactual kinds. Artifactual kind terms, Schwartz held, refer via associated descriptions that establish what it takes for there to be a pencil in a certain situation, for a pencil to survive or be destroyed, etc. As a result, such kinds have only a nominal essence established analytically by the criteria we associate with the term and discoverable merely by reflection on speakers’ use of the term ‘pencil’, not a real essence discovered via empirical investigation into the kind in question. Claims such as these generated a great deal of discussion about whether or not artifactual kind terms could be understood on a direct reference model, and correspondingly about whether artifactual kinds themselves have real or only nominal essences. Hilary Kornblith [1980] and James Nelson [1982] argued that, although it is true that many common artifactual kinds cannot be thought to have hidden underlying natures analogous to those of chemical kinds, they should nonetheless be thought of as having real natures of a different sort. As Kornblith puts it “What serves to determine the underlying nature of an object depends, in part, on what kind of object it is” [1980, pp. 111-112]. In the case of artifacts, Kornblith suggests, “At least for the most part, it seems that what makes two artifacts members of the same kind is that they perform the same function” [1980, p. 112]. If we allow that artifacts do have real natures in this sense then, Kornblith argues, we can preserve the idea that direct reference theories apply across the board: in each case, the term refers to whatever has the same nature as members of the relevant sample, where speakers may all be in ignorance or error about what this nature is.

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The insights behind Schwartz’s argument, Kornblith suggests, are simply based on the fact that the sort of nature in question may differ for artifactual kind terms versus (say) chemical kind terms. On closer examination, however, this line of response suggests a much deeper worry about whether or not a thoroughgoing direct reference approach is tenable at all. For suppose we accept Kornblith’s suggestion that artifactual kind terms and chemical kind terms both refer directly to ‘whatever shares the same essence as is instantiated by all or most members of this sample’, merely adding the amendment that artifactual kind terms will pick out whatever shares the same functional essence while chemical kind terms will pick out whatever shares the same chemical essence. Which sort of essence is in question will make all the difference as to which entities are unified as being of a kind, and which are not — according to whether they share a function with other members of the kind, a chemical structure, or both (or neither). But what determines whether or not a term, the reference of which we wish to ground, is to pick out entities that share the same function as these or entities that share the same chemical structure as these (or the same physical structure, shape, legal status, and so on — as presumably there may be kinds whose essences are unified in all these various ways)? It seems that here we must appeal to the intentions of speakers regarding whether the term they intend to ground the reference of is to be a term for an artifactual kind or chemical kind (or physical kind, institutional kind, etc.). In short, as soon as we consider artifactual kind terms alongside terms for (various sorts of) natural kinds, direct reference theories are confronted with a formidable problem in determining whether and if so to what sort of kind our terms refer, given that we may intend our kind terms to refer to a wide range of different kinds (physical, chemical, biological, artifactual, institutional, aesthetic. . . ).11 This is the notorious qua problem faced by direct reference theories. In light of this qua problem, many have argued (e.g., [Devitt and Sterelny, 1999; Thomasson, 2007a, Chapter 2]) that we have reason to give up pure direct reference theories in favor of a hybrid theory of reference. Such hybrid theories allow that our kind terms at least have a basic form of conceptual content specifying the category of kind to be referred to (e.g., artifactual, chemical, biological, institutional. . . ) by establishing what sorts of features are and are not relevant to unifying the kind (e.g., sameness of chemical structure versus sameness of function). If we accept such a hybrid theory of reference, then we must accept that speakers’ intentions establish at least what category of kind each general term refers to (if it refers at all), and what sorts of features (though perhaps not what particular features) will be essential or accidental to members of the kind (cf. [Thomasson, 11 Again, considering artifactual kind terms is not essential to uncovering the qua problem, since considering any range of different types of kind terms would do. Nonetheless, failure to notice the qua problem initially may have resulted from considering too narrow a range of examples, and considering problems of the reference of artifactual kind terms as well as natural kind terms is sufficient for raising the problem, and has in fact played a key role in critical discussion of direct reference theories.


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2007b]).12 So, e.g., on this view speakers’ intentions establish that ‘gold’ is to be a chemical kind term, so that gold, if there is any, has its chemical structure essentially. Speakers can nonetheless be entirely in ignorance or error about what the precise chemical structure of gold actually is. On this view, human concepts are relevant to the natures of the kinds they refer to, at least insofar as the intentions of speakers who ground the reference of terms determine what category of kind is to be referred to by establishing what sorts of features are and are not essential to belonging to the kind.13

4

DO ARTIFACTUAL KINDS HAVE MIND-INDEPENDENT NATURES?

The idea that artifactual kinds have some sort of nature, enabling us to refer to whatever shares that nature, leaves open the question: what sort of nature do artifactual kinds have? Given the above approach to reference, we can ask this by asking what sorts of feature those who ground the reference of artifactual kind terms treat as essential to unifying members of artifactual kinds (as opposed to chemical or biological kinds). Many have argued that, although artifacts are brought into existence by humans, they still may have natures that are every bit as mind-independent and open to ignorance, error, and discovery, as the natures of chemical and biological kinds are. As mentioned above, the chief suggestion along these lines has been that artifactual natures are at least largely distinguished by sameness of function rather than by sameness of chemical or genetic structure [Kornblith, 1980, p. 112]. Nelson similarly suggests that the essence of artifactual kinds may be a matter of a shared ‘structure and function’ [1982, p. 363]. The most sophisticated view along these lines has been recently developed by Crawford Elder [2004], who argues that at least a great many artifactual kinds are ‘copied kinds’ possessing discoverable ‘real’ natures, comprised of a cluster of properties centered on three shared features: 1) shape or qualitative makeup 2) proper function (members are produced by a mechanism that copies them from 12 Some have the intuition that speakers may also be wrong about, e.g., the very category of entity referred to (e.g., about whether kangaroos are animals). I discuss this objection in my [2007a, p. 48-53]. 13 This view is sometimes thought to express a form of modal conventionalism — the view that “The essential status of essential properties is mind-dependent” [Elder, 2004, p. 8]. And modal conventionalism, in turn, has been widely rejected since it is generally thought to lead to a form of anti-realism [Elder, 2004; Rea 2002]. As a result, debates about the natures of artifacts have played a central role in rekindling debates about the status of basic modal truths and essences generally, and thus again proven of wider relevance to fundamental issues in metaphysics. But since this debate about modality would take us far afield from the particular issues concerning artifacts, I will not discuss it further here. Instead, I refer the reader to the criticisms of modal conventionalism in [Elder, 2004] and [Rea, 2002], and to my extensive arguments that the view of modality that follows from the above hybrid view of reference does not commit us to any form of anti-realism [2007a, Chapter 3]. I now think that it is best to consider the allied view of modality a form of expressivism about modality — a view I develop and defend elsewhere [forthcoming].

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previous similarly shaped members as a consequence of the previous members’ performance of certain functions) and 3) historically proper placement (being where there are also tokens of other specific artifactual kinds — as, e.g., screwdrivers must be located with slotted screws [2004, p. 137]). According to Elder, we can see that these form a core essential nature by seeing that they cluster together in such a way that whenever two of them are found together, the third is there as well, and whenever one departs, one (or both) of the other two depart [2004, p. 139-140]. In most cases, Elder holds, many other properties cluster around this central core as well, making a richer, more interesting nature of the kind available for discovery. One of the acknowledged limitations of Elder’s ontology is that it does not seem able to include, e.g., merely decorative items for personal ornamentation (such as neckties and nose rings). These are not plausibly construed as members of copied kinds, since they don’t have a characteristic shape the replication of which is due to something previous members could do in virtue of that shape [2004, p. 158], so a great many apparent artifacts of these sorts must be treated as ‘mere projections’. Worse still is the situation for quite ‘general’ kinds of artifacts, such as tables and chairs. For, as Elder again acknowledges, “kinds as broad as chairs and tables can barely be said to have any one ‘shape’ or qualitative character in common at all” [2004, p. 149], and so shape cannot be part of a cluster of essential properties at the core of the kind’s discoverable nature. The problem is not limited to decorative and highly general kinds, however. Practical artifacts like paperclips and corkscrews ought to present something like the best-case scenarios for treating artifacts as copied kinds, but even these lack a consistent shape. A corkscrew may be a delicate t-shaped contraption of wood and metal, a giant handle with internal metal spiral attached by a vice to a bar, a flexible two-pronged puller, an air pump, etc. Paperclips may be elongated ovals, triangles, rectangles, or of any of a great many other shapes, colors, and materials. So again, we seem to lack a copied kind here if those must be characterized by the three clustering essential properties of proper function, shape, and historically proper placement. If there are no tables or chairs, paperclips or corkscrews (certainly no paperweights!), this does not seem to be the route to defending an ontology of artifacts. Elder responds by suggesting that what is preserved is more narrowly construed artifactual kinds (which may have the tightly clustering properties characteristic of copied kinds) such as the 1957-design Eames desk chair, or presumably the gem paperclip, or the ‘jumping jack’ corkscrew. In fact, though, on closer inspection, it seems that Elder cannot even defend the existence of these specific artifactual kinds as being copied kinds in his sense. For there to be a copied kind, recall, there must be three types of property: proper function, historically proper placement, and shape, such that these cluster together in virtue of laws of nature, so that whenever one goes, at least one of the other two goes as well. But the very presence of these widely varying design kinds for chairs, corkscrews, paperclips, and the like (which makes it implausible to defend the existence of very general


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artifactual kinds) also seems to demonstrate that shape regularly fails to cluster in this way with function and historically proper placement. The corkscrew, let us imagine, traditionally had the characteristic ‘shape’ of a rigid spiraling piece of metal (of perhaps 1-2 inches) attached perpendicularly to a solid cylinder of about a half-inch diameter and three inches length. Its historically proper placement would have been in contexts where cork-sealed wine bottles were in use, and its proper function obviously would be to open the bottle — with success at that function leading to the production of further members of the kind. But if corkscrews — or even T-corkscrews — form a genuine copied kind, then when we change the shape, one or both of the other core essential properties (historically proper placement or proper function) should go with it [2004, p. 139]. Suppose the shape is changed to a novel design: the two-pronged corkscrew, with two parallel straight flexible pieces of metal about an inch apart, attached to an empty oval on its side, and reproduce these so that they too may be said to have the proper function of opening wine bottles. Clearly this retains the historically proper placement and proper function of the original, despite the drastic change in shape. So contrary to our original supposition, there is not a copied kind characterized by those three essential features — it is simply not the case that “the absence of that third property would require the absence of one or another of the first two properties” [2004, p. 140]. This sort of problem is not unique to cases like corkscrews. A careful study of the history of artifactual kinds shows that function and historically proper placement vastly underdetermine the ‘shape’ of most of our familiar artifacts — function and placement may jointly set up an engineering problem, for which there is a wide variety of solutions that may be selected for all sorts of reasons (aesthetic, economic, social. . . ). Henry Petroski makes precisely this historical argument based on a number of case studies in The Evolution of Useful Things, where he argues extensively against the na¨ıve view that ‘form determines function’ in artifacts [1992, p. 20] — he would clearly also reject the idea that function and historically proper placement determine form. Even the simple paperclip has been made in an enormous variety of shapes and materials, which have changed without change of the function (clipping papers) or placement (where papers are used) of these artifacts. Having run through a history of paperclip designs, Petroski writes: There are still other styles of paper clips offered by other companies. . . and the variety reminds us not only of the nonuniqueness of form for this object but also of the fact that nontechnological (and subjective) factors such as aesthetics can account for the competitive dominance of one particular form over functionally superior forms [1992, p. 74]. Even the Eames desk chair will share proper function and placement with all other desk chairs, though these vary widely in shape, showing that shape may vary independently from the latter two, and so undermining the claim that there is a copied kind here at all — even if we try to make it specific. To say that, where the shape does so vary, we don’t have an Eames (1957) desk chair (since that very

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shape is an essential feature of that kind) is not a response available to Elder, since for him we must discover where clustering uniformities hold to discover what is essential, not (e.g.) infer that shape is essential from the fact that “Eames (1957) desk chair” is a design-specifying term.14 It is of course open to Elder to say that if these (general and specific) artifactual kinds cannot be identified with copied kinds (and thus shown to have real natures), so much the worse for artifactual kinds; we should speak instead of whatever copied kinds we may identify. But that would be to renounce all claim to preserving an ontology of artifacts; it would only give us a replacement ontology of biological and pseudo-biological kinds. Given these difficulties for the cluster view, it would be wise to reconsider the simpler view that the natures of artifactual kinds are just based in a shared function. But what notion of function is at issue here? It seems that it cannot be the actual functional capacities of the objects in question that are relevant. First, that would make every object a member of far too many supposedly artifactual kinds; second, it would rule out broken or malformed can openers (say) from being members of the relevant artifactual kind. So it seems instead that it must be something like ‘proper function’ in Millikan’s [1993] sense that is relevant — that is, the same sense of ‘function’ at issue in Elder’s proposal, though treated on its own, not as one of cluster of properties that jointly make up the core of an artifact’s nature. On Millikan’s account, a proper function may be either ‘direct’ or ‘derived’; a thing’s direct proper function (roughly) is what its ancestors did that led to their reproduction (and thus to this thing’s production); a thing has a derived proper function if it is the product of a prior device that has the relevant proper function and normally performs it by producing something like this. As items created by our own intentional needs, desires, and plans, artifacts would seem to have derived proper functions — so, as Millikan herself notes, artifacts have as derived proper functions ‘the functions intended for them by their makers’ [Millikan, 1999, p. 205]. Beth Preston [1998, p. 245-7] pursues the other account of artifactual proper function, arguing that artifacts may be understood to have direct proper functions in much the same sense as biological entities do. An artifact kind’s proper function, she argues, is whatever those artifacts actually do that leads to their reproduction. But a problem arises here since the reproduction of artifacts (unlike organisms) must be mediated by humans who believe the ancestral artifacts to have performed some useful function and intend the new ones to do the same. So, 14 A

further problem arises with treating the kind Eames (1957) Desk Chair as a copied kind: Clearly, according to ordinary use and application of the term, this is at least in part a historical kind. If something is to be an Eames (1957) desk chair, it must be modeled after the design by Charles Eames, created in his factory or its licensed heirs, etc. If I place an ad on Ebay to sell an Eames (1957) desk chair but am selling a chair of similar design unauthorizedly produced by a factory in China, I may be sued. So Elder’s model of copied kinds also cannot account for the historical element in many artifactual and common sense concepts — indeed Elder explicitly denies that such historical factors as origin may play any role in membership in real kinds [2004, pp. 155-6].


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for example, various superstitious and religious artifacts are reproduced because they are thought to increase rain, aid fertility, avert natural catastrophes — even if they do no such thing. Preston argues that in such cases the proper function of members of the artifactual kind in question is whatever they actually do — not what they are intended or (perhaps mistakenly) believed to do — that leads to their reproduction. We could interpret this in two ways: we could either find something the various religious objects actually do apart from altering people’s beliefs about what they do (the usual suggestion is something about aiding social bonding or providing psychological comfort) — but then we would end up classifying all manner of different religious artifacts, social objects, art objects, etc. together, contrary to the practices of classifying artifacts that are central to those social sciences like anthropology and archeology that actually deal with artifacts. Or we could treat their proper function more narrowly, saying that their function is to cause the belief that, e.g., rain will be brought (rather than to cause rain). But if it is the causal relation to our beliefs that is genuinely central to artifacts’ function (since it is these beliefs, not performance, that is relevant to their reproduction), then it seems by the same token we should hold that the proper function of can openers is not to open cans, but to cause beliefs that cans are open, and so on — a very counterintuitive result. In any case, even if we could successfully understand the function of artifacts along these lines, Maarten Franssen [2008] and Peter Kroes [2007] have recently pointed out that no such account of artifactual function can be used as the basis for distinguishing artifactual kinds, as any such attempts would be circular. Theories of (direct) proper function for artifacts aim to identify the proper function of artifacts of a given kind, e.g., of bug zappers or corkscrews. To determine the proper function of an individual (token) artifact, we must ask what its ancestors (or predecessors) did that led to their reproduction. What are an artifact’s ancestors, however? They must be previous members of the same kind (e.g., previous bug zappers or corkscrews) — but this presupposes a categorization of entities into artifactual kinds, a categorization that cannot itself be grounded on sameness of function.15 For all of these reasons, it seems better to hold that the notion of artifactual function that is relevant to our standard ways of delineating at least many artifactual kinds is in fact a notion of intended function (or Millikan’s derived proper function), not (direct) proper function in Preston’s sense. For we can discuss the intended function (unlike the proper function) of an individual (token) artifact without appeal to the kind it belongs to, and can similarly avoid the problem of misclassifying artifacts that fail to perform their intended function, or characterizing all artifacts as having the function merely of producing certain kinds of beliefs in us.

15 This is not in itself a criticism of the way in which proper function theories for artifacts are drawn out (e.g., by Preston) — only of the further idea that proper functions so understood could be used as the basis for unifying and distinguishing artifactual kinds.

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Paul Bloom’s [1996] discussion, however, suggests that even intended function would be too narrow to serve as the feature that unifies artifactual kinds, since there may be members of artifactual kinds that are not intended to fulfill the characteristic function of the kind (say, if they are intended only ‘for show’), and since some artifactual kinds such as art kinds may fail to share any common function (with some intended for decoration, others to provoke political protest, others to make money or add to the glory of the artist, others to express emotion. . . ). So while the appeal to intended (rather than performance) function may have been on the right track, sticking to functions (however understood) may be too narrow, and we may do better to pursue an account of artifactual kinds based more broadly on an appeal to intentions. Along these lines, Bloom suggests that artifact kinds are unified in terms of being products of the same sort of intention: namely, to create something of the same kind as current and previous members of the kind [1996, p. 10].16 I have argued elsewhere [2003; 2007b] that being the product of the same sorts of intentions is indeed relevant to unifying artifactual kinds, but that these intentions must be understood substantively — not just as intentions to make ‘one of these’ (referring transparently to members of a sample), but rather as intentions to make something possessing certain features considered relevant to kind membership. More precisely, I have argued, for any essentially artifactual kind K, something is a K only if it is the product of a largely successful intention to make something of kind K, where such intentions must involve a substantive conception of what sorts of features are K-relevant and this conception largely matches that of some group of prior makers of Ks (if there are any). So, for example, what is crucial to being of the artifactual kind knife is to be the product of intentions to make a knife, where these involve a substantive conception of what properties are relevant to being a knife (say, having a sharp blade and handle, along with capability of cutting standard foods) and where the intention to make something with these features is largely successfully realized. On this model, human concepts and intentions are relevant to the natures of artifactual kinds on two levels. First (as I have argued in §2), speakers’ intentions establish what category of kind is to be referred to by establishing what sorts of features are relevant to unifying artifactual kinds in general (and thus are the sorts of features that will be essential to those kinds). Second, since the sorts of features appealed to involve the intentions of those who make the objects in question, makers’ concepts of what specific features are relevant to belonging to the kind establish which particular intended features are relevant to membership in a given artifactual kind and thus may be said to form the particular nature of the kind [cf. Thomasson 2007b, p. 63]. On this view, then, the particular natures of artifactual kinds are constituted by makers’ intentions regarding what particular features are relevant to kind membership, thus marking an important difference between artifactual kinds and natural kinds. 16 For criticisms of Bloom’s account of how we categorize objects into artifactual kinds, see [Malt and Johnson, 1998].


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This view leaves it open that the most important intended features for most artifactual kinds may be functional features — capturing what seemed right about functional accounts of artifactual natures. But it also leaves it open that other intended features may (also or instead) be relevant to membership in an artifactual kind, including intended structure/design, intended use-practices,17 intended reception, etc. In fact, this account leaves open the possibility of distinguishing different sorts of artifacts, according to which sorts of intended features are relevant to kind membership. Thus, e.g., Peter Kroes [2007] suggests that intended function and structure are what is centrally relevant to technical artifact kinds; Jerrold Levinson [2007] has argued that intended reception (i.e. being intended to be regarded or treated as earlier works of art were correctly regarded or treated) is what is centrally relevant to membership in the kind ‘work of art’, etc. Accepting that the natures of artifactual kinds are constituted by human intentions in this way is highly controversial, however. For it is often held that possessing a nature that is entirely independent of human concepts, language, etc., which is open to genuine discovery and about which everyone may turn out to be ignorant or in error, is a central criterion for treating kinds as real or genuine parts of our world [Elder 1989; Lakoff 1987]. So, for example, George Lakoff assumes that any metaphysical dependence on human intentionality vitiates a purported entity’s claim to reality, taking it to be a central feature of objectivism that “No true fact can depend upon people’s believing it, on their knowledge of it, on their conceptualization of it, or on any other aspect of cognition. Existence cannot depend in any way in on human cognition” [1987, p. 164]. Crawford Elder similarly writes, “I shall myself construe realism as a denial of epistemic privilege” [1989, p. 440], namely that: . . . for any component of the world and any set of beliefs about that component, the mere facts that those beliefs are (i) about that component and (ii) are held by the particular believers, by whom they are held, never by themselves entail that that set of beliefs is free from massive error. [1989, p. 441] As a result of this view, he holds that: Realists. . . must either argue that members of a given culture could in fact hold shared beliefs about their own CGKs [culturally generated kinds] that were massively mistaken, or else maintain that CGKs are not genuine components of the world. [1989, p. 427] Indeed it was this line of reasoning that prompted Elder [2004] to defend the existence of (at least many kinds of) artifacts by arguing that we can understand artifactual kinds as possessing mind-independent natures — discoverable in much the same way as the natures of natural kinds are. 17 Pieter Vermaas and Wybo Houkes [2006] have brought out the importance of what they call ‘use plans’ to the nature of artifacts.

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But if we accept the idea that the existence of members of any kind requires that that kind have a completely mind-independent nature regarding which everyone may be totally ignorant or in error, then we would be forced to deny the existence of artifacts if we can’t defend the view that they have mind-independent natures. Given the difficulties that have surfaced above for such views, we seem to be left with the options of giving up an ontology of artifacts and artifactual kinds or giving up the idea that possessing discoverable mind-independent natures is the central criterion for ‘really’ existing. I prefer to take the latter route, holding that although the natures of artifactual kinds turn out to be mind-dependent in ways the natures of natural kinds do not, this should not lead us to deny that artifacts and artifactual kinds are real parts of our world: we should reject this criterion for realism, not the objects that apparently fail to meet it. And indeed the discussion from §2 above gives us principled reason for taking this route. For proposals for ‘across the board’ conditions for ‘really’ existing, whether these include existential mind-independence, or, as in this case, possessing purely mind-independent discoverable natures, merely beg the question against entities like artifacts which, if they exist at all, must both depend existentially and for their natures on human intentionality. Instead of adopting a substantive across the board criterion for ‘real existence’, as I argued above, we should accept a purely formal criterion for existence: for any term ‘K’, things of kind K exist just in case the application conditions criterially associated with proper use of the term are met. If we follow this criterion, we can of course accept that there are corkscrews, tables, chairs, and other artifacts — even if the substantive features relevant to being a member of these artifactual kinds are established by the beliefs and intentions of makers about what substantive features are relevant to kind membership. 5

THE NATURAL SCIENCES VERSUS THE SOCIAL AND HUMAN SCIENCES

Although, on my view, the fact that the natures of artifactual kinds are minddependent in the above sense does not make a difference to whether or not we should accept that these kinds (and their members) really exist, it does make a substantial difference to our epistemic relation to artifactual kinds, and for understanding differences in method between the natural sciences on the one hand, and the social and human sciences on the other hand. Epistemically, it follows from the proposed view of artifactual natures that at least some humans have a much closer epistemic relation to artifactual kinds than anyone has to natural kinds. For as I have argued [2003], for any essentially artifactual kind K, something is a K only if it is the product of a largely successful intention to make something of kind K, where such intentions must involve a substantive conception of what sorts of features are K-relevant and this conception largely matches that of some group of prior makers of Ks (if there are any). Since the substantive features that are relevant to being a member of kind K are


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established by makers’ conceptions of what sorts of features are K-relevant, the makers of the kind of artifact in question cannot all be massively mistaken about what it takes for there to be a K; their views on the matter are constitituve of what it takes — at least for Ks of that time and tradition. (Gradual changes over time in what features are K-relevant are possible, given that the requirement is only that the substantive conception largely match that of some prior K-makers, if any there be.) By contrast, no one has any protection from error in their beliefs about what specific chemical or biological structures are relevant to belonging to a natural kind like gold or tiger.18 This in turn has interesting consequences regarding the methodologies suitable for the natural sciences as compared with the social and human sciences. At least where questions about the nature of a kind are concerned, those scientists studying natural kinds can do best at finding out, e.g., the specific chemical nature of gold or biological nature of tigers by directly studying gold or tigers.19 By contrast, an archeologist attempting to determine the nature of a kind of artifact she has apparently discovered must do more than investigate the objects unearthed: she must attempt to determine what they were made for — what the objects’ makers would have considered the relevant features determining whether or not the objects belonged to the relevant kind, and so on. In short, she must also try to unearth — by study not only of the artifacts in their historical setting, but also using background understanding of the culture and peoples in question — what features were considered to be essential to membership in that artifactual kind. Discussion of methodology in the social sciences has often focused on the question of whether or not some form of empathy or understanding (Verstehen) must play a role in the social sciences that is not needed in the natural sciences. Humanists (e.g., [Stein, 1917; Dray, 1957/1994; Geertz, 1983/1994]) have argued that the social sciences do require some way of grasping others’ beliefs, intentions, or way of seeing the world, while naturalists (e.g., [Hempel, 1942/1994]) have denied this. The above result provides at least one sense in which it is true that something like empathy must play a central role in the methodology of the social and human sciences that is unnecessary to the natural sciences, since even identifying the natures of the artifactual kinds that play a role in such sciences as archeology and art history requires understanding the intentions and concepts of others. Similarly, it seems that something like empathy must play a role in the engineering sciences, insofar as designers intend to design something of an extant artifactual kind — or, for that matter, hope to create something of a new kind designed to fulfill certain practical purposes of users other than the maker.

18 For more detailed discussion of the sorts of epistemic privilege that do and do not follow, see my [2003]. 19 Given the above hybrid view of reference, however, the same does not go for discovering what category of kind (e.g., chemical, biological) ‘gold’ and ‘tiger’ are to refer to, should they refer at all. That much must still go by way of a form of conceptual analysis.

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6

CONCLUSION

It is undeniable that issues concerning artifacts have been largely neglected and have played only a peripheral role in mainstream analytic metaphysics over the past century. And it is only recently, especially in discussions of theories of direct reference, modality, and especially in arguments for various forms of eliminativism about ordinary objects, that discussions of artifacts have occasionally come to the forefront. I have been at pains to argue, however, that this disinterest in artifacts, and the ‘one-sided diet of examples’ (to borrow a phrase of Austin’s) that has characterized a metaphysics focused on the natural sciences, has been problematic not just for our understanding of artifacts themselves, but also for much broader issues in philosophy. For ignoring the particular issues that arise with artifacts has led to a great many problems in metaphysics, including thinking that we can sidestep central metaphysical problems to do with causal redundancy, colocation, or vagueness by denying the existence of artifacts, and adopting substantive criteria for ‘real existence’ that (however suitable they may be for the postulates of the natural sciences) are not appropriate as across the board criteria. Even beyond metaphysics itself, careful consideration of artifactual kind terms raises important problems for direct reference theories as well as for the corresponding conceptions of real kinds, and failure to note the differences in the types of nature characterizing things of different kinds has inhibited understanding why we might require different methods in pursuing the natural sciences versus the social and human sciences. More work is needed on all these issues — in fact, given the scanty attention that has been paid to artifacts, debates about most of these topics remain in their infancy. But there is hope, nonetheless, that at least by pointing out the farreaching significance of issues concerning artifacts, their natures, and our terms for them, metaphysicians may once again turn more explicitly to consider the problems presented by artifacts, to the benefit not only of those interested in those objects, but also to the benefit of metaphysics. ACKNOWLEDGEMENTS A prior version of this paper was presented in the session ‘Technical Artefacts as Ordinary Objects’ (American Philosophical Association Central Division Meetings, April 2007). Thanks to the other participants: Lynne Baker, Crawford Elder, Wybo Houkes, Peter Kroes, and Pieter Vermaas, as well as to those in the audience, for a very helpful and interesting discussion.


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BIBLIOGRAPHY [Baker, 2007] L. R. Baker. The Metaphysics of Everyday Life: An Essay in Practical Realism. Cambridge University Press, 2007. [Beaney, 2007] M. Beaney. Conceptions of Analysis in the Early Analytic and Phenomenological Traditions: Some Comparisons and Relationships. In The Analytic Turn: Analysis in Early Analytic Philosophy and Phenomenology, M. Beaney, ed. Routledge, 2007. [Bloom, 1996] P. Bloom. Intention, History and Artifact Concepts, Cognition 60, 1-29, 1996. [Collingwood, 1938] R. G. Collingwood. The Principles of Art, Oxford University Press, 1938. [Dipert, 1993] R. Dipert. Artifacts, Artworks, and Agency, Temple University Press, 1993. [Dray, 1994] W. Dray. The Rationale of Action. From Laws and Explanation in History, Oxford University Press, 1957. Reprinted M. Martin and L. C. McIntyre, eds. pp. 173-180, 1994. [Elder, 1989] C. Elder. Realism, Naturalism and Culturally Generated Kinds, Philosophical Quarterly 39, 425-444, 1989. [Elder, 2004] C. Elder. Real Natures and Familiar Objects, MIT Press, 2004. [Franssen, 2008] M. Franssen. The inherent normativity of functions in biology and technology. In Functions in biological and artificial worlds: comparative philosophical perspectives, U. Krohs and P. Kroes, eds. MIT Press, 2008. [Geertz, 1994] C. Geertz. Thick Description: Toward an Interpretive Theory of Culture. From The Interpretation of Cultures. Basic Books, 1983. Reprinted in M. Martin and L.C. McIntyre, eds., 1994. [Heller, 1990] M. Heller. The Ontology of Physical Objects, Cambridge University Press, 1990. [Hempel, 1994] C. G Hempel. The Function of General Laws in History, Journal of Philosophy 39, 35-48, 1942. Reprinted in M. Martin and L. C. McIntyre, eds. pp. 43-54, 1994. [Hilpinen, 1992] R. Hilpinen. On Artifacts and Works of Art, Theoria 58, 58-82, 1992. [Horgan and Potrc, 2000] T. Horgan and M. Potrc. Objectivism and Indirect Correspondence, Facta Philosophica 2, 249-270, 2000. [Kornblith, 1980] H. Kornblith. Referring to Artifacts, The Philosophical Review 89, 109-114, 1980. [Kripke, 1980] S. Kripke. Naming and Necessity, Blackwell, 1980. [Kroes, 2007] P. Kroes. “Theories of Technical Functions and Artefact Kinds”. Paper presented at Central Division APA session: Technical artifacts and ordinary objects: metaphysics meets the philosophy of technology, Chicago, April 20, 2007. [Lakoff, 1987] G. Lakoff. Women, Fire and Dangerous Things, University of Chicago Press, 1987. [Levinson, 2007] J. Levinson. Artworks as Artifacts. In Creations of the Mind: Theories of Artifacts and their Representation, E. Margolis and S. Laurence, eds. Oxford University Press, 2007. [Malt and Johnson, 1988] B. C. Malt and E. C. Johnson. Artifact category membership and the intentional-historical theory, Cognition 66/1, 79-85, 1998. [Martin and McIntyre, 1994] M. Martin and L. C. McIntyre, eds. Readings in the Philosophy of Social Science. MIT Press, 1994. [Merricks, 2001] T. Merricks. Objects and Persons, Oxford University Press, 2001. [Millikan, 1993] R. Millikan. White Queen Psychology and Other Essays for Alice, MIT Press, 1993. [Millikan, 1999] R. Millikan Wings, Spoons, Pills and Quills: A Pluralist Theory of Function, The Journal of Philosophy 96/4, 191-206, 1999. [Nelson, 1982] J. Nelson. Schwartz on Reference, Southern Journal of Philosophy 20. 359-65 1982. [Preston, 1998] B. Preston. Why is a Wing like a Spoon? A Pluralist Theory of Function, The Journal of Philosophy 95/5, 215-254, 1998. [Petroski, 1992] H. Petroski. The Evolution of Useful Things, Vintage Books, 1992. [Rea, 2002] M. Rea. World Without Design: The Ontological Consequences of Naturalism, Oxford University Press, 2002. [Schwartz, 1978] S. Schwartz. Putnam on Artifacts. Philosophical Review 87/4, 566-574, 1978. [Schwartz, 1980] S. Schwartz. Natural Kinds and Nominal Kinds, Mind 89, 182-95, 1980. [Searle, 1995] J. Searle. The Construction of Social Reality, The Free Press, 1995.

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[Stein, 19171989] E. Stein. On the Problem of Empathy. 3rd Revised Edition, ICS Publications, 1917/1989. [Thomasson, 2001a] A. L. Thomasson. Geographic Objects and the Science of Geography, Topoi 20, 149-159, 2001. [Thomasson, 2001b] A. L. Thomasson. Fiction and Metaphysics, Cambridge University Press, 2001. [Thomasson, 2003] A. L. Thomasson. Realism and Human Kinds, Philosophy and Phenomenological Research 67/3, 580-609, 2003. [Thomasson, 2007a] A. L. Thomasson. Ordinary Objects, Oxford University Press, 2007. [Thomasson, 2007b] A. L. Thomasson. Artifacts and Human Concepts. In Creations of the Mind: Theories of Artifacts and their Representation, E. Margolis and S. Laurence, eds. Oxford University Press, 2007. [Thomasson, forthcoming] A. L. Thomasson. Modal Expressivism and the Methods of Metaphysics, Philosophical Topics, Forthcoming. [Van Inwagen, 1990] P. Van Inwagen. Material Beings, Cornell University Press, 1990. [Vermaas and Houkes, 2006] P. E. Vermaas and W. Houkes. Technical functions: a drawbridge between the intentional and structural natures of technical artifacts, Studies in the History and Philosophy of Science 37, 5-18, 2006. [Yablo, 2000] S. Yablo. Apriority and Existence. In New Essays on the A Priori, P. Boghossian and C. Peacocke, eds. Oxford University Press, 2000.

PHILOSOPHICAL THEORIES OF ARTIFACT FUNCTION Beth Preston

1

INTRODUCTION

Function theory is a growth industry in philosophy of biology, but artifact function has received comparatively little attention. Perhaps the most important factor contributing to this neglect is the lack — both historical and contemporary — of a philosophical literature dedicated to the study of artifacts.1 In short, there is no philosophy of material culture in general, and thus no breeding ground for theories of artifact function. Moreover, the only two areas of philosophical research that do study artifacts — aesthetics and philosophy of technology — have been largely preoccupied with questions about the aesthetic, ethical and social value of specific kinds of artifacts (art works and sophisticated, industrial technologies, respectively), and have not addressed the basic ontology and epistemology of artifacts generally. Thus artifact function has not emerged as a major topic of discussion in either of these areas, despite their interest in specific aspects of material culture. A second important factor contributing to the neglect of artifact function is the widespread perception that it is unproblematic in comparison to biological function.2 The background intuition here is that because of the uncontroversial existence of intelligent makers in the artifact case, what the function of an artifact is and how that function is established can be easily traced to the purposes of the human agents who made and/or used it. Thus if there are any theoretical difficulties, they must lie in the analysis of intention and intentional action, not in the analysis of artifacts. On such a view, anything that needs to be said about artifacts can and should be said within the confines of philosophy of mind and action. A separate philosophical account of material culture is neither necessary nor desirable. Furthermore, many authors discount the difficulty of theorizing about artifact function by making the artifact account a mere appendage of the 1 In this same vein, Randall Dipert discusses “the (non-)history of theories of artifacts” [1993, p. 7]. On his view, this (non-)history can be explained by an overwhelming focus on the “passive” aspects of cognition — the environment to mind direction — to the virtual exclusion of the “active” aspects — the mind to environment direction [1993, pp. 7-11]. 2 Paul Griffiths [1993] complains about this perception and makes an effort to show that there are some special difficulties for theories of artifact function. Mark Perlman [2004] acknowledges this perception as well, discusses some of the possible special difficulties for a theory of artifact function, and concludes that none of them is insurmountable.


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biological account, usually along the lines of ‘What I have just said about biological function goes for artifact function as well, mutatis mutandis.’ Not surprisingly, then, many philosophical theories of artifact function consist of scattered remarks, or at best a paragraph or two as an afterthought to a thorough discussion of biological function. Because of the sketchy and fragmentary nature of existing accounts, it is at present difficult to see what the central issues would be for a full-fledged theory of artifact function. And taxonomizing existing theories of artifact function is difficult for the same reason. In Section 2 I will begin to deal with the first difficulty by describing the main, characteristic phenomena of material culture for which a full-fledged theory of artifact function should account in the hope that the central issues for such a theory will become manifest by the end of this essay. In Section 3 I will try to deal with the second difficulty by describing existing theories of artifact function in accordance with an organizing theme — the role of human intentions in the establishment, maintenance and change of artifact functions. The result will not be a taxonomy, properly speaking, but it will make manifest one important network of relationships among these theories. Finally, in Section 4 I will assess the theories described in Section 3 with regard to their progress explaining the characteristic phenomena described in Section 2. This will complete the task begun there of ferreting out some of the central issues for full-fledged theories of artifact function, as well as providing some indication of the current state of the discussion with regard to these issues. 2

PHENOMENA OF ARTIFACT FUNCTION

In this section I sketch some characteristic phenomena involved in artifact function. I have not focused particularly on characteristics that are peculiar to material culture, as opposed to biology. Some material culture phenomena pretty clearly have biological analogues; others equally clearly do not. I do not claim that this list exhausts the characteristics of material culture a theory of artifact function could profitably take into account. Nor do I claim that the emphases I have chosen within this list are the only ones possible. What I do claim is that the phenomena described here constitute salient characteristics of material culture; that they make manifest central features of artifact function; and that a fullfledged theory of artifact function ought minimally to account for at least these features. My purpose here is thus to establish an initial benchmark for what would count as a full-fledged, well-integrated theory of artifact function, as opposed to the incipient, scattered fragments of such a theory that are available in the literature at present.3 Artifact functions are multiply realizable. There may be some rare functions that can only be realized by a device with a specific form made out of a specific 3 In a similar vein, Vermaas and Houkes [2003] propose a set of four desiderata they believe an adequate account of artifact function should satisfy. Lewens [2004, pp. 88-89] might be interpreted as proposing something along these lines as well.

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material — an object used for a religious ritual with very specific requirements, perhaps.4 But typically artifact functions are realizable in a variety of materials and/or forms, provided some general constraints are satisfied. Take spoons, for instance. They do have to be made out of a relatively rigid material, and have a form that includes a handle attached to a bowl. But other than that, form and material are very variable. Historically, in addition to just about any metal you care to mention, spoons have been made from wood, shell, horn, bone, pottery, porcelain, plastic, silicon, and so on. Similarly, the bowl may be oval, round, pointed or even fluted; and the handle may be long or short, flat, bowed, or looped, decorated or plain. Moreover, the multiple realizability of function means that material cultures often contain several types of things all designed to serve basically the same function. Brooms, mops, vacuum cleaners and “swiffers” are all good for cleaning uncarpeted floors, for instance. And of course the same function is often served by different types of things in different material cultures. The function of conveying solid food from plate to mouth is carried out in Japan with chopsticks and in the United States with a fork, for example. Artifacts are multiply utilizable. Some artifacts are designed to serve only one function, but more typically they are designed to serve several, often simultaneously. Uniforms, for instance, serve the functions of ordinary clothing — keeping the wearer warm, dry and modest — but they also serve identification functions with regard to telling friends apart from enemies, displaying rank, and so on. As Michael Schiffer [1992] points out, and the example of uniforms illustrates, multiple functionality is often a matter of serving social or communicative functions in addition to purely technical ones. A crucial aspect of multiple utilizability is that artifacts are usable — and very often used — for functions they were not designed to serve. For example, an umbrella designed specifically to ward off rain, can also be used as a sunshade, as a weapon, as a lampshade (hung upside down underneath a ceiling light fixture), as a cane, or as a handy extension of the arm for reaching and retrieving things (from the top of the bookcase or the other side of the park railing, for instance). These alternate functions sometimes become standardized or routine, either with regard to a particular artifact or to a type of artifact. A person might set aside a particular spoon to be used to transplant seedlings, for instance; and spoons in general are routinely used to open cocoa tins.5 This aspect of multiple utilizability is, in a sense, the other side of the coin of multiple realizability. Since a given artifact function is realizable in a range of forms and materials, it is no wonder that it can also often be performed by other artifacts originally designed to fulfill different functions. There is an important connection here to recycling, a process in which one type of artifact is used as raw material for the production of another type of artifact with a different function. Like multiple utilizability, recycling is important to an account of artifact function because it bears on the issue of change of function 4 Thanks

to Wybo Houkes for suggesting this example. [Preston, 1998] for a more detailed discussion of routine and standardized alternate functions. 5 See

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over time. Most of the examples of multiple utilizability above leave the artifact in a condition to perform its original function(s). But sometimes modifications are required. In order to use an umbrella as a lampshade, for instance, you might need to shorten the handle to the point where using the artifact as an umbrella again would no longer be feasible. A closely related phenomenon is the reuse for a new function of items that can no longer be repaired, as when old clothes are cut up for workshop rags or used as pet bedding. Further along this continuum are cases where the structure is so radically modified that the original item is no longer clearly identifiable, as when old clothes are cut into strips and braided for chair mats or rugs. And finally there is recycling proper — breaking the structure down so thoroughly that it is returned to the raw material state and can be used to produce new items, sometimes of a very different type, as when old clothes are processed to make paper.6 Multiple utilizability and recycling appear to be located on a continuum, so I am not sure whether what we have here is one phenomenon or two. But for clarity and convenience, I will reserve ‘multiple utilizability’ for cases where the artifact is still usable for its original function(s), and ‘recycling’ for cases where there is sufficient modification that the original function(s) can no longer be performed. Another central feature is that the functional structures of material culture are not merely produced but reproduced. That is, they have standardized forms and uses that are relatively stable over years, generations or even millenia. That they are only relatively stable bears emphasizing. As in biology, the process of reproduction in material culture ordinarily involves variation, whether intentional or accidental. And as in biology it is this variation that ultimately accounts for cultural innovation, which is overwhelmingly a matter of incremental changes and extensions of existing cultural forms and functions rather than a matter of radical novelty. For example, electronic computers are only the latest entry in a long historical line of calculating machines, and could have arisen only on the basis of such a line. Similarly, when a material culture does encounter radical novelty, it is usually an import from another material culture rather than a local invention. The point is not that radical novelty does not occur — although I must confess I have been unable to come up with an example that qualifies as absolutely novel — but that it is vanishingly rare. On the other hand, it is important to note that in material culture variation and innovation are sometimes promoted in ways that are not possible in biology. But the bottom line here is that innovation is not separate from reproduction, but rather integral to it. In other words, reproduction with variation is simultaneously the process by which material culture changes over time and the process by which it is preserved relatively unchanged over time — these are two sides of the same coin. In order to indicate the dual nature of this phenomenon we will call it reproduction with variation. It is integral to a theory of artifact function because what are reproduced and varied are not mere physical 6 It is important to remember here that what we call “raw” materials are usually not raw, in the sense that naturally occurring materials like clay or stones are raw, but are already artifacts. Paper and plastic are raw materials only in this latter, already artifactual, sense.


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structures to which functions are later attached, but always already functional structures. A rarely remarked group of phenomena closely related to reproduction with variation involve processes like maintenance, repair and rebuilding. These processes are integral to a theory of artifact function because the point of the maintenance, repair or rebuilding is to preserve or restore functionality. Most items of material culture must be maintained in order to continue to perform their functions effectively. Sometimes the need for maintenance is a direct result of the exercise of the function — e.g., spoons become encrusted with food during use and must be washed. In other cases the need for maintenance results from natural deterioration of the material of which the item is made — e.g., silver spoons need to be polished from time to time even if they are not used. Most items of material culture are also subject to damage that calls for repair. Sometimes this is the result of normal functioning, as when socks get holes in the toes or heels; and sometimes it is the result of misuse or accidents, as when a spoon is bent by being used to pry up floor tiles. Items of material culture more complex than socks or spoons sometimes undergo a radical type of repair, usually called rebuilding or remodeling, in which an item is disassembled, its parts refurbished or changed out for new ones, and then reassembled. Maintenance, repair and rebuilding are all intended to return the item to a condition in which it can perform its original function(s), so these processes are the complement of recycling, which has the opposite effect. Another important phenomenon associated with the functional structures of material culture is malfunction. This is something a theory of function needs to account for just as theories of representation need to account for misrepresentation. A malfunction, it should be noted, is not something going wrong with the function itself, but with the structure that subserves the function. This may be due to a variety of causes, among them misuse, manufacturing defects, poor design, accident, or simply wearing out as a result of normal use. Thus an umbrella malfunctions if a rib breaks due to long-term normal use, or if the wrong fabric was used and it is not waterproof, or if its handle is cut off so it can be used as a lampshade, and so on. An important limiting case of malfunction occurs when an artifact is well on the way to losing a particular function altogether — something like becoming vestigial in the biological realm. For example, souvenir spoons are often not at all suitable for use as spoons. They are maintained in the material culture largely because they have acquired a separate, social function as souvenirs. Finally, there is a related but little remarked phenomenon I call phantom function. This occurs when a type of artifact is regularly reproduced to serve a specific function, but no exemplar of it has ever been structurally capable of performing that function, or, in the nature of things, ever will be. Examples of phantom functions are tendentious, because there are often people who believe the artifacts in question do perform the functions they are alleged to perform. But here are a couple that should be relatively untendentious with regard to the audience for this essay. Laetrile is a drug that is produced and used in Mexico for treating cancer. It is not produced or used in the United States (or in Canada, for that matter) be-

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cause it has not been approved by the U.S. Federal Drug Administration (FDA), which believes that there is no scientific evidence of its efficacy. Assuming the FDA is right about this, curing cancer is a phantom function of laetrile. Similarly, Europe, North America and the Middle East are rife with amulets for protection against the “evil eye,” the alleged capacity of some persons to cause harm simply by looking at someone or some thing. These amulets come in the shape of “all seeing” eyes, hands with an eye on the palm, horseshoes with eyes on them, and so on.7 On the assumption that there is no evil eye, these amulets do not protect against it. In short, phantom functions appear to be normal functions in terms of the design, reproduction and dissemination of their artifact bearers, but not in terms of the performance of the function, which has never actually occurred.8 These phenomena — multiple realizability, multiple utilizability, recycling, reproduction with variation, malfunction and phantom function — can serve as an initial benchmark for a full-fledged theory of artifact function. First of all, any full-fledged theory ought to have something to say about most or all of them insofar as they all bear in one way or another on recognized issues in function theory. Second, any full-fledged theory of artifact function ought to recognize the complexities of these phenomena and the concomitant difficulties in accounting adequately for the features of artifact function they make manifest. In the next section, I will review current theories of artifact function, and then in Section 4 I will employ these benchmark phenomena to assess the state of the art and recent progress made by theories of artifact function.

3 THEORIES OF ARTIFACT FUNCTION The overwhelmingly most important theme to have emerged to date in the literature concerns the role of human intentions in the establishment, maintenance and change of artifact functions. I will use this as the organizing theme in this section. It is, I think, admitted on all hands that human purposes and intentions have something to do with the functions of artifacts. But a fairly common view is that artifact functions are directly and exhaustively determined by individual and/or collective human intentions. Such views have been termed “intentionalist,” and have been contrasted with “reproduction” views.9 Reproduction views appeal to 7 There is an archive of these amulets, historical and contemporary, at http://www.luckymojo.com/. Oddly enough, the “all seeing” eye shows up on the great seal of the United States masquerading as the “eye of providence.” See http://www.greatseal.com/, or just take a look at the back of a U.S. one dollar bill. 8 It has been suggested to me that phantom function is just a very extreme type of malfunction [Wybo Houkes, private communication]. While this suggestion does make sense on the face of it, I am nervous about accepting it. To treat phantom functions as merely the limiting case of malfunction strikes me as analogous to treating fiction as merely the limiting case of misrepresentation. And that does not seem very plausible, given the important and multitudinous positive roles fiction plays in human life. I suspect phantom function is more like fiction than like misrepresentation. 9 This is the terminology of Houkes and Vermaas [2003].


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a history of selective reproduction as the primary determining factor for artifact functions, and they regard human intentions as having a secondary or indirect role, mediated by various non-intentional factors. So at one end of the spectrum are radically intentionalist views, at the other end are views that accord human intention only a secondary role, and then there are some mixed views in between. I will start at the radically intentionalist end. John Searle [1995, especially Chapter 1] takes up the question of artifact function in the context of explaining how social reality is created by human agents. On his view, all functions, biological as well as cultural, are entirely “observer relative” [1995, pp. 9-13, et passim]. They are “assigned” [1995, p. 9, et passim], often collectively, by human agents in accordance with human purposes, goals and values. Searle distinguishes between “agentive” functions that depend on a use to which human agents intentionally put artifacts they have made for this or other purposes, and “non-agentive” functions that are mechanical, causal processes of naturally occurring objects to which human agents have assigned an extrinsic purpose or value [1995, pp. 22-23]. Thus for Searle artifact functions depend on the intentions and intentional states of human agents who impose them through the intentional use of artifacts.10 Searle takes this imposition of agentive functions on artifacts to be paradigmatically a matter of individual action, but he also allows for collective imposition. If it is easy to see how a single person might decide to use some object as a chair or a lever, then I believe it is not difficult to see how two or more people together could decide to use some object as a bench on which they can all sit or to use something as a lever to be operated by several people, rather than just one. Collective intentionality can generate agentive functions as easily as individual intentionality. [Searle, 1995, pp. 38-39] On Searle’s theory, collective intentions are not reducible to individual intentions [1995, pp. 23-25], which implies that for him some artifact functions are irreducibly social. This point connects with his discussion of a special class of agentive functions he calls “status functions,” where a function is assigned to something the physical structure of which is related only in an arbitrary way to the performance of the function, e.g., paper money. On Searle’s view, this phenomenon depends on the prior collective assignment of a special status to the object — the status of medium of exchange, in the case of paper money — which subserves specific assigned functions — in this case, the functions of paying debts, making purchases, and so on. Status functions are important for Searle because their assignment to artifacts creates what he calls an “institutional fact,” a fact which exists only through collective human agency [1995, p. 40 ff.]. Searle’s theory of artifact function is thus not only intentionalist; but in addition it foregrounds collective intentions and the social or institutional results of their operation. 10 See

[Kroes, 2003] for a thorough critique of Searle’s view.

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Randall Dipert [1993] also has a radically intentionalist account of artifact function. He begins by defining ‘artifact,’ ‘instrument,’ and ‘tool’ in terms of the intentions of their makers and/or users [1993, Chapter 2]. Not surprisingly, then, he analyzes their functions entirely in terms of agent intentions as well. It should be noted that although Dipert is clearly talking about artifact functions, he uses the term ‘artifact purpose’ instead of the more usual locution. I maintain that artifact purposes are derivative from agent purposes. . . . That is, roughly, anyone who thinks of artifact purposes has a conceptualization that unpacks into agent purposes but is not always fully conscious of this relationship. Talk and thought about artifact purposes, however, acquires a certain conceptual independence through habit of use, the present lack of conscious connection, and, of course, the fact that an artifact purpose is not “just” an agent purpose — that is, the telephone’s purpose is not the telephone’s in the way that my purpose in writing this book is mine. [Dipert, 1993, p. 237] As is clear from this passage, Dipert is very sensitive to the non-conscious nature of many human purposes and intentions, and provides for this in his theoretical framework. For instance, in discussing the intentional activity of the creators of artifacts, he says that to the extent that it is not the result of specific prior deliberation or the subject of conscious attention, it is better described as “halfintentional” [1993, pp. 50-51]. But it is also clear that Dipert, like Searle, does not allow for any other source of artifact function apart from human intentions and the beliefs and desires that contribute to the formation of those intentions. He just allows for these intentional states to operate below the level of conscious awareness. Similarly, Dipert acknowledges that these intentional states may involve “group agency” [1993, p. 126 ff.]. But unlike Searle, he does not make this a feature of his account. It is not clear whether he disagrees outright with Searle about the reducibility of collective intentions/agency to individual intentions/agency. But he argues that group agency is best considered as “virtual” individual agency for the purposes of a theory of artifacts and their features [1993, p. 32 and pp.194-5]. So for Dipert, as for Searle, the paradigm case is that of an individual agent planning and constructing an artifact with a specific purpose in mind and thereby endowing it with a function. But unlike Searle, he recommends that cases of group agency be rationally reconstructed in accordance with the individual model for purposes of analysis and theory construction. Dipert’s views appear to have been an important influence on Peter McLaughlin’s account of artifact function. McLaughlin [2001, especially Chapter 3] holds that since artifacts are not self-reproducing systems they have no functions in their own right; rather their functionality is completely dependent on the intentions of their makers and/or users. The function of an artifact is derivative from the purpose of some agent in making or appropriating the object; it is conferred on the object by


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the desires and beliefs of an agent. No agent, no purpose, no function. [McLaughlin, 2001, p. 60] These agent purposes do not have to result in the modification of material or even a change of location — an agent can appropriate an artifact for a particular purpose and thus endow it with a corresponding function simply by noticing the artifact and approving its use for that purpose — an operation McLaughlin refers to as “virtual assembly” [2001, p. 54]. But McLaughlin does distinguish between intentions that precede the actual assembly of the artifact and intentions that follow it, calling functions that result from preceding intentions “design functions” and those that result from following intentions “use functions” [2001, pp. 47-48]. McLaughlin is explicit about the epistemological consequences of this intentionalist analysis of artifact function: it is only the beliefs and desires of agents that explain the features of artifacts. The functions of artifacts, being themselves entirely derivative from agent beliefs and desires, do not explain anything. This echoes Dipert’s view that the purposes of artifacts can be unpacked as the purposes of human agents, and suggests that any discussion of artifact function is properly to be carried out under the aegis of philosophy of mind and/or philosophy of action. Another consequence of McLaughlin’s view — and one he stresses throughout his book — is that artifact functions differ from biological functions in that they benefit the user of the artifact and not the artifact itself. Unlike biological organisms, then, artifacts have no intrinsic “good” or interests of their own, but are only good insofar as they serve the extrinsic interests of human agents [2001, Chapter 9]. Thus McLaughlin definitively divorces the discussion of artifact function from the discussion of biological function by referring artifact functions and other associated features of artifacts to human intentions and other intentional states. Karen Neander [1991] also advances an intentionalist theory that distances artifact functions from biological functions, but for somewhat different reasons. On her view, the functions of biological traits are the effects for which these traits were selected over the course of evolutionary history. Similarly, artifact functions are the effects for which they are intentionally selected by human agents. Despite the quasi-biological terminology retained in the artifact case, Neander takes intentional selection to be toto caelo different from natural selection. Whereas evolved functions must be generalizable over types, artifact functions may be idiosyncratic. Unique inventions, like the additions to James Bond’s brief case, can have proper functions peculiar to them because they can be individually selected for particular effects. Also, because intentional agents do have foresight, there need be no past performances of the functional effect, nor any ‘ancestral’ artifacts to do any performing. It is enough, in the case of intentional selection, if the designer believes or hopes that the artifact will have the desired effect and selects it for that purpose. [Neander, 1991, p. 462] Thus whereas biological selection applies to types and so necessarily involves a history of reproduction, intentional selection can apply to tokens and so is in-

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dependent of the history of reproduction, if any, of the object. The intentions of designers and users are on an equal footing here as far as endowing artifacts with proper functions goes. But like McLaughlin, Neander distinguishes several sub-types of proper function related to the designer-user distinction. I suggest that the function of an artifact is the purpose or end for which it was designed, made, or (minimally) put in place or retained by an agent. . . . Since there will often be several agents involved, and each might have a different purpose, we might want to distinguish between ‘design functions’, ‘user functions’, ‘occasion functions’, and so on. The everyday notion of an artifact’s function is context sensitive, and in some contexts one intentional agent can take precedence over another. However, although the particular context might highlight the intentions of the user, rather than the designer, say, the function of an artifact is always whatever it was selected for. [Neander, 1991, p. 462] These distinctions among types of functions are not worked out in any detail by Neander, but they clearly echo McLaughlin’s distinction between design functions and use functions and hint at a somewhat more elaborate scheme of the same sort. Finally, and only in a footnote, Neander mentions that non-intentional trial and error, a process that mimics natural selection more closely than intentional selection, may be responsible for the functions of some artifacts or their components [1991, p. 462, n. 11]. This is the first break in the so far monolithically intentionalist reading of artifact functions.11 This non-intentionalist dimension is elaborated and given a more significant role by Paul Griffiths [1993]. Like Neander, Griffiths has a selectionist account of function in general; and like Neander, he thinks that “human selection does for artifacts what natural selection does for organisms” [1993, p. 419]. But unlike Neander, he emphasizes that human selection is often not intentional. Many features of artifacts make no intended contribution and yet have proper functions. In societies with low-level technologies, artifacts are often designed by trial and error over periods of many generations. The contribution that a feature makes to the performance of overall function may never be appreciated. Such features do not have an intended use but they do have functions, and they can be explained by their functions. [Griffiths, 1993, pp. 418-419] The prevalence of an artifact, or an artifact trait, can be explained by selective processes in which people meet their needs, sometimes by 11 The possibility of non-intentional selection of artifacts by trial and error is also pointed out by Bigelow and Pargetter [1987]. More recently, Mark Perlman has sounded a note of caution for intentionalist accounts of artifact function, warning that there is much evidence from psychology, sociology, and anthropology that artifacts often have functions unknown to, and unintended by, their users — and perhaps in some cases even their designers [2004, pp. 33-34]. However, he fails to note that Karen Neander and (as we shall see) Paul Griffiths both provide for this.


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conscious design, sometimes by trial and error, and sometimes by an amalgam of the two. [Griffiths, 1993, p. 419] Griffiths’s view is thus much more clearly of a mixed, intentionalist/nonintention alist type, because it is possible for cultural selection to be either intentional or non-intentional or a bit of both. Griffiths is also more at pains to spell out the analogies and disanalogies between natural and human selection. For example, natural selection requires selection between competing variations. Griffiths points out that non-intentional trial and error parallels natural selection in this respect, with competition between actual variations under actual, real world conditions. But in the case of intentional selection there is often intentional design, and here the competition may be “hypothetical,” taking place between imagined variations under imagined, virtual world conditions [Griffiths, 1993, p. 419]. Griffiths notes that in these cases fallible human designers may select something that never has and never can perform the function intended — for example, the tapered tails of early racing cars, which their designers theorized would reduce drag, but which did not in fact do that. Such cases are phantom functions, in the terminology introduced in the previous section. Griffiths points out that this can never happen in biology, since natural selection can only act on an actual performance, whereas intentional selection can act on an imagined performance. And he analyzes this phantom function phenomenon, too, in terms of hypothetical alternatives. It occurs when such alternatives are evaluated and selected in light of false beliefs or theories about the world. Ruth Millikan [1984; 1999] also appears to mix intentionalist and non-intentionalist components in her theory of artifact function, but in a different way.12 She is mainly interested in what she calls proper function — what a thing is supposed to do, and is malfunctioning if it cannot do.13 Most biological proper functions are established by a (non-intentional) history of selection and reproduction for this performance. But on Millikan’s view, some proper functions are established derivatively, through some other, already proper functional, trait. Her favorite example is chameleon skin color — when the skin color changing mechanism of a chameleon sitting on my deck turns it brown, that brown color has the proper function of camouflaging that chameleon from predators (my wretched cats, say, who are altogether too fond of lizard for dessert) even if it is a unique shade of brown that has not been specifically selected for in the course of evolution. As applied to artifacts, Millikan argues, this means that they typically have two, simultaneous sources of proper function. They have direct proper functions in virtue of their history of reproduction, just as biological traits do; and they have derived 12 Millikan is not explicit enough about certain points for this interpretation to be on absolutely sure ground. But it seems to be at least a plausible reading of what she does say, for reasons I will explain in a moment. 13 She also acknowledges another type of function she usually calls Cummins functions, in reference to a well-known article by Robert Cummins [1975]. But she does not make much use of Cummins functions for theoretical purposes in what she has to say about artifacts, although she does sometimes use artifacts as examples of things with Cummins functions (e.g., in [Millikan, 2002]).

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proper functions in virtue of the intentions of their producers [1999, p. 205]. Usually these artifactual proper functions coincide. For example, the loaves of bread you bake today have both the direct proper function of providing nourishment in virtue of the long history of similar loaves providing such nourishment, and the derived proper function of providing nourishment in virtue of your current desire for nourishment. But sometimes these proper functions diverge. For instance, if you bake bread in order to sell it rather than eat it, the loaves have the direct proper function of providing nourishment, but the derived proper function of providing income — derived from your desire on this occasion for income rather than nourishment from these particular loaves of bread. This divergence is not problematic in principle, because things often have more than one proper function at the same time. Moreover, Millikan points out, some artifacts — novel prototypes, in particular — do not have direct proper functions because they do not (yet) have a history of reproduction. So if they are to have proper functions at all, they must have derived proper functions grounded in the intentions of their designers [1984, especially Chapter 1; 1999, pp. 204-205].14 For Millikan, then, some artifacts have only an intentional source of proper function; some have only a non-intentional source in a history of reproduction (e.g., the trial and error scenario suggested by Griffiths and Neander); but most of them have both sources of proper function simultaneously.15 Pieter Vermaas and Wybo Houkes [2003] also appear to have a mixed theory, although as with Millikan, the case is not entirely clear. They begin by setting forth four desiderata they think any adequate theory of artifact function should satisfy. It should 1) distinguish proper (“standardly ascribed”) functions from accidental (“ascribed only occasionally”) functions, 2) be able to ascribe proper functions even to malfunctioning tokens, 3) entail that the physical structure of the artifact be sufficient to perform its ascribed function, and 4) provide for an ascription of proper functions to novel prototypes [2003, pp. 265-266]. They argue that only an intentionalist theory that ignores the reproductive history of artifacts can succeed in meeting all of these desiderata, and in particular only an intentionalist theory can meet the novel prototype desideratum. On the other hand, Vermaas and Houkes point out, due attention to the design process shows that designers do not form intentions about the purposes of artifacts in a vacuum — they deliberate and form beliefs about proposed physical structures of artifacts with regard to their capacity to carry out proposed functions. They criticize both Neander and Millikan for ignoring this physical structure desideratum, and propose a more satisfactory alternative.

14 For more details and a critical discussion of Millikan’s distinction between direct and derived proper functions, see [Preston, 1998]. See [Millikan, 1999] for a reply. 15 Tim Lewens [2004, especially Chapters 5 and 7] seems to have a similar idea in mind. He distinguishes between intended artifact functions and evolutionary artifact functions, and stresses that they represent different sources of function. Artifacts, on his view, may get their functions from either one of these sources, depending on the circumstances [2004, p. 166].


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Consider a theory that ascribes a function to an artefact on three conditions: (1) the designers intended to design the artefact to have the function, (2) these designers determined the phyiscal structure of that artefact on the basis of their technological and scientific knowledge, and (3) by means of this knowledge, they can provide explanations as to why the artefact with the thus-determined physical structure can perform the function. [Vermaas and Houkes, 2003, p. 287] This might well be interpreted as a pure intentionalist view, since it takes artifact functions to depend entirely on the intentional states of human agents. But the physical structure desideratum is fundamentally a non-intentionalist consideration, since it appeals to the actual physical makeup of the artifact. This is captured in the passage above by the requirement that the designers be applying knowledge, and not just engaging in wishful thinking, irrational fantasies or blatantly false beliefs. Beth Preston [2003] argues that Vermaas and Houkes fail to satisfy their first desideratum, namely, to maintain a theoretical distinction between proper functions and accidental functions of artifacts. Perhaps in response to this criticism, the authors address this distinction directly in a later paper [Houkes and Vermaas, 2004]. Their approach is to derive a theory of artifact function from a theory of artifact use and design. On our theory, an artifact function is a capacity, supposed or actual, which has a preferential status in the context of certain actions and beliefs. It is therefore a highly relational property, which supervenes on both the actual physical makeup of an artifact and on the beliefs and actions of human agents, designers as well as users. [Houkes and Vermaas, 2004, p. 67] Proper functions can be distinguished from accidental functions on this approach via a prior distinction between standard use plans and various kinds of alternative (non-standard) use plans. It is also clear from this passage that Houkes and Vermaas wish to maintain the non-intentionalist emphasis on actual physical structure they stressed in their earlier article, and this supports the interpretation of their view as a mixed intentionalist/non-intentionalist one.16 Marcel Scheele [2005; 2006] follows Houkes and Vermaas in emphasizing use as essential to artifact function, as well as in analyzing use in terms of the plans of individual or collective agents. But he emphasizes the social aspects of use to a much greater extent than they do. Indeed, he argues [2006, p. 32 ff.] that not only their theory, but also the non-intentionalist theory of Beth Preston [1998; 2003], can be improved through greater attention to the social aspects of use. Scheele [2005, especially Chapters 2 and 3] begins by accepting the now familiar distinction between the proper function and the system function of artifacts, i.e., 16 Houkes and Vermaas continue to refine and extend their theory, which they refer to as the ICE (intention/cause/evolution) theory of artifact function. See [Vermaas and Houkes, 2006; Vermaas, 2006; Houkes, 2006], for instance.

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between their standard uses and their occasional, non-standard uses. He then argues that standard uses, and thus proper functions, must be understood in terms of social institutions. Social institutions, on his view, are collective, relatively stable patterns of action that are socially enforced. Scheele [2006, p. 28 ff.] also stresses the changes that the functions of an artifact often undergo, and analyzes these changes in terms of the social roles of the designers vs. users, or of original users vs. current users. Scheele does not claim to have a distinct theory of artifact function. But he does have a distinctive theory of the social dimensions of artifact function that can be used to amend or supplement existing theories, many of which are alarmingly thin when it comes to an analysis — or even an acknowledgement — of the social character of artifacts. Beth Preston [1998; 2003; unpublished] is the only theorist to espouse a nonintentionalist account of artifact function. Her initial concern [1998] is to put forward a pluralist account.17 Artifacts, on her view, have two different kinds of functions neither of which is reducible to the other. On the one hand, they have the functions they have historically been reproduced to serve, which Preston calls ‘proper’ functions in reference to Ruth Millikan’s [1984] theory of proper function which Preston adopts and adapts. On the other hand, artifacts often are used for functions they were not reproduced to serve but are capable of serving — e.g., spoons can serve as musical instruments. Preston calls these ‘system’ functions in reference to Robert Cummins’ [1975] theory of biological function as the role a component plays in the overall context of a system. Since both Millikan’s theory and Cummins’ theory are focused on giving an account of biological function, neither is intrinsically intentionalist; and Preston resists reformulating them in intentionalist terms, as Vermaas and Houkes [2003] in effect do, for instance. In general, then, non-intentionalist theories of function maintain the distinction between proper function and accidental [system] function by reference to the distinction between the embedding of a performance in a history of selection and reproduction, and the embedding of a performance in a currently operating system. But intentionalist theories of function eschew reference to either the ancestry or the system context of an artifact, and rest everything on the intentional states of agents. [Preston, 2003, pp. 603-604] [I]ndividual intentional states do have a role to play in a non-intentionalist theory, because they are necessary for the implementation of histories of reproduction and social systems. . . . But regarding individuals’ intentional states collectively as a necessary condition for the establishment of artifact function in this way is quite a different matter from regarding the intentional states of individual agents as a sufficient condition, as the intentionalist theory. . . does. [Preston, 2003, p. 611] 17 Millikan [1989; 1999] endorses a pluralist account of function in general, although not specifically with reference to artifacts.


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Preston [2003; unpublished] argues that only non-intentionalist theories can adequately distinguish proper functions from system functions. Intentionalist theories are forced to elide the distinction because they have no adequate way of distinguishing between designers’ intentions and users’ intentions. She concedes, on the other hand, that non-intentionalist theories like her own have no way of ascribing proper functions to novel prototypes. But she argues that this is neither necessary nor desirable. Preston [unpublished] also gives a non-intentionalist account of phantom functions. It appeals to the history and the predominant patterns of use of an artifact type rather than to the designers’ intentions in a context of false beliefs, as Griffiths does in his account of phantom functions. This completes the review of current philosophical theories of artifact function.18 In the next section I will assess the state of the art as described here with regard to progress in accounting for the features of artifact function made manifest by the material culture phenomena described in Section 2. This exercise will also help bring into better focus the most important issues full-fledged theories of artifact function have to face. 4

ASSESSING THEORIES OF ARTIFACT FUNCTION

In Section 2 I described six central and characteristic phenomena of material culture that make manifest important features of artifact function — multiple realizability, multiple utilizability, recycling, reproduction with variation, malfunction and phantom function. I noted there that multiple realizability of function and multiple utilizability of artifacts are two sides of the same coin. Because any given artifact function can be realized in a number of different physical structures, not only are there different types of artifacts with the same standard function, but artifacts for which that function is not standard may be coopted to serve it on occasion. In other words, multiple realizability leads directly to multiple utilizability in the form of alternative uses of artifacts meant for some other purpose. And multiple utilizability in turn implies multiple realizability given that the motive for alternative uses of existing artifacts is typically unavailability of the type of artifact for which the use is standard — e.g., you tighten a loose screw on a doorknob with a tableknife because you can’t find your screwdriver, or don’t want to go all the way out to the garage to get it, or don’t have a small enough one, or whatever. This very close connection between multiple realizability and multiple utilizability makes it reasonable to take them together for the purpose of describing how these two characteristics show up in theories of artifact function. Most of the theories outlined in the previous section make some kind of a distinction between the standard functions of artifacts and alternative, occasional func18 There are a number of other function theorists who claim, or sometimes only imply, that their preferred theory of biological function also applies to artifacts. But they say so little about artifacts, or their theories as applied to artifacts overlap so much with one or more of the theories already discussed, that a separate review does not seem warranted. Examples are [Bigelow and Pargetter, 1987; Kitcher, 1993], and [Cummins, 1975].

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tions, although they employ different terminology. Preston and Scheele distinguish between proper function and system function; Houkes and Vermaas distinguish between proper function and accidental function; McLaughlin distinguishes design function from use function; Millikan accepts a distinction between proper function and Cummins functions; and Neander suggests a tripartite distinction between design function, user function, and occasion function. All of these schemes are clearly aimed at analyzing the phenomena associated with multiple utilizability and multiple realizability. Searle emphasizes the relatively unconstrained nature of physical structure with respect to function, especially for his status functions. This is at least a comment upon multiple realizability, if not multiple utilizability. Thus it seems that most theorists of artifact function do recognize these twin phenomena as important, and distinguish them terminologically. However, how best to account for these phenomena theoretically is a very large question that remains to be answered. The predominantly intentionalist cast of theories of artifact function predisposes theorists to try to account for the relevant distinctions in terms of intentional action. But the exchanges between Preston [1998] and Millikan [1999], as well as between Vermaas and Houkes [2003] and Preston [2003] show that the intentionalist approach is at least problematic in this regard. In addition, multiple realizability and multiple utilizability taken together indicate that the relationship between function and physical structure of artifacts is in principle — and very often in fact — a many-many relationship. Thus the more general, underlying issue here is the nature of the relationship between function and structure. This issue has not really been addressed head on, although Searle and Vermaas and Houkes have taken some steps in that direction. The third characteristic I described above is recycling, which I noted may well be best understood as a special case of multiple utilizability where the utilized artifact loses its capacity to perform its original function in the process of being modified for the performance of an alternative function. This phenomenon really has not been discussed as such, although Scheele’s emphasis on the various processes involved in change of function seems like a promising beginning. But there are two points to be made, nonetheless. First, recycling involves a concomitant change of function and change of physical form, so the nature of the relationship between function and structure underlies this phenomenon just as it does the twin phenomena of multiple realizability and multiple utilizability. Second, the neglect of recycling in the philosophical literature on function is symptomatic of the general neglect of material culture in philosophy I noted at the very beginning of this essay. It shows up here as a failure to examine in any detail the full range of human interactions with material culture that are relevant to questions of function. An examination of recycling is especially important in this regard because it would provide a counterweight to the overwhelming emphasis on design that is detectable in the intentionalist theories of artifact function outlined above. I would be willing to bet that a much greater amount of human time, energy, and ingenuity is spent on reuse and recycling of existing artifacts than on designing


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new artifacts for specific purposes. Theorists of artifact function should find out the facts and take them into account when constructing their theories.19 The fourth characteristic I listed was reproduction with variation, but I will save that for last because it is perhaps the most important and interesting of the lot. Let us go on to the fifth characteristic, then: malfunction. The centrality of this phenomenon hinges on its connection to the widely accepted idea that artifacts have standard functions (or proper functions, as most theorists now call them). Proper functions are normative; they are what an artifact is supposed to do. If, as all too frequently happens, an artifact is broken or defective and cannot perform its proper function, it is malfunctioning. So theorists of artifact function must account for malfunction, just as theorists of linguistic or mental representation must account for misrepresentation. Searle [1995, p.15], mentions the normative valence of function assignments and the consequent possibility of identifying malfunctions. Dipert [1993, p.143 ff.] discusses malfunction under the heading of failures of function, and gives quite an extensive description of the common sources of such failures. Millikan’s [1984] original theory of proper function contains perhaps the best known account of malfunction. Other theorists who have adopted some version of Millikan’s theory of proper function (e.g., Preston [1998]; Scheele [2005]) can, of course, help themselves to it. Neander [1995] gives an account of malfunction that explicitly relates it to misreprentation. Vermaas and Houkes [2003] list accounting for malfunction as one of their four desiderata for an adequate theory of artifact function, and provide a provisional intentionalist explanation of this phenomenon. Thus it seems that malfunction is not only recognized as important, but is usually, if not universally, addressed by theorists of artifact function.20 This is not the case with the sixth characteristic on the list — phantom function. Artifacts have phantom functions when they are designed and reproduced to perform a specific function, but are in principle unable to do so, e.g., amulets to ward off evil or bring good luck, patent medicines, and so on. Griffiths [1993] gives an intentionalist account of this phenomenon. Preston [1998] gives one non-intentionalist account; and then later [unpublished] a different, but equally non-intentionalist account. Otherwise, phantom function seems to have gone unnoticed. This may be partly due to a tendency to focus on instrumental functions, and to ignore social or communicative ones. The failure of an artifact type to have the bare capacity to perform its instrumental function is likely to be recognized at some point, and the likely consequence is modification of the artifact so that it does have the required capacity, or being dropped from the production schedule if modification is fruitless. Consider Griffiths’s example of the tapered tails of early racing cars. Once the false theory of drag that spawned those tails was rejected, 19 Thinkers in other disciplines have a good headstart on this, from which philosophers have much to learn. See, for instance, the work on reuse and recycling by Schiffer, Downing and McCarthy [1981]. 20 Malfunction is also discussed by Maarten Franssen [2006] in the context of an article on the normativity of artifacts.

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the tails disappeared as well. But the most important phantom functions are ones where this sorting out process is unlikely to occur for social reasons or for reasons having to do with the psychological and cognitive makeup of human beings. Good luck charms, artifacts with religious significance, and popular remedies, for example, continue to be reproduced without let or hindrance regardless of their capacity to perform their functions. Phantom functions are thus both common and persistent. Moreover, the sharp disagreement between Griffiths and Preston is evidence that accounting for them is not going to be easy or straightforward. So here again, facts need to be gathered and more attention needs to be paid to them during theory construction. Finally, let us consider reproduction with variation. On the face of it, the theories referred to above as reproduction theories — Preston’s theory and the non-intentionalist component of Millikan’s theory — do recognize reproduction with variation as central to theories of artifact function. But neither Preston nor Millikan has really theorized reproduction in material culture in its own right, as opposed to appealing to it for the purposes of analyzing other phenomena of artifact function, such as the distinction between proper function and system function. Millikan [1984, p.19 ff.] does have an account of copying. But it is couched in the most generic of terms so as to cover both biology and material culture, and so avoids saying anything specific about the sources of persistence and variation. Since these sources are widely held to be very different in the two realms, philosophical issues and problems concerning reproduction in material culture are simply bypassed — as are issues and problems concerning reproduction in biology, of course. Preston [unpublished] does make an attempt to rectify this situation in her last chapter, where she discusses the reproduction of structure, function, and intention in material culture. But this is only a start.21 With regard to the rest of the theories outlined above, the focus on human intentions predisposes theorists to focus on production to the neglect of reproduction. McLaughlin, for instance, has a great deal to say about self-reproducing systems — which, on his view, artifacts are not — and then talks only about intentional “assembly” with regard to artifacts. Dipert, too, focuses exclusively on production and refers this activity back to the intentions of the producing agent. A correct description of an artifact as an artifact describes the artifact in the way that its creator conceived of it — at least as much as is now possible. Specifically, an object is contemplated by an agent, and some of its properties are intentionally modified (or perhaps, intentionally left alone); the production of an artifact is the goal of some intentional activity. [Dipert, 1993, pp. 15-16] Vermaas and Houkes [2003] do not even mention reproduction in the four desiderata they set forth for theories of artifact function. On the other hand, their fourth 21 One philosopher who has a lot to say about reproduction in material culture is Karl Marx, e.g., Chapter 23 of Capital. Similarly, there is relevant work in other disciplines. See, for example, [Schiffer and Skibo, 1997] and [Miller, 1985] on types and sources of artifact variability in the reproduction process.


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desideratum concerns the ascription of proper functions to novel prototypes, echoing Millikan’s insistence that theoretical provision must be made for things that are “new under the sun” to have proper functions [1984, p. 19; 1999, p. 204]. Dipert falls into this camp as well, since the ultimate aim of his book is an account of works of art — which are, of course, widely held to be unique and idiosyncratic productions. This emphasis not just on production, but on novel productions, is, it seems to me, a red herring. The salient characteristic of material culture is not production, but reproduction; not novelty, but standardized forms and functions. Indeed, as indicated above, novelty in any radical sense is vanishingly rare in material culture. What is common is minor or incremental variations on existing artifacts; and that is what primarily needs to be examined and explained. Another crucial issue that arises in connection with reproduction concerns the obviously social and collaborative nature of the processes involved. Scheele does not focus on reproduction in material culture, but his salutary emphasis on the social aspects of artifact function certainly provides a starting point for doing so. This is also true of Searle, who discusses artifact function in the context of a work devoted to the nature of social institutions in general. For the intentionalist wing, the social aspects of material culture are most naturally understood as Searle understands them — in terms of collective intentions.22 But philosophical theories of collective intention are in their infancy, since action theory has traditionally concentrated exclusively on individual intentional action. Moreover, the theories of collective intention that have been advanced so far, including Searle’s, only apply to small, close-knit, egalitarian groups. As Christopher Kutz [2000] complains, this leaves unanalyzed the larger, more diffuse, non-egalitarian groups that are paradigmatically the subjects of social processes — tribes, trade unions, the Vatican, military units, the faculty of [your institution’s name here], extended families, and so on. And with the possible exception of those small and isolated human cultures of which there are fewer and fewer these days, the reproduction of material culture is carried out mostly by these larger and more diffuse groups. In short, an intentionalist approach to the social aspects of reproduction with variation is barely off the ground and faces some rather large questions. Moreover, it will undoubtedly face non-intentionalist alternatives. Indeed, a non-intentionalist approach is most plausible precisely with regard to large, diffuse, non-egalitarian groups, where the overall nature and ultimate result of the activity undertaken may be explicitly intended only by some or even by noone involved. So what is the current state of the art in theories of artifact function? There is some discussion of the twin phenomena of multiple realizability and multiple utilizability, although many significant issues in this area have hardly been touched. And current theories do seem to have some leverage on the issue of malfunction. But recycling, phantom function, and reproduction with variation present some 22 There is one other option, represented here. Dipert [1993] prefers to reconstruct group or collective activity as “virtual” individual activity for the purposes of theorizing about artifacts. However, this is more a way of bypassing the social aspects of material culture than of analyzing them.

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very difficult problems and have hardly been broached. Overall, then, almost everything remains to be done. BIBLIOGRAPHY [Bigelow and Pargetter, 1987] J. Bigelow and R. Pargetter. Functions, The Journal of Philosophy 84, 181-196, 1987. [Cummins, 1975] R. C. Cummins. Functional analysis, The Journal of Philosophy 72, 741-765, 1975. [Dipert, 1993] R. R. Dipert. Artifacts, Art Works, and Agency. Temple University Press, 1993. [Franssen, 2006] M. Franssen. The normativity of artifacts, Studies in History and Philosophy of Science 37, 42-57, 2006. [Griffiths, 1993] P. E. Griffiths. Functional analysis and proper functions, British Journal for the Philosophy of Science 44, 409-422, 1993. [Houkes, 2006] W. Houkes. Knowledge of artefact functions, Studies in History and Philosophy of Science 37, 102-113, 2006. [Houkes and Vermaas, 2004] W. Houkes and P. E. Vermaas. Actions versus functions: A plea for an alternative metaphysics of artifacts, The Monist 87, 52-71, 2004. [Kitcher, 1993] P. Kitcher. Function and design, Midwest Studies in Philosophy 18, 379-397, 1993. [Kroes, 2003] P. A. Kroes. Screwdriver philosophy: Searle’s analysis of technical functions, Techn´ e 6, 22-35, 2003. [Kutz, 2000] C. Kutz. Acting together, Philosophy and Phenomenological Research 61, 1-31, 2000. [Lewens, 2004] T. Lewens. Organisms and Artifacts: Design in Nature and Elsewhere. The MIT Press, 2004. [McLaughlin, 2001] P. McLaughlin. What Functions Explain: Functional Explanation and SelfReproducing Systems. Cambridge University Press, 2001. [Miller, 1985] D. Miller. Artefacts as Categories: A Study of Ceramic Variability in Central India. Cambridge University Press, 1985. [Millikan, 2002] R. G. Millikan. Biofunctions: Two paradigms. In Functions: New Essays in the Philosophy of Psychology and Biology, A. Ariew, R.C. Cummins, and M. Perlman, eds., pp. 33-59. Oxford University Press, 2002. [Millikan, 1999] R. G. Millikan. Wings, spoons, pills, and quills: A pluralist theory of function, The Journal of Philosophy 96, 191-206, 1999. [Millikan, 1989] R. G. Millikan. An ambiguity in the notion ‘function’, Biology and Philosophy 4, 172-76, 1989. [Millikan, 1984] R. G. Millikan. Language, Thought, and Other Biological Categories: New Foundations for Realism. The MIT Press, 1984. [Neander, 1995] K. Neander. Misrepresenting and malfunctioning, Philosophical Studies 79, 109141, 1995. [Neander, 1991] K. Neander. The teleological notion of ‘function’, Australasian Journal of Philosophy 69, 454-468, 1991. [Perlman, 2004] M. Perlman. The modern philosophical resurrection of teleology, The Monist 87, 3-51, 2004. [Preston, unpublished] B. Preston. The Stuff of Life: Towards a Philosophy of Material Culture. Unpublished. [Preston, 2003] B. Preston. Of marigold beer — a reply to Vermaas and Houkes, British Journal for the Philosophy of Science 54, 601-612, 2003. [Preston, 1998] B. Preston. Why is a wing like a spoon? A pluralist theory of function, The Journal of Philosophy 95, 215-254, 1998. [Searle, 1995] J. Searle. The Construction of Social Reality. The Free Press, 1995. [Scheele, 2006] M. Scheele. Function and use of technical artefacts: Social conditions of function ascription, Studies in History and Philosophy of Science 37, 23-36, 2006. [Scheele, 2005] M. Scheele. The Proper Use of Artefacts: A Philosophical Theory of the Social Constitution of Artifact Functions. Simon Stevin Series in the Philosophy of Technology, 2005.


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[Schiffer and Skibo, 1997] M. B. Schiffer and J. M. Skibo. The explanation of artifact variability, American Antiquity 62, 27-50, 1997. [Schiffer et al., 1981] M. B. Schiffer, T. E. Downing, and M. McCarthy. ‘Waste not, want not’: An ethnoarchaeological study of reuse in Tucson, Arizona. In The Archaeology of Us: Modern Material Culture, R.A. Gould and M.B. Schiffer, eds., pp. 67-86. Academic Press, Inc., 1981. [Vermaas, 2006] P. E. Vermaas. The physical connection: Engineering function ascriptions to technical artefacts and their components, Studies in History and Philosophy of Science 37, 62-75, 2006. [Vermaas and Houkes, 2003] P. E. Vermaas and W. Houkes. Ascribing functions to technical artefacts: A challenge to etiological accounts of functions, British Journal for the Philosophy of Science 54, 261-289, 2003. [Vermaas and Houkes, 2006] P. E. Vermaas and W. Houkes. Technical functions: A drawbridge between the intentional and structural natures of technical artefacts, Studies in History and Philosophy of Science 37, 5-18, 2006.


FUNCTIONAL DECOMPOSITION AND MEREOLOGY IN ENGINEERING Pieter Vermaas and Pawel Garbacz

1

INTRODUCTION

A key characteristic of the engineering sciences is that their descriptions of physical objects and processes are cast not only in structural terms but in functional terms as well. The engineering sciences share this characteristic with biology and with some of the humanities. If, however, it is added that these functional descriptions express the aims for which objects and processes can be employed, the characteristic becomes more discriminative to the engineering sciences. Functional descriptions in the engineering sciences have been analysed in philosophy. The focus of these analyses has largely been on determining what is meant when an individual technical object or process is ascribed a function.1 Functional descriptions in the engineering sciences are, however, much richer than individual ascriptions of functions. Design methodologists, for instance, often characterise the initial conceptual phase of engineering designing as one in which engineers reason from a required overall function of some product-to-be to a number of subfunctions that can make up this overall function.2 In reverse engineering and other explanatory tasks the reasoning may be the other way round, deriving an overall function from a series of subfunctions. And in engineering knowledge bases, functional descriptions of technical objects, processes and their parts are related to one another.3 This functional reasoning in the engineering sciences leads to descriptions of technical systems in which different functions are related to one another. Functional reasoning leads in particular to functional decompositions, which are descriptions in which one overall function is related to a series of subfunctions that, together, make up the overall function. And functional reasoning can be taken to define what can be called a functional part-whole relationship by identifying the subfunctions in a functional decomposition as parts of the overall function that they make up. These interrelated functional descriptions have 1 See

Beth Preston’s chapter “Philosophical Theories of Artifact Function” in this Handbook. [Pahl and Beitz, 1996, Section 2.1; Umeda and Tomiyama, 1997; Chittaro and Kumar, 1998; Stone and Wood, 2000; Chakrabarti and Bligh, 2001; Kitamura et al., 2005/2006; Bell et al., 2007]. See also Peter Kroes’ chapter “Foundational Issues of Engineering Design” in this Handbook. 3 See William H. Wood’s chapter “Computational Representations of Function in Engineering Design” in this Handbook, in which reverse engineering also plays a role. 2 E.g.,


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received little to no philosophical attention, which probably becomes more clear when it is noted that they are about functions only. In functional decompositions overall functions are related to subfunctions and not to, say, the structural parts (the components) of the technical systems that have the overall functions. The functional part-whole relationship described above is similarly a part-whole relationship directly between functions and not a functionally-defined structural part-whole relationship between technical systems and their structural parts. A wall, for instance, may be a functionally-defined structural part of a house but the wall is not a subfunction part of the function of the house; rather the function to support is a functional part of the function to provide shelter. In this chapter we consider interrelated functional descriptions of technical systems, specifically functional decompositions and the functional part-whole relationship that such decompositions define.4 Yet, given the embryonic stage of research, we cannot do more than announcing them as a topic for philosophical analysis; in this chapter we give a first explorative analysis and an incomprehensive sketch of what this analysis may have in store.

1.1 Functional descriptions in engineering To pin down functional descriptions, functional decompositions and the functional part-whole relationship to which they lead, we introduce some initial notation, anticipating a more thorough exposition in Section 2. Let Φ be the function that is decomposed and let φ1 , φ2 , . . ., φn be the subfunctions into which Φ is decomposed. We write this down as Decomp(Φ, Org(φ1 , φ2 , . . ., φn )), where Org(φ1 , φ2 , . . ., φn ) refers to a functional organisation, that is, a set of functional orderings of the subfunctions φ1 , φ2 , . . ., φn . This organisation is introduced, and in Section 2 we do this more systematically, in order to capture that the ordering of subfunctions matters in functional decompositions. If, for instance, the functions φ1 to heat with 150 degrees centigrade and φ2 to cool with 150 degrees centigrade are temporally ordered, one has two possibilities, which lead to different overall functions: in the area of food processing, the ordering “φ1 and then φ2 ” may make up the function to bake; and the ordering “φ2 and then φ1 ” may make up the function to refrigerate. Hence, an initial reading of Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) is that the subfunctions φ1 , φ2 , . . ., φn ordered by the organisation Org(φ1 , φ2 , . . ., φn ) provide a decomposition of the function Φ. In general there exist in engineering more than one decompositions of a given function Φ, hence a reading of Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) in which the subfunctions φ1 , φ2 , . . ., φn ordered by Org(φ1 , φ2 , . . ., φn ) are presented as the unique decomposition of Φ, is to be avoided. A decomposition Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) is a description in which the subfunctions φ1 , φ2 , . . ., φn are related to one another by the organisation Org(φ1 , φ2 , . . ., φn ), and are in that organisation making up the function Φ. Moreover, the subfunctions φ1 , φ2 , . . ., φn can be taken as the functional parts of the overall function Φ. 4 See [Simons and Dement, 1996] for an analysis of the structural part-whole relations between technical systems.

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With this notation Decomp(Φ, Org(φ1 , φ2 , . . ., φn )), functional decomposition is analysed in primarily functional terms. The analysis is made broader by also including the objects and processes that are described in functional descriptions. Let S be a technical system that is described functionally by Φ, and let s1 , s2 , . . ., sn be the systems that are described by φ1 , φ2 , . . ., φn , respectively (the systems s1 , s2 , . . ., sn are functionally-defined structural parts of S; in Section 2.1 we discuss the relation between the systems s1 , s2 , . . ., sn and S in detail). With references to these systems we can, for instance, characterise more precisely the engineering activities in which functional descriptions play a role. Conceptual designing, for instance, can be analysed as follows. The starting point of conceptual designing is an overall function Φ and the aim is to determine the physical description of a system S, that is, the product-to-be, that can perform this function Φ. Assuming that this aim cannot be realised immediately by the engineers, i.e., assuming that they cannot derive the physical description of S directly from the description of the overall function, engineers reason by the following intermediate steps. First, they determine a series of subfunctions φ1 , φ2 , . . ., φn and an organisation Org(φ1 , φ2 , . . ., φn ) that defines a decomposition Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) of the overall function. Second, engineers determine objects and processes s1 , s2 , . . ., sn that can perform these subfunctions φ1 , φ2 , . . ., φn , respectively. And, finally, they arrive at a physical description of S from the organisation Org(φ1 , φ2 , . . ., φn ) of the subfunctions5 and the physical descriptions of s1 , s2 , . . ., sn .6 If the description of the overall function Φ is very detailed, one may assume that the entity S consists of only the entities s1 , s2 , . . ., sn , but if this description is a coarse-grained one, say when Φ is only the primary function of the product-to-be, then S may also contain other entities. Think, for instance, of an aeroplane. If Φ is merely the primary function to fly, then the subfunctions in a functional decomposition Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) of this overall function do not single out the systems that enable emergency evacuations of the passengers and crew. But if Φ refers to the more detailed function of to fly safely, then a functional decomposition should identify more of these latter systems. In reverse engineering the overall function Φ and the physical description of the system S are initially known, and the aim is to derive the subfunctions φ1 , φ2 , . . ., φn , their organisation Org(φ1 , φ2 , . . ., φn ) and the systems s1 , s2 , . . ., sn that perform these subfunctions.7 5 De Ridder [2007, Chapter 4] has argued that functional decompositions help engineers in reasoning from a purely functional description Φ of a product-to-be S to its physical description since especially the organisation of the subfunctions φ1 , φ2 , . . ., φn gives engineers early in the design process information about the spatiotemporal structure of the product: even if the systems s1 , s2 , . . ., sn are still only functionally characterised by φ1 , φ2 , . . ., φn , the organisation Org(φ1 , φ2 , . . ., φn ) fixes how these systems s1 , s2 , . . ., sn are spatiotemporally related. 6 Conceptual designing is not a process in which these three steps are taken one after the other; in engineering design literature it is emphasised that designing is an iterative process. 7 In reverse engineering some of the systems s , s , . . ., s may also be known initially. The n 1 2 objects in the set {s1 , s2 , . . ., sn } are technical components of S, and it seems reasonable to assume that engineers are able to recognise some of those components. The processes in {s1 , s2 , . . ., sn } may, however, be more difficult to identify.

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In engineering knowledge bases, functional descriptions of technical systems contain all functions Φ, φ1 , φ2 , . . ., φn related to one another through organisations Org(φ1 , φ2 , . . ., φn ) and decompositions Decomp(Φ, Org(φ1 , φ2 , . . ., φn )).8 These functional description may also contain the systems S, s1 , s2 , . . ., sn , and, conversely and more typically, descriptions in engineering knowledge bases are descriptions of the systems S, s1 , s2 , . . ., sn to which the functions Φ, φ1 , φ2 , . . ., φn are added. With these characterisations of engineering activities, one can also to some extent formulate criteria that functional descriptions and functional reasoning should meet in order to be useful. In conceptual designing the subfunctions φ1 , φ2 , . . ., φn should be ones for which, given the technological state of the art, one has available or can find easily the systems s1 , s2 , . . ., sn that can perform them (see also Section 5). Decomposing, for instance, a function to emit light into the subfunctions to collect acoustic energy and to convert acoustic energy in light does not help in finding via the entities s1 and s2 a physical description of a light source S: currently systems s2 that can perform the function to convert acoustic energy in light are technologically not available. In reverse engineering, assuming that the description of the overall function Φ is sufficiently detailed, the systems s1 , s2 , . . ., sn should make up together a substantial part of S, in order to avoid the conclusion that the original designers of S added all kinds of spurious systems to their design of S. Finally, one of the goals for developing knowledge bases is to enhance communication about functional descriptions among engineers of different disciplinary backgrounds, between engineers and computer tools like CAD/CAM systems, and among computer systems. For achieving this goal, at least the subfunctions φ1 , φ2 , . . ., φn can be chosen from a standardised set (cf. [Hirtz et al., 2002]; see also Section 5).

1.2 Relevance The analysis of functional descriptions will be of relevance to a number of existing topics in philosophy, thus providing new and renewed links between the philosophy of the engineering sciences and other more classical domains in philosophy. We see four of such domains: philosophy of technology and philosophy of biology, both specifically with respect to accounts of functions, and epistemology and mereology. Existing philosophical accounts of the concept of technical functions provide input to the analysis of functional descriptions, and this analysis can in turn be seen as a next step in developing these accounts. The analysis of functional descriptions may in this way yield criteria for judging the accounts, for it is not yet clear if all these accounts can provide for a basis sufficient for taking this next step (we come back to this point in Section 3). 8 Alternatively one can analyse functional descriptions in engineering knowledge bases as descriptions containing functions Φ, φ1 , φ2 , . . ., φn related to one another through organisations Org(φ1 , φ2 , . . ., φn ) and a composition function (in the mathematical sense) Comp(Org(φ1 , φ2 , . . ., φn )) = Φ expressing that the subfunctions φ1 , φ2 , . . ., φn ordered by Org(φ1 , φ2 , . . ., φn ) make up Φ. We introduce this composition relation in Section 2.


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Since the analysis of technical functions is typically related and contrasted to the analysis of biological functions, it is to be expected that the analysis of functional descriptions establishes a similar interaction with philosophy of biology.9 In that domain there already is attention for the biological counterpart of functional decompositions (e.g., [Wimsatt, 2002]), which is especially manifested in the philosophy of science and epistemology literature on mechanistic explanations. In that literature scientific discoveries in biology and other sciences are interpreted as discoveries and explanation of mechanisms [Machamer et al., 2000; Craver, 2001; Bechtel and Abrahamsen, 2005], where the concept of mechanisms is sometimes explicitly introduced by means of technical artefacts [Craver and Bechtel, 2006]. In such explanations activities of mechanisms are analysed in terms of the organised10 objects and activities that make up mechanisms. Mechanistic explanations and functional descriptions in engineering are clearly related (Bechtel and Abrahamsen [2005, pp. 432—433] even speak about functional decompositions in the context of mechanistic explanations). A mechanism can arguably be interpreted in engineering as the entity S, the mechanism’s activity as the overall functions Φ, and the entities and activities that make up the mechanism as the objects and processes s1 , s2 , . . ., sn , revealing the epistemic dimension of functional decompositions as explanations. Functional reasoning in engineering is thus a form of explanatory reasoning, albeit one that provides more types of explanations than the one referred to in mechanistic explanations. In conceptual designing a functional decomposition of the overall function Φ of the product-to-be S implies also an explanation of the function Φ in terms of the organised subfunctions φ1 , φ2 , . . ., φn of the systems s1 , s2 , . . ., sn (see [de Ridder, 2006]). In reverse engineering and in knowledge bases, functional descriptions may, however, represent reasoning in which the subfunctions φ1 , φ2 , . . ., φn and their organisation are, in reverse order, explained in terms of overall functions Φ. We will not further develop these links between engineering functional descriptions, functional descriptions in biology and functional reasoning in epistemology,11 apart from a few isolated remarks in our concluding Section 6. Functional decompositions define in two ways part-whole relationships and their analysis may contribute to logic and specifically mereology. First, functional decompositions define a part-whole relation between the systems S and s1 , s2 , . . ., sn , with S taking the role of whole and s1 , s2 , . . ., sn taking the role of parts. These parts are called functional components by Peter Simons and Charles Dement [1996, p. 264] and we called them functionally-defined structural parts. Simons and Dement have argued that this part-whole relationship is different to a number of other part-whole relationships that are in use in engineering, and that it is not 9 Through this interaction with biological theories of functions, it may be expected that the analysis of functional decompositions in engineering will eventually also interact with philosophy of mind and the philosophy of the cognitive sciences, since accounts of biological functions are applied in these two domains. 10 Our notion of the organisation of functions Org(φ , φ , . . ., φ ) is adapted from the notion n 1 2 of organisation as used in the literature on mechanistic explanations. 11 See [Houkes, 2006] for a broader discussion of functional reasoning in engineering.

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coinciding with the standard notion of the part-whole relationship as defined in mereology. Second, functional decompositions define a part-whole relation directly on the level of functions, that is, between the functions Φ and φ1 , φ2 , . . ., φn , with Φ taking the role of whole and φ1 , φ2 , . . ., φn taking the role of parts. We called this the functional part-whole relationship and this second relationship is to our knowledge not yet considered in mereology even though it may be taken as inducing the first structural part-whole relationship between the systems S and s1 , s2 , . . ., sn , since these systems are singled out by the functions Φ and φ1 , φ2 , . . ., φn (we come back to the functional part-whole relationship in Section 4). In addition to being relevant to philosophy, the analysis of functional descriptions can also be of benefit to engineering. Clearly engineering is initially primarily a source to this analysis. But when it will advance, philosophical analysis may contribute to the different engineering uses of functional descriptions. Conceptual clarity provided by the different accounts of technical functions may be of benefit to engineering as a whole; research on the epistemology of functional decompositions will be more of use to design methodology and to functional reasoning, whereas research on mereology may prove to be of value for developing engineering ontologies for knowledge bases (we come back to these points in Section 5).

1.3 Our plan In the next section we develop our characterisation of functional descriptions by defining the organisation Org(φ1 , φ2 , . . ., φn ) of sets of functions and the composition Comp(Org(φ1 , φ2 , . . ., φn )) of such sets. Then we discuss in Section 3 the extent to which existing philosophical accounts of technical functions provide already means for carrying out the analysis and how these accounts may in turn be affected by the analysis. In Section 4 we focus on mereology and in Section 5 we consider engineering work on functional decompositions and illustrate how this may interact with philosophical research on the topic. 2 FUNCTIONAL ORGANISATION AND FUNCTIONAL COMPOSITION In the previous section we introduced functional decomposition as a relation Decomp(Φ, Org(φ1 , φ2 , . . . , φn )) that is to be read as that the subfunctions φ1 , φ2 , . . . , φn ordered by the organisation Org(φ1 , φ2 , . . . , φn ) provide a decomposition of the function Φ. For defining this relation more formally, we here describe functional descriptions in general and more systematically. We start by introducing organisations Org(φ1 , φ2 , . . . , φn ) of functions, then we introduce compositions Comp(Org(φ1 , φ2 , . . . , φn )) of such organisations of functions, and finally we define decompositions Decomp(Φ, Org(φ1 , φ2 , . . . , φn )) in terms of such compositions. Let, firstly, F be the set of all functions of technical systems. This set contains thus both the overall function Φ and the subfunctions φ1 , φ2 , . . . , φn in the case of a decomposition Decomp(Φ, Org(φ1 , φ2 , . . . , φn )).


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Define, secondly, “→” as a relation of functional ordering between two functions φ and φ in F that expresses that functional output of φ is functional input to φ . Functional input and output of a function are for now primitive terms; in Section 5 we discuss an engineering approach to functional decomposition in which this input and output consist of flows of materials, signals and energies, yet other choices — the functional input and output may consist of forces and fields — are not ruled out. But, having this approach in mind, we assume that a functional ordering φ → φ implies the temporal ordering that φ is not later than φ . The ordering is in general neither symmetric nor reflexive, but for specific functions φ and φ it may hold that φ → φ and φ → φ, or that φ → φ: a force can be functional output of φ and functional input to φ , while the reaction force is output of φ and input to φ; heat may be functional output of φ and functional input to other functions but also input to φ itself. A functional ordering φ → φ is represented by an ordered pair φ, φ that belongs to the Cartesian product F ×F . Define, thirdly, a functional organisation Org(φ1 , φ2 , . . ., φn ) of a set {φ1 , φ2 , . . ., φn } of functions as a set {φi → φj } of functional orderings of φ1 , φ2 , . . ., φn . A functional organisation Org(φ1 , φ2 , . . ., φn ) is thus a, not necessarily connected, network of orderings between the functions φ1 , φ2 , . . ., φn , as depicted in the figures 1 and 2. A functional organisation Org(φ1 , φ2 , . . ., φn ) is represented by a set {φi , φj } of ordered pairs from the Cartesian product F ×F .

Figure 1. A linear functional organisation Networks of organised sets {φ1 , φ2 , . . ., φn } of functions in F , like those depicted in figures 1 and 2, are in engineering taken as making up other functions Φ in F . We capture this by defining, fourthly, functional composition Comp(Org(φ1 , φ2 , . . ., φn )) which maps the functions φ1 , φ2 , . . ., φn in their organisation Org(φ1 , φ2 , . . ., φn ) to this function Φ, that is, Comp(Org(φ1 , φ2 , . . ., φn )) = Φ. Formally, the general notion of functional composition is represented by a set of ordered pairs {φi , φj }, Φ that contains a set {φi , φj } of ordered functions from F ×F and a function Φ defined on F . Yet, functional composition Comp does not map every set {φi , φj } in F ×F onto another function Φ in F . Engineering constraints rule out some ordered pairs φk , φl as representing possible functional orderings φk → φl , and if a set

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Figure 2. A more complex functional organisation

{φi , φj } is containing one or more of such impossible orderings, then this set {φi , φj } neither represents a functional organisation Org(φ1 , φ2 , . . ., φn ), nor is mapped by Comp to another function in F . A general example of an engineering constraint on functional orderings φk → φl is that φk is not later than φl since then functional output of φk cannot possibly be functional input to φl . And in the approach in which functional inputs and outputs are flows of materials, signals and energies, one can derive more specific constraints: if, for instance, φk has electricity as its functional output, it cannot provide input to a function φl that has only water as its functional input. With functional composition defined, we are now able to define the general notion of functional decomposition as a relation Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) represented by a set of ordered pairs Φ, {φi , φj } such that a function Φ from F occupies their first argument place and a set {φi , φj } of ordered functions from F ×F occupies the second argument place. For this relation it holds that Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) if and only if Comp(Org(φ1 , φ2 , . . ., φn )) = Φ. In other words, the relation Decomp is the inverse relation to Comp. A more verbose reading of this decomposition relation Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) is thus that the subfunctions φ1 , φ2 , . . ., φn ordered by the organisation Org(φ1 , φ2 , . . ., φn ) provide a decomposition of the function Φ, since the composition Comp(Org(φ1 , φ2 , . . ., φn )) of φ1 , φ2 , . . ., φn ordered by Org(φ1 , φ2 , . . ., φn ) is equal to Φ. We assume that functional composition Comp(Org(φ1 , φ2 , . . ., φn )) is unique in the sense that a set of functions {φ1 , φ2 , . . ., φn } organised by Org(φ1 , φ2 , . . ., φn ) composes one function Φ. Yet we assume also that other compositions Comp(Org (φ1 , φ2 , . . ., φn )) may compose that same function Φ as well; engineering practices


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provide evidence for this latter assumption. Hence, a given function Φ may be decomposable in more than one way, meaning that Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) should indeed not be read as that the set of functions {φ1 , φ2 , . . ., φn } ordered by Org(φ1 , φ2 , . . ., φn ) is the unique decomposition of Φ. In sum, both functional composition Comp and functional decomposition Decomp are relations, but only the former is in the mathematical sense a function.

2.1

Technical systems

With the above concepts and definitions, the analysis of functional descriptions is given in primarily functional terms; only in the functional ordering relation between two functions φ and φ there is a reference to the temporal ordering: φ is not later than φ . The functions in functional descriptions are, however, functions of technical systems, and we now broaden our analysis to those systems to make the concepts and definitions more tangible. Let s be the system that is described by the function φ, and adopt the convention that s1 , s2 , . . ., sn and S are the systems described by the functions φ1 , φ2 , . . ., φn and Φ, respectively.12 In general a function φ does not single out uniquely one system s due to the underdetermination that exists between functional and structural descriptions of systems; a function φ rather fixes a set of entities {s} that have that function: the function to conduct an electric current can be performed by a copper wire, but also by other technical systems. Conversely, for a system s there exists in general a set {φ} of functions it can perform: a copper pipe can conduct electricity but also guide a fluid or gas. A technical system s can, moreover, be an endurant or a perdurant;13 up to now we sloppily alluded to this distinction by distinguishing between technical systems that are objects and those that are processes, respectively. A consequence of these observations is that a functional organisation of a set of functions {φ1 , φ2 , . . ., φn } does not in general translate straightforwardly into an associated spatiotemporal organisation of a set of systems {s1 , s2 , . . ., sn }, and vice versa. There exist, for instance, functions φ that can describe both endurants and perdurants: the function to prevent depletion of soil, for instance, describes fertilizers (endurants) and rotary crop systems (perdurants).14 A functional organisation of a set of functions {φ1 , φ2 , . . ., φn } containing 12 In the examples discussed in this chapter the entities s , s , . . ., s are all different systems: n 1 2 system si is thus by our convention straightforwardly described by the function φi . More complex cases are possible as well. Two functions φj and φk , may, for instance, describe the same system, which then implies that the associated systems sj and sk are one and the same. 13 We use the terms “endurant” and “perdurant” in the standard philosophical sense, i.e., an endurant is an entity that persists through time by being wholly present at more than one time and a perdurant is entity that persists through time by having different temporal parts or stages at different times (cf., for instance, [Lewis, 1986]). Nonetheless, we do not want to engage in the debate between threedimensionalism and fourdimensionalim, in which these terms are applied. Without accepting or rejecting the actual existence of endurants and perdurants, we just provide a theoretically broad framework, which makes room for both types of entities. 14 One can distinguish in general three types of functions: functions φ that single out only endurants {s} (the function to support the back and bottoms of humans, for instance, seems to

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such a “hybrid” function, clearly defines spatiotemporal organisations of the set of systems s1 , s2 , . . ., sn , that may be mutually quite different. And two ordered or unordered functions φ1 and φ2 may be performed by one system (the two functions to conduct an electrical current and to guide water may be performed by one single copper pipe), meaning that the functional organisation may even be lost all together. For analysing relationships between functional organisations of sets of functions and the associated organisations of the sets of technical systems, we continue by considering four special cases defined by the ontological opposition between endurants and perdurants. We sketch how in these examples the functional organisation of the functions φ1 , φ2 , . . ., φn is related to the spatiotemporal organisation of the systems s1 , s2 , . . ., sn . In the first two cases the systems in the set {s1 , s2 , . . ., sn } are all endurants and each si is functionally described by one separate function φi from the set of functions {φ1 , φ2 , . . ., φn }. In the first case the function Φ that composes φ1 , φ2 , . . ., φn in their functional organisation, i.e., Φ = Comp(Org(φ1 , φ2 , . . ., φn )), describes a system S that is an endurant as well; in the second case this function describes a system S that is a perdurant. In the third and fourth case the systems {s1 , s2 , . . ., sn } are all perdurants, is each si functionally described by one separate function φi , and is the composite function Φ describing an endurant S or a perdurant S, respectively. Table 1. The four special cases

Case Case Case Case

1 2 3 4

s 1, s 2, . . . , s n endurants endurants perdurants perdurants

S endurant perdurant endurant perdurant

So, let us start with considering the first two cases in which the functions φ1 , φ2 , . . ., φn are all functions of different endurants s1 , s2 , . . ., sn . These endurants are spatially positioned relative to one another and this positioning determines which of the endurants s1 , s2 , . . ., sn can physically interact with one another in a technological relevant way.15 There may, for instance, be a technologically relevant single out different chairs but not processes); functions φ that single out only perdurants (say, the function to provide health care); and functions for which the set {s} contains both endurants and perdurants (to prevent depletion of soil, as argued in the main text). Further examples of the third type reveal also that a function φ of an endurant s may sometimes be reinterpretable as a function φ of a perdurant s that takes place in that endurant s, and vice versa: the function to tear down city walls of the endurant “cannon”, for instance, can be taken alternatively as a function of the perdurant “shooting cannon balls” with that cannon, and vice versa. 15 Engineering determines what technologically relevant physical interactions are. In electrical engineering, electromagnetic interactions are relevant, in mechanical engineering, action and reaction forces are relevant, and so on.


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interaction between the endurants s1 and s2 , but not between s1 and s3 , and so on. Let us now capture this spatial positioning by the set of unordered pairs {{si , sj }} of endurants that do interact and call this the spatial organisation of the endurants {s1 , s2 , . . ., sn }. Assuming that the functional output of one function φ can only be the functional input to another function φ through physical interactions between the systems s and s that these functions are describing, this spatial organisation of {s1 , s2 , . . ., sn } puts a direct constraint on the functional ordering of the functions φ1 , φ2 , . . ., φn and thus on their functional organisation: Org(φ1 , φ2 , . . ., φn ) can consist of only a set {φi → φj } of functional orderings for which holds that for each element φi → φj the pair {si , sj } is an element of the spatial ordering of the endurants {s1 , s2 , . . ., sn }. The system S that is functionally described by the function Φ=Comp(Org(φ1 , φ2 , . . ., φn )), is now in its turn either an endurant or a perdurant. If S is an endurant, it is to be taken as an endurant that contains the physical composite of the endurants s1 , s2 , . . ., sn in their spatial organisation.16 If S is a perdurant, it is to be taken as a perdurant in which an endurant that contains that physical composite participates. An example of the first case is the composition of the three functions to attach to the seabed (φ1 ), to fix distance (φ2 ) and to attach to the vessel (φ3 ) of the three endurants “anchor”, “rope” and “knot”, yielding the function to fix the location of a vessel in open sea of an endurant “anchoring system”. The right spatial positioning of the anchor, rope and knot, allows action and reaction forces between the anchor and rope, and between the rope and the knot. Hence the spatial organisation of these endurants is captured by the set {{anchor, rope}, {rope, knot}}. The functional organisation Org(φ1 , φ2 , φ3 ) of the three functions is {φ1 → φ2 , φ2 → φ1 , φ2 → φ3 , φ3 → φ2 } with the functional inputs and outputs all forces, and this organisation satisfies the constraint that the spatial organisation puts on it. The endurant S consisting of anchor, rope and knot in their spatial organisation can perform the function Comp(Org(φ1 , φ2 , φ3 )) = Φ of fixing a ship. An example of the second case is the composition of the functions to remove solid particles (φ1 ), to absorb particles in solution (φ2 ) and to sieve bacteria (φ3 ) of the different reservoirs s1 , s2 and s3 (which are assumed to be endurants) part of a wastewater plant, yielding the function to purify water of the perdurant “ water treatment” (i.e., the process) that is performed by the plant. Assuming a linear spatial positioning of the reservoirs, fluids can flow from s1 to s2 , and from s2 to s3 letting the spatial organisation be equal to {{s1 , s2 }, {s2 , s3 }}. The functional organisation of the three functions is {φ1 → φ2 , φ2 → φ3 } (see also Figure 1) with the functional inputs and outputs all fluids in different phases of cleansing, and this organisation satisfies the constraint that the spatial organisation puts on it. The perdurant S is the water treatment process that takes place in the reservoirs 16 One may assume that the endurant S is just the physical composite of the endurants {s1 , s2 , . . ., sn }. This assumption is however challenged by the occurrence of all types of back-up systems and other safety systems in technical systems. It thus seems more tenable to hold that the physical composite of {s1 , s2 , . . ., sn } is part of S.

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in their linear spatial organisation and this process can perform the composite function Φ to purify water. In the third and fourth case that we consider, the functions φ1 , φ2 , . . ., φn are all functions of different perdurants s1 , s2 , . . ., sn . These perdurants are spatiotemporally positioned relative to one another and this positioning again determines which pairs of perdurants can physically interact with one another in a technological relevant way. If two perdurants si and sj take place simultaneously and in one another’s vicinity, such an interaction can take place from si to sj and fromsj to si . Such bidirectional interactions are represented in the spatiotemporal organisation of the perdurants s1 , s2 , . . ., sn by unordered pairs {si , sj }, similar to the interactions between the endurants discussed in the first two cases. If a perdurant sk takes place before another sl , interaction is possible only from sk to sl . These unidirectional interactions are particular to sets of perdurants and we represent them by ordered pairs sk , sl . The spatiotemporal organisation of the perdurants s1 , s2 , . . ., sn thus has the form {{si , sj }, sk , sl }. Assuming again that the functional output of one function can only be the functional input to another function through a physical interaction, this spatial organisation of {s1 , s2 , . . ., sn } puts again a constraint on the functional organisation of these functions: Org(φ1 , φ2 , . . ., φn ) can consist of only a set {φi → φj } of functional orderings for which holds that for each element φi → φj the pair {si , sj } or si , sj is an element of the spatiotemporal ordering of the perdurants {s1 , s2 , . . ., sn }. The system S that is functional described by the function Φ=Comp(Org(φ1 , φ2 , . . ., φn )) is an endurant or a perdurant. If S is an endurant, it is to be taken as an endurant which participates (possibly only partially) in the perdurants s1 , s2 , . . ., sn in their spatiotemporal organisation. If S is a perdurant, it is to be taken as a perdurant that consists of the perdurants {s1 , s2 , . . ., sn } in their spatiotemporal organisation. An example of the third case is the composition of the functional organisation of the functions to spin (φ1 ), to collect water (φ2 ), to produce hot dry air (φ3 ) and to vent humid air (φ4 ) of the four processes s1 , s2 , s3 and s4 that perform them in a drying machine (the endurant S), yielding the function Φ to dry clothes of that machine. The four processes can take place simultaneously, giving a spatiotemporal organisation containing every possible combination {si , sj }. The functional organisation Org(φ1 , φ2 , φ3 , φ4 ) is {φ1 → φ2 , φ3 → φ4 } with the functional inputs and outputs all consisting of water, and this organisation satisfies clearly the constraint that the spatiotemporal organisation of the processes {s1 , s2 , s3 , s4 } puts on it. The endurant S “drying machine” can perform the function Comp(Org(φ1 , φ2 , φ3 , φ4 )) = Φ of drying clothes. indent An example of the fourth and final case is the composition of the functions to emit radio waves with a specific frequency (φ1 ), to detect radio waves with the same frequency (φ2 ) and to display the direction and the delay of the reflected waves (φ3 ) of processes s1 , s2 and s3 (perdurants) that take place in the radar equipment, yielding the function to detect plane positions of a process S (also a perdurant) that includes these three (sub)processes {s1 , s2 , s3 }. If the


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processes s1 , s2 and s3 take place one after the other, their spatiotemporal organisation is given by {s1 , s2 , s2 , s3 }. The functional organisation Org(φ1 , φ2 , φ3 ) is {φ1 → φ2 , φ2 → φ3 } with the functional inputs and outputs all signals, and this organisation satisfies the constraint that the spatiotemporal organisation of the perdurants {s1 , s2 , s3 } puts on it. The perdurant S containing the three processes s1 , s2 and s3 can perform the function Comp(Org(φ1 , φ2 , φ3 )) = Φ of detecting the positions of planes. These four special cases suggest the following generalisation. For a functional description of the technical systems {s1 , s2 , . . ., sn , S} by means of the functions {φ1 , φ2 , . . ., φn , Φ}, where Comp(Org(φ1 , φ2 , . . ., φn )) = Φ and where the technologically relevant physical interactions between the systems {s1 , s2 , . . ., sn } are given by the spatiotemporal organisation {{si , sj }, sk , sl }, the following constraint on the functional organisation holds: Org(φ1 , φ2 , . . ., φn ) can consist of only a set {φi → φj } of functional orderings for which holds that for each element φi → φj the pair {si , sj } or the pair si , sj is an element of the spatiotemporal ordering of the perdurants {s1 , s2 , . . ., sn }. This constraint is most probably not the only one that is possible or reasonable. One can envisage also that in engineering one wants to limit the number of technological relevant (and irrelevant) physical interactions between the systems {s1 , s2 , . . ., sn } that are not required by the functional organisation Org(φ1 , φ2 , . . ., φn ); such ‘spurious’ interactions may, for instance, lead to unintended effects. Moreover, the systems {s1 , s2 , . . ., sn } are all systems that have by definition functions, meaning that the endurants and perdurants part of S that do not have functions are ignored; including such nonfunctional systems in the description will most probably again amount to all kinds of constraints.

3

FUNCTIONAL DESCRIPTIONS AND PHILOSOPHICAL ACCOUNTS OF TECHNICAL FUNCTIONS

Philosophy has, as we have noted, produced a number of accounts that spell out what it means to describe individual technical objects or processes functionally.17 These accounts may be taken as a starting point for the analysis of functional descriptions, and this analysis can in turn be seen as a next step in the development of the accounts. We present here three archetypical approaches towards technical functions18 and assess them for their ability to be developed to also describe more complex functional descriptions. The analysis of functional descriptions becomes as such also a criterion for judging the versatility of the existing accounts of technical functions to incorporating engineering activities such as functional decomposition. In the first approach functions of technical systems are analysed in terms of the intentions of their designers or of their users: the function of a system is taken as 17 See

Preston’s chapter Philosophical Theories of Artifact Function in this Handbook. and Vermaas, 2009].

18 [Houkes

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the capacity or goal for which it is designed or for which it is used. This approach can be called the intentionalist approach. An example is the account of technical functions by Karen Neander in which “the function of an artifact is the purpose or end for which it was designed, made, or (minimally) put in place or retained by an agent” [1991a; 1991b, p. 462]. The second approach emphasises the physical structure of technical systems and identifies the functions of parts of a system as the parts’ actual physical capacities that, together, causally contribute to a physical capacity of that system. This approach can be called the causal-role approach (the systems functions approach may be an alternative) and it was Robert Cummins [1975] who put it forward in detail. The third approach towards technical functions is one that defines functions relative to long-term developmental histories of technical systems. It takes distance from individual design processes of technical systems and individual uses, and focuses instead on their cultural dissemination. It identifies the function of a system with the capacity for which the system is reproduced for a longer period of time. This final approach can be called the evolutionist approach.19 The example is now Ruth Garrett Millikan’s notion of proper function [1984; 1993]. Of these three approaches especially the causal-role approach provides more than just an analysis of what it means to describe individual systems functionally. In this approach a set of actual capacities c1 , c2 , . . ., cn of a set of parts s1 , s2 , . . ., sn of a technical system that, together, causally contribute to a physical capacity C of that system, are all simultaneously taken as the functions φ1 , φ2 , . . ., φn of the parts s1 , s2 , . . ., sn . Hence, in this approach one has at once a functional description that consists of multiple functions {φ1 , φ2 , . . ., φn } and of a spatiotemporal organisation of the parts s1 , s2 , . . ., sn that puts constraints on the functional organisation Org(φ1 , φ2 , . . ., φn ) of these functions. Moreover, the interactions between the parts s1 , s2 , . . ., sn required for letting the capacities c1 , c2 , . . ., cn causally contribute to the physical capacity C, provides more definite information about the functional organisation Org(φ1 , φ2 , . . ., φn ).20 Conversely, functional descriptions containing the functions φ1 , φ2 , . . . , φn and Φ with Comp(Org(φ1 , φ2 , . . . , φn ))=Φ, and describing sets of technical systems {s1 , s2 , . . . , sn , S}, plausibly fit the causal-role account. In this account the functions φ1 , φ2 , . . . , φn , Φ single out capacities c1 , c2 , . . . , cn , C of the systems s1 , s2 , . . . , sn , S for which has to hold that, firstly, the systems s1 , s2 , . . . , sn are parts of S and, secondly, the capacities c1 , c2 , . . . , cn contribute causally to the capacity 19 The evolutionist approach has its origin in the analysis of biological functions and theories that fall under this approach are called etiological theories in that domain. In Preston’s chapter in this handbook, the evolutionist approach falls under the heading of non-intentionalist reproduction views. 20 In the causal-role approach a system s can have more than one function: s may have a capacity c as its function φ since c causally contributes to a capacity C of a system S, s may have a capacity c , different to c, as its function φ since c causally contributes to a capacity C of a system S , and so on. We here ignore questions about the relations between these multiple functions and consider only the organisation of the functions that parts s1 , s2 , . . ., sn have on the basis of their causal contributions to one capacity C of one system S.


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C. On the basis of the discussion of the four cases of functional composition given in section 2.1, the first condition plausibly holds and the second also although it introduces an explicit commitment that functions compose overall functions due to causal contributions. Consider, for instance, the example of the composition of the three functions to attach to the seabed, to fix distance and to attach to the vessel of anchor, rope and knot, yielding the function to fix the location of a vessel in open sea of an anchoring system. This example fits the causal-role approach, since the anchor, rope and knot are parts of the anchoring system and the capacities corresponding to their functions are causally contributing to the capacity “fixing the location of a vessel in open sea” of the anchoring system as a whole. Intentionalist approaches, in which users determine by their intentions the functions of technical systems, provide less means to analyse functional descriptions; intentionalist approaches that put designers at centre stage in determining functions, fare better. A technical system that is used for a specific capacity can in a user-intentionalist approach be ascribed that capacity as a function. Yet, for also ascribing functions to parts of that system, one has to assume that these parts are also intentionally used for specific capacities. This latter assumption seems, however, in general less tenable. In the anchoring-system example, for instance, it can be maintained that the system as a whole is intentionally used to fix the location of a vessel in open sea, but it is less tenable to maintain that a sailor who is throwing out an anchor line, uses the anchor for its capacity to attach to the seabed, the rope for its capacity to fix distance, and the knot for its capacity to attach to the vessel. Hence, user-intentionalist approaches provide for descriptions consisting of single functions ascribed to single systems, but may fail to give more complex functional descriptions. A designer-intentionalist approach can create such complex functional descriptions, since it can be maintained that designers in addition to designing technical systems as wholes, also design their parts intentionally. In the anchoring-system example, for instance, it can be said that the system as a whole, and the anchor, rope and knot were designed for the capacities to fix the location of a vessel in open sea, to attach to the seabed, to fix distance and to attach to the vessel, respectively. A first conclusion about the intentionalist approach seems therefore that it should de-emphasise the relevance of user intentions in the determination of functions of technical systems, in favour of designer intentions: designer-intentionalist approaches seem better equipped to incorporate more complex engineering functional descriptions. The account of technical functions as put forward by Wybo Houkes and Pieter Vermaas,21 is an example of a designer-intentionalist approach (it incorporates also elements of the causal-role and evolutionist approaches). In this account engineers can ascribe a capacity C as a function Φ to a technical system S as a whole, ascribe capacities c1 , c2 , . . . , cn as functions φ1 , φ2 , . . . , φn to parts s1 , s2 , . . . , sn of S, and relate these functions [Vermaas, 2006]. The difference with the causal-role approach is that in the Houkes-Vermaas account the entities S and s1 , s2 , . . . , sn need not actually have the capacities C and c1 , c2 , . . . , cn , respectively; the engineers need 21 [Houkes

and Vermaas, 2004; Vermaas and Houkes, 2006].

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only to have justified beliefs about S and s1 , s2 , . . . , sn having these capacities and about the capacities c1 , c2 , . . . , cn contributing to the capacity c. Conversely, functional descriptions containing the functions {φ1 , φ2 , . . . , φn , Φ} with Comp(Org(φ1 , φ2 , . . . , φn ))=Φ, and describing sets of technical systems {s1 , s2 , . . . , sn , S}, plausibly fit designer-intentionalist approaches, since, for instance, such functional descriptions can be taken as supported by the beliefs of designers. For evaluating the construction of functional descriptions in evolutionist approaches, again a distinction is to be made between approaches that put users at centre stage and those that favour designers. Let a designer-evolutionist approach be one in which a function φ of a technical system s is the capacity c for which designers reproduce s’s, say by including s’s repeatedly in their designs as systems that can perform the capacity c. Such evolutionist approaches can provide for functional descriptions like functional compositions. Anchor systems are repeatedly included in designs of ships for the capacity to fix the location of a vessel in open sea, so this capacity becomes the anchor system’s function Φ in a designer-evolutionist approach. And also anchors, ropes and knots are repeatedly included in designs for the capacities to attach to the seabed, to fix distance and to attach to the vessel, respectively, so also these capacities become the functions φ1 , φ2 and φ3 of the anchor, rope and knot part of the anchoring system. In a user-evolutionist approach, in which a function φ of a technical system s is the capacity c for which users (let) reproduce s’s by using s’s repeatedly for the capacity c, more complex functional descriptions may in general be harder to create. The reason for this is that it is less tenable to maintain that users (let) reproduce parts s of larger systems S for a specific capacity c. Consider again the anchoring system S and the anchor s1 , rope s2 and knot s3 . The reproduction of the whole system S due to user demands does now not necessarily imply that the parts s1 , s2 and s3 are also reproduced due to user demands. Due to the underdetermination that exists between functions and systems, a technological innovation may make that at some point in time some of the parts of the anchoring system are changed. The rope s2 may, for instance, be changed into a chain s2 and the knot s3 by a welded joint s3 . In an extreme case there may exist for a specific system S with a fixed function Φ a number of different sets {s1 , s2 , . . . , sn }, {s1 , s2 , . . . , sn }, . . . of parts by means of which S can be constructed, showing that reproduction of S need not imply reproduction of its parts. This conclusion may be rejected by taking systems consisting of different parts also as different systems: one could take the position that the anchoring system consisting of the anchor s1 , chain s2 and welded joint s3 is a different system S as compared to the anchoring system S consisting of the anchor s1 , rope s2 and knot s3 . By that position reproduction of the old anchoring system S still implies reproduction of the anchor s1 , rope s2 and knot s3 as well, and reproduction of the new anchoring system S implies reproductions of the anchor s1 , chain s2 and welded joint s3 . For anchoring systems this position may be tenable, but from an engineering perspective this position is less plausible for more complex technical systems. In


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technical systems like cars and industrial plants, simple parts like wires, pipes and switches can typically be replaced by alternative parts, and this replacement does not immediately turn those systems from an engineering point of view into new systems. The conclusion that this reproduction of technical systems S due to user demands does not in general imply that the parts of S are reproduced by those user demands as well, may also be rejected by noting that in the case of parts the users should be identified with the designers of the system S: user demands make that whole systems are reproduced and make that designers reproduce the parts of those systems. This position, however, turns a user-evolutionist approach towards functions into a designer-evolutionist approach. Hence, our first conclusion about evolutionist approaches is that they should de-emphasise the relevance of the reproduction of systems due to user demands in the determination of their functions, in favour of the reproduction of those systems by designers.22 Conversely, functional descriptions containing the functions {φ1 , φ2 , . . . , φn , Φ} with Comp(Org(φ1 , φ2 , . . . , φn ))=Φ, and describing sets of technical systems {s1 , s2 , . . . , sn , S}, plausibly fit designer-evolutionist approaches. In those approaches the systems {s1 , s2 , . . . , sn , S} have the their functions {φ1 , φ2 , . . . , φn , Φ} only if designers have reproduced for a longer period those systems for the capacities corresponding to their functions {φ1 , φ2 , . . . , φn , Φ}, and the fact that designers did so, provides support to the conclusion that the functions φ1 , φ2 , . . . , φn compose in their functional organisation to Φ. Our assessment of the three philosophical approaches towards technical functions is clearly a preliminary one that needs to be developed. This development is bound to correct our conclusions that the causal-role approach is suitable to an analysis of functional decomposition, and that the intentionalist and evolutionist approaches are so only if they de-emphasise the role of user intentions and of user demands in their respective analyses of functions of technical systems.

4

THE FUNCTIONAL PART-WHOLE RELATIONSHIP AND MEREOLOGY

Functional descriptions, and specifically functional decompositions Decomp(Φ, Org(φ1 , φ2 , . . . , φn )) and compositions Comp(Org(φ1 , φ2 , . . . , φn ))=Φ, amount to a part-whole relation directly on the level of functions, where the overall function Φ takes the role of whole and the subfunctions φ1 , φ2 , . . . , φn take the role 22 Another attempt to save user-evolutionist approaches may consist of an argument that draws on the distinction between “selection for” and “selection of” as made in [Sober, 1993] in the context of biological evolutionary theory. One may assume that the evolution of technical systems can be described by a technological version of this theory and then argue that user demands amounts to a selection of systems S “for” the capacities corresponding to their functions Φ, which in turn amounts to a selection “of” the parts s1 , s2 , . . . , sn with the capacities corresponding to their functions {φ1 , φ2 , . . . , φn }. This argument seems, however, again blocked by the underdetermination phenomenon that a particular system S can in principle be constructed from different sets {s1 , s2 , . . . , sn }, {s1 , s2 , . . . , sn }, . . . of parts: a selection of S “for” the capacity corresponding to its function Φ does not unambiguously amount to a selection “of” a specific set of parts s1 , s2 , . . . , sn .

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of parts. This analysis of functional descriptions faces, however, a serious problem with the understanding of the term “part”. If we understand the relation of parthood in the sense establish by standard mereology, then one can derive a number of consequences for subfunctions and overall functions that at least at first sight are incompatible with the engineering understanding of functional descriptions. In this section we present this problem and discuss possible solutions. First, we briefly sketch one of the possible axiomatisations of mereology. Then we expose the consequences involved in expressing the relation of being a subfunction in terms of the mereological relation of parthood. Finally, we try to investigate whether the current state of the art in philosophy and logic provides with some feasible means to work around the problem. So let us start with mereology itself. We cannot provide here a comprehensive exposition of the formal theory of mereology.23 The following axiomatisation, which is one among a number of equivalent systems, is given just for the sake of reference. Alfred Tarski formalised the standard mereology originally formulated by Stanislaw Le´sniewski (cf. the English translation of his works in [Srzednicki and Rickey, 1984]) by means of one primitive term: the relation of (improper) parthood denoted here by “≤”, where “x ≤ y” is to be read as “x is an improper part of y”. Tarski’s axiomatisation contains two axioms: (4.1)

If x ≤ y and y ≤ z, then x ≤ z.

(4.2)

If X = ∅, then there exists exactly one x such that x SUM X.

The expression “x SUM X” means that x is the mereological sum of the set X. The relation SUM is defined as follows:24 (4.3)

x SUM X ≡ ∀y ∈ X (y ≤ x) ∧ ¬∃y (y ≤ x ∧ ∀z ∈ Xy ∫ z).

(4.4)

x ∫ y ≡ ¬∃z(z ≤ x ∧ z ≤ y).

The expression “x ∫ y” is to be read as: x is disjoint from y. The complement of the relation of disjointness is the relation of overlap, which is usually denoted by “”. The problem now arises when we assume that axioms 4.1 and 4.2 apply to the domain of technical functions. If mereology is to be considered as a useful tool in analysing the part-whole relationship between subfunctions and overall functions, we must assume some kind of correspondence between this relationship and mereological terms. A suitable candidate mereological term for establishing this correspondence seems to be the relation SUM. (4.5) 23 An 24 Cf.

The functions {φ1 , φ2 , . . ., φn } are subfunctions of the function Φ iff Φ SUM {φ1 , φ2 , . . ., φn }. interested reader may consult [Simons, 1987] and [Casati and Varzi, 1999]. [Tarski, 1956].


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There are, however, a number of reasons to reject this choice. First, one can argue that SUM is not the proper relation to connect subfunctions and overall functions. Axiom 4.2 guarantees that for any non-empty set of functions, there exists the mereological sum of these functions. By means of 4.5 one thus has for any set of functions {φ1 , φ2 , . . . , φn } an overall function Φ that has them as subfunctions, meaning that for any set of functions {φ1 , φ2 , . . . , φn } there is a function Φ that can count as the composition of these functions. Yet, one can envisage sets of functions for which this will not be the case. Consider, for instance, the set {to maintain a pressure of 1 atmosphere in vessel x at time t, to maintain a pressure of 2 atmosphere in vessel x at time t}; by their intentional meaning it seems impossible to compose these two functions.25 Using the terminology defined in Section 2, we can explicate this fact by saying that for some sets of functions there does not exist any organisation by which they can be composed: an organisation of the function to maintain a pressure of 1 atmosphere in vessel x at time t and the functions to maintain a pressure of 2 atmosphere in vessel x at time t does not exist since these two functions cannot be temporally ordered one after the other, nor ordered simultaneously. Second, also if sets of functions {φ1 , φ2 , . . . , φn } properly compose overall functions Φ by SUM, there may be scientific and engineering reasons to nevertheless deny that they define reasonable compositions. The functions in the set {to cool, to allow a magnetic degree of freedom, to process a login request signal, to sand } may be taken as composing the overall function to smooth surfaces. But that composition seems also technologically nonsensical by the spurious first three elements. The reason for this embarrass de richesse is the fact that mereology lacks the conceptual tools needed to express any kind of scientific or engineering constraint or standard. Again, using the terminology defined in Section 2, we can formulate this second fact by saying that for some sets of functions specific organisations are from a scientific or engineering point of view (better) ruled out. Third, Axiom 4.3 guarantees that any non-empty set of functions composes exactly one other function. Consequently, if there are two engineering models in which the same set of subfunctions composes different functions due to different orderings between the subfunctions, the embedding of the functional part-whole relationship into mereology by 4.5 turns out to be inadequate. That such sets exist was briefly indicated at the beginning of section 1.1. Let φ1 be the function to heat with 150 degrees centigrade, let φ2 be to keep the temperature fixed, and let φ3 be to cool with 150 degrees centigrade. If these functions are performed sequentially in the order given, they may be taken as composing the function Φ1 to bake, but if they are performed in reverse order, they compose the overall function Φ2 to refrigerate. Again, the source of this incongruity seems to be the 25 [Simons, 2006] is one of the recent attempts at restricting the general principle of composition. Simons proposes to restrict this principle to the equivalence classes of mereologically disjoint objects. That is to say, if X is an equivalence class of this kind, then there exists such x that x SUM X. For instance, if we define in the set of protons and neutrons the relation: x exchanges gluons with y, then this relation will yield the equivalence classes of protons and neutrons such that each such class compose a single nucleus.

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fact that we cannot express in mereology any kind of order among subfunctions, whereas ordering seems vital to functional composition and was therefore part to our characterisation of it. Using the terminology defined in Section 2, we can formulate this consequence by saying that for some sets of functions there can exist more than one organisation by which the functions in these set compose mutually different overall functions: for the functions {φ1 , φ2 , φ3 } introduced above one has, for instance, Org 1 (φ1 , φ2 , φ3 ) given by the orderings {φ1 → φ2 , φ2 → φ3 }, which defines the composition Comp(Org 1 (φ1 , φ2 , φ3 ))=Φ1 , and Org 2 (φ1 , φ2 , φ3 ) given by {φ3 → φ2 , φ2 → φ1 }, which defines Comp(Org 2 (φ1 , φ2 , φ3 )) = Φ2 = Φ1 . In sum, 4.5 is not a plausible candidate for a conceptual bridge between mereology and the functional part-whole relationship. Now the problem with applying mereology to functional descriptions is that there are not many alternatives to 4.5 available.26 The above consequences derived from 4.5 suggest replacing the SUM relation with a more flexible expressive composition relation since some sets of functions do not (reasonably) compose27 whereas other sets of functions have more than one composition. In a recent proposal to describe components of technical systems in mereological terms as given by Peter Simons and Charles Dement [1996] this flexibility may seem to be present.28 Their proposal presupposes one of the strategies of reconciling mereology with the real world, consisting of proclaiming that besides the notion of parthood defined in standard mereology, there are a number of more specific relations of parthood, e.g., being a functional part or being a component (cf. [Casati and Varzi, 1999, pp. 33—36]). These more specific relations need not satisfy all the requirements imposed by Le´sniewski on the general relation of parthood. First, we need to emphasise that Simons and Dement focus on functional parts of technical systems and not on subfunctions part of overall functions. Thus, their theory applies to what we have called the functionally-defined structural partwhole relationship between physical systems. Being in a seminal way sensitive to the gap between standard mereology and actual engineering, Simons and Dement suggest substituting the general notion of part with a more specific notion that would be applicable to technical systems. The latter notion is tensed: x is part of y at time t. The following axioms establish the formal properties of the relation that captures this specific notion: (4.6)

If x exists at time t, then x is part of x at t.

26 The problems with applying mereology outside the domain of mathematics are well investigated (cf. Rescher [1955], Casati and Varzi [1999], Pribbenow [2002], Johansson [2004]). But, due to the fact that functional descriptions have not received much philosophical attention, there are not yet solutions available that deal specifically with the problems associated with applying mereology to functional descriptions. 27 [Simons, 2006] takes this approach in mereology, as is described in an above footnote. 28 Other attempts at defining the notion of parthood suitable for technical systems may be found in [Tzouvaras, 1993], [Salustri and Lockledge, 1999], [Johansson, 2004] and [Vieu and Aurnague, 2005]. Nonetheless, none of these accounts concerns functional descriptions as discussed of this chapter.


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(4.7)

If x is part of y at time t, then x exists at t.

(4.8)

If x is part of y at time t and y is part of z at t, then x is part of z at t.

(4.9)

If x is a proper part of y at time t, then there exists z such that z is a proper part of y at t and x is disjoint from (i.e., does not overlap) z at t.

Simons and Dement define the relation of proper parthood and the relation of overlap in the following way. (4.10) x is a proper part of y at time t iff x is part of y at t and y is not part of x at t. (4.11) x overlaps y at time t iff there exists some z such that z is part of x at t and z is part of y at t. If one ignores for a moment the tensed character of the relation parthood in question, the theory developed in [Simons and Dement, 1996] might be seen as a weaker version of standard mereology. In particular, we do not find here the counterpart of Axiom 4.2, so none of the aforementioned mereological paradoxes occurs here. Simons and Dement claim further that the relation they define provides the most general framework for speaking about the mereology of technical systems. From a philosophical point of view, we may distinguish within this framework the following kinds of parts: • assembly components, which are those parts that are manipulated as units during the processes of assembly or manufacturing, • functional components, which are those parts that perform certain functions, • maintenance components, which are those parts that are manipulated as units during the process of repairing, and a number of other kinds. The actual engineering practice involves however more specific notions. Simons and Dement draw our attention to three kinds of mereological structures related to three different engineering specifications of parts of technical systems. The engineering bill of materials represents the mereological components of the abstract physical architecture of a given system. The manufacturing bill of materials represents the mereological structure determined by a manufacturing schema for constructing the technical system in question. Finally, the logistic bill of materials specifies those components of the system that are salient for maintaining it in a state of readiness (cf. [Simons and Dement, 1996, pp. 268—271]). Despite its conceptual complexity and despite the fact that it avoids the aforementioned paradoxes of standard mereology, Simons and Dement’s theory of the mereological structure of technical systems is not meant to be applied to the modelling of functional part-whole relations, and seems also not to be applicable to

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it. Their theory does not make room for our notion of organisation or for some similar notion, which we believe is an indispensable aspect of any adequate conception of the functional part-whole relation. As a result, they can say neither that two parts of a technical system are sometimes not meaningfully or consistently combinable, nor that two subfunctions are sometimes not meaningfully or consistently combinable. And as a result, Simons and Dement cannot say that two parts of a technical system, or two subfunctions, are sometimes in multiple ways meaningfully and consistently combinable. As we can see again, mereological language as available in the literature is not expressive enough to capture functional part-whole relationship. In our characterisation of functional descriptions we introduced the concept of organisation to create this expressiveness. A way to improve on the conceptual bridge 4.5 between the functional part-whole relationship and mereology, may now seem be one in which this concept is explicitly introduced into the bridge. Using the notation defined in Section 2, we can rewrite 4.5 as 4.5*. (4.5*) Comp(Org(φ1 , φ2 , . . . , φn ))=Φ iff Φ SUM {φ1 , φ2 , . . . , φn }. 4.5* clearly implies 4.12.29 (4.12) For any set of functions φ1 , φ2 , . . . , φn , (i)

there is an organisation Org of these functions such that there exists a function Φ, for which it holds that Comp(Org(φ1 , φ2 , . . . , φn ))=Φ, and (ii) for any two organisations of those functions Org 1 and Org 2 , if Comp (Org 1 (φ1 , φ2 , . . . , φn ))=Φ1 , and Comp(Org 2 (φ1 , φ2 , . . . , φn ))=Φ2 , then Φ1 =Φ2 . In general, 4.12 is false. As we argued above, for some sets of functions there do not exist organisations or reasonable organisations of the functions and for other sets of functions there exist more than one (meaningful) organisation relative to which they compose mutually different functions. Still, in some restricted domains of engineering the specific organisation of subfunctions might not be that relevant, i.e., it may not affect the overall functions to which these subfunctions compose. If one describes a domain of this sort, then 4.5 can be seen as a definition of the purely mereological type of functional part-whole relation (within this domain). The example of the purely temporally organised functions to heat with 150 degrees centigrade, to keep the temperature fixed and to cool with 150 degrees centigrade, shows that temporal functional part-whole relations are in general not of such a mereological type. But more special cases of such temporal functional part-whole relations, or of specific spatial functional part-whole relations may be. Obviously, all these types are borderline cases of the more general spatiotemporal part-whole relationship defined in Section 2 and this general relationship is not mereological in the standard sense. 29 The implication from the left-hand side to the right-hand side in 4.5* is innocent; it is the reverse implication that should be blamed here.


5

257

AN ENGINEERING APPROACH TO FUNCTIONAL DECOMPOSITIONS

Engineering designing is, as we mentioned in the introduction, one of the engineering domains in which functional descriptions are in use. In the first conceptual phase of designing initial requirements about the product-to-be — user needs and additional specification about, for instance, safety — are translated into overall functions of the product and these functions are then by functional decompositions analysed in terms of series of subfunctions. Yet, the description of conceptual designing is far from being unambiguous: the initial design requirements are rarely standardised and often acknowledged to change during the unfolding of the design process, and the resulting overall functions and their decompositions are usually expressed in informal terms, not meeting rigorous constraints. As a result design methodologists interested in analysing and improving conceptual designing, are facing the problem how to define and represent functions and their decompositions more rigorously, a problem that has become increasingly important by the growing use of computers systems, such as CAD/CAM tools, to support engineering design. Among the different conceptual models that are devised to solve this problem,30 we report here about what has become known as the Functional Modelling approach, since it provides relatively well-defined descriptions of functional decompositions, by which we can illustrate how engineering and philosophical research on functional decompositions can benefit from one another. The origin of this approach can be located in the fundamental work of Gerhard Pahl and Wolfgang Beitz [1996]; current research centres on a framework proposed by Robert Stone and Kristin Wood [2000]. We start by discussing Pahl and Beitz’ original ideas of associating functions with flows, and then move to current research on what has become known as the Reconciled Functional Basis.31 Pahl and Beitz define a function as a relation between an input and an output of a technical system (under a specific goal) and claim that technical functions are derived from flows [1996, p. 31]. A flow is either a conversion of material (e.g., a chunk of clay being converted into a vase), a conversion of energy (e.g., electrical energy being converted into heat), or a conversion of signal (e.g., a safety buzz indicating the high pressure of a vapour). Pahl and Beitz do not spell out what it means that functions are derived from flows. But in their definitions and examples they presuppose that any function boils down to a flow, for instance, when they refer to a function denoted by the expression “transfer torque”, which clearly is a flow of torque.

30 Research in design methodology is not converging to a single approach to functional descriptions or to functional decomposition. Authors use, and acknowledged to use, different notions of functions of technical systems and different ways of representing systems (see, e.g., [Umeda and Tomiyama, 1997; Chittaro and Kumar, 1998; Hubka and Eder, 2001]) and arrive at different models of, for instance, functional modelling (see, e.g., [Chandrasekaran, 2005; Far and Elamy, 2005; Van Eck et al., 2007] for surveys). 31 The functional modelling approach is also discussed in William H. Wood’s chapter “Computational Representations of Function in Engineering Design” in this Handbook.

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Pahl and Beitz then continue with observing that an overall function of a technical system can be complex or less complex in three senses: the relationships between the input and the output of the technical system may be relatively opaque or rather transparent; the underlying physical processes might be intricate or simple; and, finally, the number of assemblies and component might be higher or lower. They suggest that in the case of functions of high complexity it is advisable to decompose such functions into subfunctions for three reasons: • to facilitate the subsequent search for design solutions, • to obtain simple and unambiguous function structures, and • to modularise the process of developing technical systems and their subsystems. These subfunctions are in turn relations between inputs and outputs defined in terms of the three flows, and are linked to one another by a causal net of such flows. This structure of subfunctions and flows as a whole should now establish the same relation between an input and an output as the overall function it decomposes. Any two subfunctions that are linked together by a certain flow in such a causal net need to be “compatible” and all subfunctions of a given overall function need to be combined in a “meaningful” way. Although it is unclear what Pahl and Beitz mean with these terms, they observe that the relationship between subfunctions and overall function is very often governed by certain constraints, inasmuch as some subfunctions have to be satisfied before others. Moreover, the Pahl and Beitz methodology leads to the question of whether there is a level at which the decomposition stops or effectively stops. Pahl and Beitz consider five types of conversions that they take as reasonably not sub-dividable, being to channel, to connect, to vary, to change and to store. Together with their distinction between flows of material, energy and signal, one arrives at a taxonomy of 15 basic functions occurring at the lower level of functional decompositions. In later developments, topic of the next part of this section, especially this taxonomy has been changed. Captured in our terminology, Pahl and Beitz arrive at functional decompositions Decomp(Φ, Org(φ1 , φ2 , . . . , φn )), where Pahl and Beitz’ overall function is Φ, the subfunctions are φ1 , φ2 , . . . , φn , and the causal net of flows defines the organisation Org(φ1 , φ2 , . . . , φn ) of these subfunctions (each flow in the net from one subfunction φi to another φj , defines a functional ordering φi → φj , and vice versa, meaning that Org(φ1 , φ2 , . . . , φn ) represents all connections in the net). Compared to our analysis of functional descriptions as given in Section 2, Pahl and Beitz introduce a number of additional assumptions. It seems at first sight that in their methodology the overall function Φ and the subfunctions φ1 , φ2 , . . . , φn in a functional decomposition are all typically describing systems S, s1 , s2 , . . . , sn that are perdurants, since conversions of flows refer to processes. However, the examples mentioned by Pahl and Beitz are functions of endurants — objects — and not of perdurants. That is, even if a function is a flow, i.e., a subtype


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of perdurant, this function is not a function of a perdurant; functions are special cases of perdurants that are ascribed to endurants. Second, by taking functions as conversions of flows of materials, energies and signals, functions seem to have to comply with physical conservation laws for such flows. The conversion of a signal flow representing a small amount of energy, into a much larger electromagnetic energy flow, seems not to be a possible function in Pahl and Beitz’ methodology.32 Finally, in a functional decomposition Decomp(Φ, Org(φ1 , φ2 , . . . , φn )) the subfunctions φ1 , φ2 , . . . , φn , are according to Pahl and Beitz ultimately not just any functions from the general set F of functions, but to be taken from the set of 15 conversions that are reasonably not sub-dividable. These subfunctions can thus only be to channel, to connect, to vary, to change and to store for materials, energies and signals. When evaluating our analysis of functional descriptions with the methodology of Pahl and Beitz, a first remark can be that our functional ordering relation φi → φj may be (too) coarse-grained. In our analysis this relation holds already if there is “something” that can count as functional output of φi that is functional input to φj . In the Pahl and Beitz methodology this something is categorised as (types of) materials, energies and signals. This opens the possibility to develop our analysis by distinguishing between (associated) different types of functional ordering relations between functions. Other ways of developing our analysis can be drawn from the second and third additional assumptions sketched above. These assumptions are also made in more recent work in the Functional Modelling approach to functional decompositions, suggesting that our analysis of functional descriptions as given in Section 2 is too liberal: in order to let it cohere more with engineering work on functional descriptions, we should incorporate a requirement that functional descriptions comply with physical conservation laws and a requirement that all functions can be decomposed in terms of what can be called basic functions. Our analysis does not provide the means to formulate the first requirement; for incorporating the second requirement we can define a set BF of basic functions and the condition that for all functions Φ in F there exists a decomposition Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) with {φ1 , φ2 , . . ., φn } ⊆ BF . Taking some distance from the work of Pahl and Beitz, one can, however, doubt that especially the second additional assumption that functional descriptions have to comply with physical conservation laws holds for all engineering work on functional decomposition. Bell et al. [2007], for instance, accept functions that have a signal representing a small amount of energy as their input and a much larger 32 That functions in the Functional Modelling approach have to comply with conservation laws is not explicitly said by Pahl and Beitz or in the key publications on the Reconciled Functional Basis discussed later in this section. Yet, examples of conversions of material and energy flows that are clearly violating conservation laws are hard to find, and the tracking of (conserved) flows seems to be an important device in developing functional decomposition in Functional Modelling. Modarres and Cheon [1999], however, make an explicit link between functions and conservation laws in (their work on) Functional Modelling.

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electromagnetic energy flow as their output.33 The physics that underlies such a function will clearly have to comply with conservation laws, but the input-output description of the function itself does not. The analysis of functional descriptions as given in Section 2, may therefore also be taken as a more general analysis, compared to which the additional assumptions in engineering methodologies for functional decomposition like the ones made by Pahl and Beitz can be analysed. This brings us to the benefits of philosophical research on functional descriptions to engineering: a philosophical analysis of such descriptions can help design methodologists with making their assumption explicit and with developing their work on functional decomposition. According to Pahl and Beitz the functions in decompositions are functions of endurants, but the examples given in Section 2.1 show that one can generalise functional decompositions to apply to also functions of perdurants. According to Pahl and Beitz functions comply with physical conservation laws, but this requirement may be dropped. And also the requirement that functions always have to be decomposable into functions from a set of basic functions may be questioned. One can, for instance, argue that this requirement has a context-dependent meaning. If functional decomposition is considered in the context of conceptual designing, this requirement may be that functions have to be decomposable into functions φ1 , φ2 , . . . , φn from a set of easily solvable functions, that is, from a set of functions for which, given the technological state of the art, one has available or can find easily the systems s1 , s2 , . . . , sn that can perform them (see also Section 1.1). Such a set of easily solvable functions varies with that technological state of the art. If, however, functional decomposition is considered in the context of engineering knowledge bases aimed at enhancing communication about functional descriptions among engineers, this requirement may have the form that functions are to be decomposable into functions φ1 , φ2 , . . ., φn from a standardised set of functions, irrespectively of whether the functions in this set are easily solvable. Such a standardised set clearly should not vary (too much) over time.

5.1 The reconciled functional basis A more recent research project that originates with the foundational work of Pahl and Beitz is the Reconciled Functional Basis project. This Reconciled Functional Basis (RFB, from now on) is the result of an effort towards establishing a standard taxonomy of basic technical functions (see, e.g., [Hirtz et al., 2002]) by reconciling two previous taxonomies: the NIST taxonomy (cf. [Szykman, et al., 1999]) and the older versions of the Functional Basis (developed in [Little et al., 1997; Stone et al., 1998; McAdams et al., 1999; Stone et al., 1999; Stone and Wood, 2000]). Each of these taxonomies is a result of empirical generalisation of engineering specifications. RFB analyses the notion of a functional decomposition against the background of its taxonomy of functions, which is based on a taxonomy of flows. RFB modifies 33 See,

for instance, their model of a torch [Bell et al., 2007, p. 401].


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the meaning of the term “flow” since here “flow” does not mean “a process of flowing” (e.g., removing debris), but “a thing that flows” (e.g., debris).34 More precisely speaking, in some papers, e.g., in [Stone and Wood, 2000] this term is used in both meanings, but the RFB taxonomy of flows is based on the latter sense. This shift in meaning is, to be sure, justifiable since it is hard to see how one might differentiate between a process of flowing and a function given the conception of Pahl and Beitz. The RFB whole taxonomy of flows is depicted in Table 2. RFB also contains a three-layer classification of what are called basic functions. Each type of function is accompanied by a definition (in natural language), example, and a set of synonymous names. The basic functions are divided in a first layer into eight primary types. Then, some primary basic functions are divided into types of secondary basic functions, and some of these secondary basic functions are in turn divided into types of tertiary basic functions. The whole taxonomy is depicted in Table 3. Of course, the RFB taxonomy of basic functions is not a model of functional decomposition. For instance, the fact that Divide and Extract are subtypes of Separate does not mean that the former are subfunctions of the latter. Moreover the basic functions are not functions in the sense the overall functions are, since the overall functions are (complex) modifications of specific input flows into specific output flows, whereas the basic functions are modifications generalised for the flows subjected. Hence, the basic subfunctions are in the RFB to be identified with basic functions operating on specific primary, secondary and tertiary flows. In RFB a functional decomposition is a conceptual structure that consists of an overall function that is decomposed, its subfunctions into which the overall function is decomposed, the flows which are modified by the subfunctions, and a net that links these modifications in an ordered way. The overall function to be decomposed is defined in terms of the flows it modifies, which are taken from the RFB taxonomy of flows. Each of its subfunctions is defined both in terms of the flows the respective subfunction modifies and in terms of its type of modification, which is taken from RFB taxonomy of basic functions. For instance, the overall function of a screwdriver, which is to tighten/loose screws, is defined by means of the following ten input flows and nine output flows (see also Figure 3). • input flows for the function tighten/loose screws: – energy flows: electricity, human force, relative rotation and weight; – material flows: hand, bit and screw ; – signal flows: direction, on/off signal and manual use signal ; • output flows for the function tighten/loose screws: – energy flows: torque, human force, heat, noise and weight; 34 In engineering design the term “flow” is used in the specific sense in which it is roughly equivalent to the term “process”.

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Table 2. The RFB taxonomy of flows [Hirtz et al., 2002] Primary flow

Secondary flow Human Gas Liquid Solid

Tertiary flow

Object Particulate Composite

Plasma Material

Mixture

Status Signal Control

Gas-gas Liquid-Liquid Solid-solid Solid-liquid Liquid-gas Solid-gas Solid-liquid-gas Colloidal Auditory Olfactory Tactile Taste Visual Analog Discrete

Human Acoustic Biological Chemical Electrical Electromagnetic Energy

Optical Solar

Hydraulic Magnetic Mechanical Pneumatic Radioactive/Nuclear Thermal

Rotational Translational


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Table 3. The RFB taxonomy of functions [Hirtz et al., 2002] Primary functions Branch

Secondary functions Separate

Tertiary functions Divide Extract Remove

Distribute Import Export Channel

Transfer Guide

Connect

Couple

Transport Transmit Translate Rotate Allow degree(s) of freedom Join Link

Mix Actuate Regulate Control magnitude

Change

Stop Convert Provision

Increase Decrease Increment Decrement Shape Condition Prevent Inhibit

Convert Store

Contain Collect

Supply Sense Signal

Support

Indicate Process Stabilize Secure Position

Detect Measure Track Display

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Figure 3. The RFB modelling of the overall function of a screwdriver [Stone and Wood, 2000, Fig. 2] – material flows: hand, bit and screw ; – signal flows: looseness/tightness. On the other hand, one of the subfunctions in the functional decomposition of this overall function tighten/loose screws is called convert electricity to torque (see Figure 4), which means that it is a function of the convert-type (cf. Table 3), and modifies one input flow to three output flows: • input flows for the subfunction convert electricity to torque: – energy flows: electricity; – material flows: none; – signal flows: none. • output flows for the subfunction convert electricity to torque: – energy flows: heat, noise and torque; – material flows: none; – signal flows: none.


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Figure 4. The RFB functional decomposition of a screwdriver [Stone and Wood, 2000, Fig. 4]

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The task of a designer who performs a functional decomposition is to link any input flow of the overall function to be decomposed with some of the output flows. Any such link that starts with an input flow of the overall function and ends with one of its output flows is called a function chain. In RFB one distinguishes between two types of function chains: sequential and parallel. A function chain is sequential if it is ordered with respect to time, i.e., if any temporal permutation of its subfunction may in principle result in failing to perform the overall function. A parallel function chain is a fusion of sequential function chains that share one or more flows. In RFB one assumes that each subfunction of an overall function to be performed by a technical system S is realised by a component of S; however, the relation between subfunctions and components is many-to-many, i.e., one subfunction may be realised by several components and one component may realise more than one subfunction. The notion of functional decomposition developed within RFB plays an important role in what is called the concept generator, which is a web-based computational tool for enhancing conceptual design.35 The concept generator is to present a designer with a number of different solutions to his or her design problem on the basis of previously developed (and stored) high-quality designs. One of the input data to be provided for this tool is a function chain for a product to be newly developed. The output solutions describe the design solution in terms of the technical systems whose descriptions are loaded into the knowledge base of the concept generator. The functional decomposition links the overall function established by the generator with the conceptual components that compose a general description of the product that is construed here as a solution of the initial design problem [Strawbridge et al., 2002; Bryant et al., 2004]. The RFB proposal adds precision and a wealth of empirical details to the methodology of Pahl and Beitz. Its explicit aim to contribute to the standardisation of conceptual models in engineering makes it even more valuable for specifically mereological analysis of functional modelling. In our terminology, the overall function of an RFB functional decomposition Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) may be any function Φ but the subfunctions φ1 , φ2 , . . ., φn are to be identified with RFB basic functions from Table 3 operating on specific RFB primary, secondary and tertiary flows from Table 2. The net of flows between the subfunctions φ1 , φ2 , . . ., φn defines their organisation Org(φ1 , φ2 , . . ., φn ). In RFB the overall functions Φ and the subfunctions φ1 , φ2 , . . ., φn in functional decompositions Decomp(Φ, Org(φ1 , φ2 , . . ., φn )) may be describing systems S and s1 , s2 , . . ., sn that are endurants and perdurants, but like in the methodology of Pahl and Beitz, again the additional assumptions are made that functions comply with physical conservation laws for flows, and that the subfunctions φ1 , φ2 , . . ., φn , are to be taken from a set of basic functions. A further additional assumption seems to be that the functional orderings φi → φj making up the organisations 35 See

http://function.basiceng.umr.edu/delabsite/repository.html.


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Org(φ1 , φ2 , . . ., φn ) of the subfunctions, are always asymmetric: flows between two subfunctions in functional decompositions like depicted in Figure 4, always go in one direction. The benefit of philosophical research on functional descriptions to engineering can again lie in making these assumptions explicit and in challenging them. The requirement that functions always have to be decomposable into RFB basic functions operating on specific RFB flows introduces again a tension between the goal of functional decomposition to facilitate designing and to facilitate communication. Consider, for instance, the basic function convert acoustic energy in electrical energy. The identification of this basic function in a decomposition of an overall function may be useful to a shared understanding of this overall function but will not help designers to easily find a corresponding design solution. A requirement that subfunctions are only ordered in one direction may in turn be helpful in engineering for managing the flow of materials, energies and signals, but may also be revealed to be an unnecessary constraint to the decomposition of functions. 6 PROVISIONAL CONCLUSIONS In this chapter we have introduced interrelated functional descriptions and specifically functional decompositions as a topic for philosophical analysis. We identified conceptual designing, reverse engineering and engineering knowledge bases as the engineering domains in which such functional descriptions are given. Special cases of interrelated functional descriptions that are important to engineering are functional decompositions: descriptions in which an overall function Φ of a technical system S is analysed in terms of a series of mutually ordered subfunctions φ1 , φ2 , . . . , φn describing systems s1 , s2 , . . . , sn part of S. Features of interrelated functional descriptions that are of interest to philosophy are that they define two different part-whole relationships: a functionally-defined structural part-whole relationship by which the systems s1 , s2 , . . . , sn are part of the system S, and a functional part-whole relationship by which the functions φ1 , φ2 , . . . , φn are part of the function Φ. We introduced in Section 2 a series of formally defined relations for capturing interrelated functional decompositions. A functional ordering φ → φ exists between two functions if functional output of φ is functional input to φ . The functional organisation Org(φ1 , φ2 , . . . , φn ) of a set of functions φ1 , φ2 , . . . , φn is defined as the set of (pair-wise) functional orderings that exists between these functions. Functional composition Comp(Org(φ1 , φ2 , . . . , φn )) maps the functions φ1 , φ2 , . . . , φn in their organisation Org(φ1 , φ2 , . . . , φn ) to another function Φ. Functional decomposition was finally taken as the inverse of composition and captured as a relation Decomp(Φ, Org(φ1 , φ2 , . . . , φn )) for which holds that Comp(Org(φ1 , φ2 , . . . , φn ))=Φ. These relations were, moreover, illustrated with a series of cases and examples in which also the systems s1 , s2 , . . . , sn and S described by the functions φ1 , φ2 , . . . , φn and Φ, respectively, were considered. The discussion of these cases/examples suggest that functional organisations in functional descrip-

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tions are constrained by the spatiotemporal ordering of the systems described: φ → φ can hold only if there is a physical interaction from s to s . In this chapter we have, moreover, identified four areas in philosophy that may benefit from the analysis of functional descriptions: research on functions in philosophy of technology and in philosophy of biology, epistemology and mereology. Finally, we presented engineering work on functional decompositions, and considered how it can contribute to and benefit from our first exploration of functional descriptions. Here we sum up our findings, with the necessary provisos; since the analysis of engineering functional descriptions is a new topic in philosophy, this chapter can merely be an appetizer, shaped as well as limited by the directions we have chosen in our exploration, but hopefully rewarding nevertheless. The first philosophical area that can benefit from the analysis of functional descriptions is research on the concept of technical functions in philosophy of technology. Philosophical accounts of technical functions may initially be taken as mere means to developing the analysis of functional descriptions. We now argued in Section 3 that of the three archetypical approaches towards such accounts, only Cummins’ causal-role approach seems fully equipped to provide for such means; the other two approaches, that is, the intentionalist and the evolutionist ones, seem to be able to do so only if designers and not users are determining technical functions by their intentions or by reproduction, respectively. Hence, as its precondition, the analysis of functional descriptions requires that the role of users in the determination of technical functions in these latter two approaches is de-emphasised in favour of the role of designers. Analyses of technical functions in philosophy of technology and of biological functions in philosophy of biology are often in interaction, for instance, by using them as contrasts to one another, or by attempting to unify them. By this interaction, it may be assumed that the analysis of functional descriptions in engineering may also have an impact on philosophy of biology. We have not elaborated on the possible results of this interaction. In Section 4 we considered the area of mereology. We considered in particular the functional part-whole relationship between the functions φ1 , φ2 , . . . , φn and the overall function Φ given by their composition Comp(Org(φ1 , φ2 , . . . , φn )). We have argued that this functional part-whole relationship cannot be understood with standard mereology, but requires a modelling in mereology that can accommodate organisations of the subfunctions. In Section 5 we presented an engineering approach to functional decompositions, called functional modelling and described it in terms of the concepts part of our analysis of functional descriptions. Relative to this analysis functional modelling introduced a number of additional requirements. The more important ones were that functional descriptions have to comply with physical conservation laws, that there exists a set of basic functions into which other functional can be decomposed, and that functional ordering are always asymmetric functional orderings. We argued that these additional requirements could be incorporated into our analysis but could also be taken as assumptions in engineering approaches to functional


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descriptions that can be questioned, thus establishing the worth of philosophical analyses to engineering. The remaining philosophical area to which the analysis of functional descriptions can contribute is epistemology. We have described in Section 1 the relations between functional descriptions, functional reasoning and explanation, and linked especially functional decomposition to the literature on mechanistic explanation, a link that establishes another relation between analysing functional descriptions and philosophy of biology. Yet, we did not delve deeper into these relations. One reason for not considering epistemology in detail is that we still have to acknowledge that the analysis of functional descriptions is currently in a first phase. We hope to have shown with our analysis and with the presentation of the engineering approach to functional decomposition that a philosophical analysis of engineering functional descriptions is feasible and beneficiary. Yet, our analysis is still a first step aimed primarily at clarity on conceptual and mereological aspects of functional descriptions, and — hopefully — opening the way to a versatile and more broader analysis of functional decomposition in the engineering sciences. ACKNOWLEDGMENTS Research by Pieter Vermaas was supported by the Netherlands Organization for Scientific Research (NWO). BIBLIOGRAPHY [Bechtel and Abrahamsen, 2005] W. Bechtel and A. Abrahamsen. Explanation: A mechanist alternative. Studies in History and Philosophy of Biological and Biomedical Sciences, 36, 421—441, 2005. [Bell et al., 2007] J. Bell, N. Snooke, and C. Price. A language for functional interpretation of model based simulation. Advanced Engineering Informatics, 21, 398—409, 2007. [Bryant et al., 2004] C. Bryant, D. McAdams, R. Stone, T. Kurtoglu, and M. A. Campbell. A computational technique for concept generation. In Proceedings of IDETC/CIE 2005, Long Beach, CA, paper no. DETC2005-85323, ASME, 2004. [Casati and Varzi, 1999] R. Casati and A. Varzi. Parts and Places. MIT, 1999. [Chakrabarti and Bligh, 2001] A. Chakrabarti and T. P. Bligh. A scheme for functional reasoning in conceptual design. Design Studies, 22, pp. 493—517, 2001. [Chandrasekaran, 2005] B. Chandrasekaran. Representing function: Relating Functional Representation and Functional Modeling research streams. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 19, 65—74, 2005. [Chittaro and Kumar, 1998] L. Chittaro and A. N. Kumar. Reasoning about function and its application to engineering, Artificial Intelligence in Engineering, 12, 331—336, 1998. [Craver, 2001] C. F. Craver. Role functions, mechanisms, and hierarchy. Philosophy of Science, 68, 53—74, 2001. [Craver and Bechtel, 2006] C. Craver and W. Bechtel. Mechanisms. In The Philosophy of Science: An Encyclopedia, S. Sarkar and J. Pfeifer, eds., pp. 469—478. Routledge, 2006. [Cummins, 1975] R. Cummins. Functional analysis. Journal of Philosophy, 72, 741—765, 1975. [de Ridder, 2006] J. de Ridder. Mechanistic artefact explanations. Studies in History and Philosophy of Science, 37, 81—96, 2006. [de Ridder, 2007] J. de Ridder. Reconstructing Design, Explaining Artifacts: Philosophical Reflections on the Design and Explanation of Technical Artifacts. Simon Stevin Series in the Philosophy of Technology, Vol 4 (Delft University of Technology Ph.D. Thesis). 2007.

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ARTEFACTS IN FORMAL ONTOLOGY Stefano Borgo and Laure Vieu

1

INTRODUCTION

Since the early 1990s, there has been an increasing interest, in the knowledge representation area, in formal systems which aim at describing general notions. Indeed, as the research community became aware of the limits of ad hoc approaches such as expert systems (that focus on very specialized domains and pay no attention to flexibility and reusability) and conceptual modeling frameworks (which are limited to capturing the distinctions explicitly needed in the target application or domain), the need to provide clear and unambiguous meaning to notions across knowledge domains became self-evident. Simultaneously, researchers started to look to the philosophical tradition for the characterization of general concepts and relations and to build formal systems based on ontological distinctions. All this led to a new area of research now widely, but perhaps naively, known as applied ontology or even simply ontology. Research in this area is both theoretical and application driven since it aims to reconstruct and to organize philosophical views into sophisticated formal systems whilst achieving the semantic integration of various information systems. Although the term ontology has been endowed with several meanings in the knowledge representation field, it does not directly refer to the discipline that goes back at least to Aristotle and has influenced most of western philosophy. The two disciplines are clearly distinct since the general goal of applied ontology is to construct and apply knowledge structures in order to reliably and automatically manipulate information content, and is motivated by research in areas like information retrieval, data management and conceptual modeling. Nonetheless, applied ontology is strongly linked to the philosophical approach because it relies on general philosophical principles and considerations to justify the various adopted knowledge structures. In this chapter we are interested in the ontological systems, hereafter referred to as ontologies, which satisfy the two main requirements of being formal and foundational. Roughly speaking, an ontology is formal if it is expressed in a logic language endowed with clear semantics (for instance in model-theoretic terms as first-order predicate logic [Hodges, 1983]). This choice is not determined by application concerns (at least not primarily), it emphasizes the relevance that semantic transparency has in this domain. By foundational ontologies we mean those knowledge Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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Stefano Borgo and Laure Vieu

systems that focus on very general and basic concepts (like object, event, state, quality) and relations (such as constitution, participation, dependence, parthood). Often the term formal ontology is used to cover both the above requirements, thus reminding us of Husserl’s distinction between formal logic and formal ontology. In this specific meaning, formal ontology is the study of the interconnections between entities, properties, parts, wholes and collectives. These are considered to be “formal” because they can be exemplified by objects in all domains of reality [Smith, 1998]. To take yet another perspective, one can say that formal ontology is the study of formal (logical) systems which are: general, since they include the most usable and widely applicable concepts; reliable, as they are logical theories with clear semantics, a rich axiomatization and carefully analysed formal consequences (theorems); and well organized, because they are based on philosophical principles the choice of which is explicitly motivated and remains independent from particular domains. In this work, when using the expression “formal ontology” we will be referring to this latter general characterization. Among the ontological systems that have been used in applications, there are just a few that more or less satisfactorily present these properties. They are bfo, dolce, gfo, opencyc, and sumo. They will be discussed in the following sections. We anticipate that unfortunately only a couple of these ontologies will include an explicit characterization of artefacts, the topic of this chapter, and that these existing characterizations will be quite shallow. An ontological system that properly models artefacts would have large potentialities in applications where artefacts are central. For instance, information systems that control processes in the manufacturing industry must rely on a rich but coherent notion of artefact. The integration of artefact data is crucial to concurrent engineering scenarios and to product lifecycle management. Our goal is to show that a satisfactory characterization of the artefact category can be made within a system as complex as a foundational ontology and within the constraints of classical formal logic, in other words, via an axiomatization. In particular, this means formalizing the notion in such a way that it does justice to several of the crucial properties (derived from philosophical considerations as well as from practical usage) that we usually ascribe to artefacts. At the same time, the definition has to remain independent from particular application domains. We achieve this goal by working within a specific foundational ontology (dolce) which, in its present form, lacks a characterization of the notion of artefact. However, our work goes beyond the extension of this system since it provides a general analysis of the category of artefacts which is helpful when formalizing this and related notions in other formal systems as well. It is important to note that in this enterprise we place ourselves within the framework of an ontology of social reality. Social reality [Smith, 1995; Searle, 1983] has to do with the part of reality that covers groups of agents and the social relationships therein, actions that are either collective or directed towards a social group, and the whole range of relevant resulting “social entities”, such as contracts or companies. Such entities are often dependent on mental attitudes, either

Artefacts in Formal Ontology

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individual or collective. A formal ontology dedicated to social reality takes into account all such entities in its domain and attempts to characterize them by modeling general properties and facts. The ontology of social reality and the ontology of mind need to be separated from epistemological studies that would account for the ways in which an agent constructs his or her beliefs about reality, for instance by categorizing entities. Formal ontology takes for granted an objective1 point of view on reality, that is, a point of view that is external to any particular agent. This stance justifies the introduction of notions like “intentional selection” or “social artefact”, which are important to our approach as well as to the philosophical debates that inspired us.

2

FORMAL ONTOLOGY, SYSTEMS AND CHOICES

In this section, we shall begin by giving a brief description of the relevant foundational ontologies. This presentation, though admittedly brief and limited, provides information on the development and quality of the available ontological systems.2 Later we shall go on to discuss some of the ontological issues which help in the characterizing and comparing of these systems. Although the literature on foundational ontologies and their comparison remains scarce, the ontological topics we will review have largely been analyzed within the context of the philosophical tradition [Rea, 1997].

2.1

Existing formal ontologies

Basic Formal Ontology, BFO. The development of bfo3 was initiated in 2002 by the Institute for Formal Ontology and Medical Information Science (IFOMIS, first at the University of Leipzig and later at Saarland University). What characterizes this ontology is the careful description of both the general philosophical viewpoint and the organization of its structure. The ontology is only partly axiomatized and is not aligned with other knowledge systems, e.g. lexical resources. bfo is actually a framework of sub-ontologies linked together by formal relations. Every sub-ontology must be conceived of as a particular perspective on reality: the user selects the sub-ontology that she finds most appropriate to capture the aspects of the world she is interested in. The most important ontologies in bfo are: SNAP (a series of time-indexed snapshot ontologies, these are ontologies of endurants which, roughly speaking, are objects) and SPAN (a single ontology of perdurants which are, in rough terms, events). SNAP-bfo provides a list of all the entities existing in time such as cars, animals and mountains. They can be seen as 1 Certain ontologies adopt a cognitive approach: the categories of entities and the relations used to represent reality are chosen for their compatibility with those arguably used by humans in their language structures and/or their conceptual notions. If a cognitive approach is adopted this does not necessarily mean that the represented facts have to be subjective. 2 Data in this section have been collected in July 2007. 3 http://www.ifomis.org/bfo

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“a snapshot of reality” with no temporal extension. By contrast SPAN-bfo is a catalogue of events which necessarily occur over the course of time such as races, deaths and avalanches. SNAP and SPAN are intertwined via transontological relationships since SNAP entities participate in SPAN entities. bfo (version 1.1) consists of about 40 classes (categories) and is formalized in the weak language known as OWL (Web Ontology Language [Antoniou and van Harmelen, 2004]). It is partially available in first-order logic as well [Masolo et al., 2003]. bfo, which is freely available, has so far been mainly applied in the biomedical domain. Descriptive Ontology for Linguistic and Cognitive Engineering, DOLCE. dolce4 has been developed at the Laboratory for Applied Ontology (LOA), which is part of the Italian ISTC-CNR, as a reference module for a library of ontologies (mainly within the context of the WonderWeb Project). dolce has a definite cognitive bias since it aims at capturing the ontological categories underlying natural language and human common sense. The modelling choices and the various dolce terms and expressions are influenced by philosophy and linguistics. It has a tree-structure that is obtained by applying a “top-down” type of methodology. dolce provides a rich axiomatization of the different main categories and their relationships and it has been aligned to WordNet [Fellbaum, 1998; Prévot et al., 2005]. It is publicly distributed (see licence on the web site) and available in first-order logic (including KIF) and weaker languages like OWL, DAML+OIL and RDFS. It is also distributed as a software running in CASL, the Common Algebraic Specification Language,5 which makes available certain theorem provers and graphical devices. It is actively used in several projects in a variety of domains such as manufacturing, linguistics and the Semantic Web. Further information will be provided on this ontology in Section 4. General Formal Ontology, GFO. gfo6 was developed at the Onto-Med Research Group of the University of Leipzig. It consists of a taxonomy of entities, a taxonomy of relations, and an axiomatization in first-order logic (an axiomatization that is currently still under development). gfo is philosophically well motivated and its ontological choices clearly stated. A crucial guideline for gfo ontology resides in the notion of “levels of reality”. These are the material level (biological, chemical and physical), the mental level (that of psychological phenomena) and the social level (where we find agents, organizations and societies). This structure of levels forms the very basis of the ontology from the point of view that every element in gfo is required to participate in at least one of these levels. Note that gfo is a component of a larger perspective since it forms part 4 http://www.loa-cnr.it/DOLCE.html 5 http://www.brics.dk/Projects/CoFI/CASL.html 6 http://www.onto-med.de/en/theories/gfo/. At the time when this paper was being written a new version of gfo had just been presented. We therefore regret it has not been possible to fully analyze this ontology and compare it with all the others.


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of the Integrated Framework for the Development and Application of Ontologies (IFDAO), an evolution of the General Ontological Language (GOL) project which dates back to 1999. The OWL axiomatization of the previous gfo version consisted of about 80 classes, some 100 subclass-relations and around 70 properties. We expect the new version to be of a comparable size. The Onto-Med group is active in the biomedical domain which explains why gfo has been predominantly applied in that area but it has also been implemented in conceptual modeling. As far as we know, no link to WordNet [Fellbaum, 1998] has yet been developed. The ontology is available through a modified BSD Licence. OpenCyc and Cyc. Cyc, owned by Cycorp Inc., is not a proper foundational ontology but rather a very large, multi-contextual knowledge base enriched with inference engines. It started being constructed in 1984 the aim being to develop a computer program “equipped” with a large amount of commonsense knowledge. The name “Cyc” derives from encyclopedia. The purpose of the project is not, however, to build an electronic encyclopedia, but rather to “complement” such information resources [Guha and Lenat, 1990]. The idea was to create a knowledge base with millions of everyday terms, concepts and rules that would capture the na¨ıeve human knowledge bank of reality. To ensure the consistency of the system, the information stored in Cyc is segregated into hundreds of contexts or “microtheories” which are ontologically primitive entities. Essentially, a microtheory is a bundle of assertions that share common assumptions about the world (microtheories are isolated by topics, granularity, culture, etc.). One basic assumption in Cyc is that everything is a member of some microtheory. opencyc7 was first presented as the “semantic heart” of Cyc but in reality it is just the open source version of the whole Cyc. Ontologically it appears to be deeply affected by cognitive assumptions since its categories try to capture na¨ıeve conceptions of the real world or, more simply, common sense knowledge. opencyc adopts a dedicated language (CycL), an extension of first-order logic. It adopts a set-theoretical approach and makes available hundreds of thousands of terms (categories and relations) and millions of assertions (facts and rules). Clearly, only a mere fragment of more general categories may be qualified as foundational. This fragment is not explicitly singled out by the developers. If one looks at the inheritance relation opencyc has a graph-like structure revealing a “bottom-up” approach, that is to say, the organization of the ontology was developed at a later stage to systematize the data present in the knowledge base. opencyc provides connections with other formalisms and domains: (partial) translators to OWL and Lisp, a connection to WordNet’s synset structure [Fellbaum, 1998; Prévot et al., 2005], and a Cyc-to-English generator. It should be emphasized that the overall system suffers from its commercial targets. For example, the semantics of Cyc partly depends on the implemented 7 http://www.opencyc.org

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inference engines which shows the relevance of performance at run-time and blurs the semantic transparency of categories and relations. In addition, the characterization of the ontological choices on which Cyc and opencyc are based seems to be a secondary task: the documentation is still incomplete and references to the established literature are lacking. opencyc does not claim to be a foundational ontology, nor is it a proper top-level ontology because of its size. Nevertheless, it formalizes top categories and their mutual relationships, provides fairly extended descriptions for most of the categories and the examples provided are helpful in conveying (at least in part) the intended meaning of the terms. Finally, the success of this long-standing project (it has been running for almost 25 years) is definitely questioned today.8 Suggested Upper Merged Ontology, SUMO. sumo9 was created by a private company in 2000 when publicly available specialized ontologies were merged into a single structure in an attempt to obtain a top-level ontology that would be suitable for interoperability, communication and search in the Semantic Web area. The project includes Sowa’s upper level ontology [Sowa, 2000] and the work of Guarino and colleagues on theories of space, time and objects [Borgo et al., 1996]. sumo is not directly influenced by a specific theoretical approach instead it selects from various ontological proposals the categories that seem to be of general use and are broadly accepted by the knowledge representation community. Thus, like OpenCyc, sumo is not a proper foundational ontology. Nonetheless, it is often included among the others because it is a large ontology used in several applications and one that provides an axiomatization of its terms in a rich language. sumo is divided into “sections” or kinds of complementary ontologies that cluster about 1000 terms and relations, 4000 axioms, and 750 rules (but the volume increases considerably if we include all the related domain ontologies). The sections isolate relevant topics: the Mereotopology ontology, for example, contains concepts that deal with the formalization of a general part/whole relation, while the Unit of Measure ontology provides definitions for unit systems. The inheritance structure of this ontology forms a tree obtained through a top-down methodology. It provides an axiomatization of the categories and their relationships in a version of first-order logic known as SUO-KIF as well as in OWL and can be exploited via several theorem provers. It is available in different natural languages and linked to WordNet [Fellbaum, 1998; Prévot et al., 2005]. sumo has been implemented in several projects. The distribution of the ontology is regulated by a licence (see the web site).

8 In specialized mailing lists like SUO (http://suo.ieee.org/) and ontolog-forum (http://ontolog.cim3.net/) a number of discussions have been conducted on this issue. 9 http://www.ontologyportal.org/


2.2

279

Ontological choices

So, ontologies are knowledge systems. They provide a framework in which every entity and relation we want to talk about can be classified. The construction of such a general framework is not simple and it relies on various basic principles, principles that are studied in depth in metaphysics. The best ontologies do indeed refer to the philosophical literature. Universals, particulars and tropes. The ontological distinction between universals and particulars can be characterized by taking the primitive relation of instantiation: particulars are entities that cannot have instances; universals are entities that can have instances. Linguistically, proper nouns are normally considered to refer to particulars, while common nouns refer to universals. For example, Pavarotti, the Italian tenor, is an instance of “person”, but he cannot himself be instantiated. (This characterization of the concept of universal is admittedly imprecise since it does not, for instance, clarify whether sets, predicates and abstract entities should be considered as universals or not. A complete presentation of the different notions demands an analysis of these other entities. Fortunately, we do not need to go into further details to understand the overall position of the ontologies described in Section 2.1.) By adopting a different ontological perspective, one can reject universals and rely on other entities in the way done in the trope theory [Campbell, 1990]. There one claims that the “whiteness” of the specific piece of paper one is holding is a trope (a located property or individual quality) while the universal “white” does not in fact exist. Roughly speaking, tropes are properties of specific material entities upon which they ontologically depend because if the entity ceases to exist, so too does the trope. Tropes do not have instances and cannot be confused with universals. The crucial ontological choice is the decision to include universals in the domain of the ontology, a necessary step if we want to refer to and classify them within the formalism. Some ontologies, such as dolce and sumo, are examples of foundational ontologies of particulars that do not refer directly to universals. opencyc and bfo admit both particulars and universals into the domain. dolce and bfo include some forms of tropes as well. Abstract and concrete entities. Abstract entities (or abstracts) are entities that do not exist in space or time which means to say that they are not located. In contrast, concrete entities (or concretes) are defined as entities that do exist, at least in time. Mathematical objects (like numbers and sets) are examples of abstracts, while ordinary objects (like cars and planets) and events (such as the 2008 Olympiad and the Second World War) are examples of concretes. The ontological formalization of abstracts seems to depend on negative properties (i.e. a lack of location) but that is not quite correct: one can take a different tack by claiming that abstracts are eternal and immutable in that they exist at all times

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and are unchangeable. A third alternative definition is based on the “causal criterion”: abstracts possess no causal power while concretes do. Note that in this way we have already switched to a different notion of abstract entity: if abstracts are “timeless”, as in the first definition, then it seems awkward to include them in causal relations; conversely it is possible to individuate entities located in time and space that lack any causal power, like the center of mass of the solar system [Lowe, 1998]. Existing ontologies tend to focus on the first kind of characterization. In dolce, temporal and/or spatial locations are not defined for categories like Abstract Quality and Abstract, so it is roughly the “negative” perspective that is adopted.10 dolce also distinguishes between “direct” and “indirect” location. Some entities do not have a direct location but they inherit their locations from entities on which they depend: tables inherit their temporal locations from the temporal locations of events of which they are participants. In opencyc, instances of the class SetOrCollection do not have spatial or temporal locations, thus opencyc adopts the same “negative” perspective on abstracts. Elements in TemporalThing, a subclass of Individual, are at least located in time while SpatialThing, also a subclass of Individual, are at least located in space. It is not clear if there are instances of SpatialThing that are not also instances of TemporalThing, that is to say, individuals that are located in space but not in time. If not, then all SpatialThing (like all TemporalThing) are concretes. In sumo, the distinction between Physical and Abstract is very similar to the distinction between concretes and abstracts: elements in Physical are said to be entities “that have a location in space-time” and in Abstract they are entities that “cannot exist at any particular place and time without some physical encoding or embodiment”. As far as we can see, the bfo ontology only takes into account entities existing in space and/or in time, that is to say, only concrete entities. Endurants and perdurants. Classically, endurants (also sometimes called continuants or objects) are characterized as entities that “are” in time; they are wholly present (all their proper parts are present) at any given time of their existence. On the other hand, perdurants (also called occurrents or events) are entities that “happen” in time, they extend in time by accumulating different “temporal parts”, so that, at any time t when they exist, only their temporal parts at t will be present. For example, the car you now own can be viewed as an endurant because it is now entirely present, while “your driving to the office” is a perdurant because “your driving out of the garage” is not present when “your driving through the city centre” happens (assuming that these are events that actually occur when you drive to the office). Sometimes only perdurants are admitted in an ontology. It then becomes possible to distinguish between ordinary objects (like “a person”) and events or processes (like “a person’s life”), relying on properties that lie outside 10 The courier font is used to denote the names of categories or classes of entities in the ontologies described. It is a notational system that is also adhered to in the quotations regardless of the authors’ chosen system of notation.


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spatio-temporal aspects. (It should be noted that other ways of characterizing endurants and perdurants have also been proposed.) dolce assumes a classical view which accepts both the concept of endurant and that of perdurant. opencyc has a similar view: the class SomethingExisting (i.e. entities that remain relatively stable throughout their lifetimes) corresponds fairly closely to the classical concept of endurant. Analogously, SituationTemporal (or the union of Event and StaticSituation) corresponds to the classical notion of perdurant. In the case of sumo the distinction is between Object and Process. Here processes are characterized as “the class of things that happen and have temporal parts or stages”, while for objects a less standard interpretation is accepted: “an Object is something whose spatio-temporal extent is thought to divide into spatial parts roughly parallel to the time-axis”. In any case, note that in sumo objects and processes are considered to be necessarily located in the space-time. In bfo the distinction endurant-perdurant forms the basis to the development of two separate sub-ontologies (in other words, the two types of entities do not coexist in the same ontology): SNAP-bfo contains only endurants, while SPAN-bfo contains only perdurants. Co-located entities. No matter what one decides about the ontological status of space and time, one can include spatially and/or temporally co-located objects. It is natural to accept objects that are temporally co-located (at least in part), like the moon and the earth or oneself and one’s clothes but the embodiment of spatially (or rather spatio-temporally) co-located distinct objects can sometimes be questioned. This issue is addressed by posing questions like: Is a hole different from the region of space it occupies? Is a statue different from the matter which constitutes it? Is a person different from his or her body? The subject is complex and includes rather difficult relations like identity across time, material constitution, essentiality and modality. dolce, which takes a multiplicative approach, uses spatial co-location and the relations of dependence and constitution in order to “stratify” co-located entities. For example, persons (elements of Agentive Physical Object) are constituted by their bodies (Non-agentive Physical Object), and elements of Physical Object are constituted by elements of Amount of Matter. opencyc has a weaker position. It sometimes adopts a genuine multiplication of co-located entities (e.g. it considers a statue and the matter that constitutes it as distinct co-located entities). On the other hand, it takes persons to be entities in the class CompositeTangibleAndIntangibleObject, so that Marilyn Monroe, for instance, has two components: a “body” and a “mind”. sumo, as far as we can see, does not have an explicit position on this issue. It might be that this ontology suffers from the heterogeneity of the basic theories on which it is founded (see page 278). Finally, bfo approaches this issue by distinguishing between different SNAP ontologies: a statue would be an element of an ontology of art (or of social reality) while the material it is made of would fall into an ontology of physical reality.

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3

ARTEFACTS IN EXISTING ONTOLOGIES

This section examines the present situation regarding the formalization of the notion of artefact in formal ontology. In this respect, it constitutes a preliminary step to our study that begins in Section 5. Generally speaking, the study of artefacts has attracted the attention of researchers from different domains ranging from engineering to philosophy and psychology to linguistics. Despite this wide interest, all attempts to either formally or informally characterize a shared notion of artefact have come up against serious problems. Existing formal ontologies indirectly register this fact. Indeed, of the five systems listed above, only opencyc and sumo include a category of artefacts. This might be partly attributable to contingent aspects: certain formal ontologies are still strengthening their top-level concepts, while others focus primarily on domains where the role of artefacts is marginal, such as in the biomedical study of living organisms.11 In other cases, it is all the result of specific choice: the notion of artefact may not be considered by some to be so general and basic that it warrants inclusion in foundational ontology. Nonetheless, we suspect that were a clear and shared characterization of artefact available in the literature, all formal ontologies would happily make it part of their system. After all, it is indisputable that artefacts are omnipresent components of our social life. These considerations highlight the need to extend and enrich the debate on the properties that distinguish artefacts from other entities, a topic that will be resumed later in the paper. For now we shall consider how the category of artefact is introduced into the two formal ontologies that deal with it. Since we are not interested in the particular formalization of these systems, we shall concentrate on the overall notion by looking at the inheritance structure for this category and at the explanations accompanying the relevant categories but we will not take into account the formal issues.

3.1 Artefacts in opencyc In opencyc,12 the class Artifact is part of UniversalVocabulary, one of the most general microtheories of opencyc. From the given description, Cyc’s assertions on this concept are “intrinsic to the [artefact] concept’s nature and cannot be violated in any context”. That is, no exceptions are possible, not even within other microtheories. The top class is actually called Artifact-Generic and is described as “a collection of things created by agents” where an Agent-Generic is a “being that has desires or intentions, and the ability to act on those desires or intentions” (it includes social organizations like legal corporations and animals). Elements 11 See,

for example, the Gene Ontology: http://www.geneontology.org/index.shtml. and citations are from opencyc 1.0.2: http://www.opencyc.com.

12 Data


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of Artifact-Generic like a hammer or a bird nest can be tangible or alternatively intangible like a set of laws. The category Artifact-Generic breaks down into Artifact and Artifact-Intangible. If we ignore the latter (which collects entities like computer languages and legal agreements), an element of Artifact may be said to be an inanimate thing which is “at least partially tangible” and “intentionally created by an agent (or group of agents working together) to serve some purpose or perform some function”. The result of an assembling operation or of a modification of existing matter may not be an artefact unless the creating agent performs it intentionally and with a purpose. In opencyc some amounts of matter are classified as artefacts under the class ArtificialMaterial, a subclass of Artifact. According to the informal description, this class contains “portion[s] of artificial stuff that was intentionally made by some agent(s), such as Plastic...” but excludes the byproducts of such activities. Note that the top category Artifact-Generic has a second (orthogonal) partition. The subcategories here are Artifact-NonAgentive and Artifact-Agentive. The first class collects artefacts which are not agents, like bicycles whilst the latter collects agents which are themselves created by agents, such as organizations. Unfortunately the opencyc documentation gives no information on the underlying view and no link with the specialized literature is provided. Some rationalisations and underlying intuitions are to be inferred from the category descriptions, when provided, and the formalization itself (for which the licence is needed). Finally, the hierarchy of concepts below the Artifact category seems to be more driven by application goals (like the need to have a detailed and broad coverage of concepts of specific domains) than by ontological factors. Otherwise, it seems difficult to justify the presence (at the same level in the hierarchy) of Artifact subcategories like InstrumentalArtifact (“A sub-collection of Artifact. Each instance is an artifact (or system of artifacts) that is instrumental in accomplishing some end.”), ItalianCuisine (“the collection of instances of what many Americans tend to think of as Italian food”), and StuffedToy (no description provided).

3.2

Artefacts in sumo

In sumo13 , an artefact is described as a “CorpuscularObject that is the product of a Making” where an element of the CorpuscularObject is a “SelfConnectedObject whose parts have properties that are not shared by the whole”. These descriptions do not provide a clear view because sumo adopts a very general notion of property which means that if one has two entities (e.g. an object and one of its proper parts) it will seem possible to find properties with which to distinguish them (for instance, properties relative to size). From the above definitions, it must follow that sumo artefacts are located in space-time and are self-connected. In other words, sumo artefacts are non-scattered and physical. This confinement to physical entities is adopted in several discussions even in the philosophical litera13 Data and citations derive from the sumo webpage: http://www.ontologyportal.org/ (Sept. 2007).

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ture but the constraint on self-connected objects is new and not explicitly justified: a jigsaw puzzle would count as an artefact in sumo only when assembled. Another peculiarity resides in the description of the category Making seen above and said to characterize artefacts. Making is described as follows: “The subclass of Creation in which an individual Artifact or a type of Artifact is made”. In turn, Creation is said to be “the subclass of Process in which something is created”. These descriptions are hopelessly circular, they do not help us to understand the intended meaning of the categories. When tracing the links between categories, we did not find a direct relationship between the part of the hierarchy containing Making and the category Agent. This is surprising because in the relevant literature the direct and strong connection which exists between artefacts and agents is crucial to the ontological status of artefacts. From the information collected, we can conclude that the class Artifact in sumo captures the notion of physical, self-connected, and made (created) objects. Since the terms and relations used in sumo are poorly characterized and documented, the true extent of this class of sumo remains obscure.

3.3 Artefacts and the other ontologies The foundational ontologies bfo, dolce and gfo do not introduce artefacts in their hierarchy. However, two of them, dolce and gfo, seem to be in a position to provide a definition for the category. From our previous description, it can be concluded that bfo has a limited number of categories and thus few expressive tools to introduce artefacts. If it is true that the ontology has a minimal notion of function, the lack of axiomatization and the limited number of classes makes the formalization of a category of artefacts problematic: one should introduce several preliminary notions simultaneously. We are not aware of any attempt to extend bfo with a category of artefacts. dolce does not provide a notion of function but it has a very expressive framework to deal with qualities. However, some categories in the hierarchy are not fully formalized while some of these (e.g. Social Object) are crucial to model artefacts. To our knowledge, there has been no attempt to extend the ontology in this direction. gfo has carried out an interesting study on the notion of function and it has a fairly rich hierarchy that may provide the tools to define the category of artefacts or, at least, a generalization of it in terms of functionalities. The developers of gfo have some idea of how to tackle the artefact category14 but the ontology is still being developed and no extension of this is expected in the near future. Since artefact is a notion that has direct consequences for applications one might assume that expanding our analysis to include non-foundational ontologies would lead to an interesting characterization of this notion. However, ontologies developed within certain application domains only rarely introduce categories of 14 H.

Herre, personal communication.


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artefacts (but one often finds a subcategory for products). Even then their characterization of artefact (or of product, for that matter) is of little or no help. These ontologies are poorly characterized and the descriptions of their categories show that they are based on practical considerations and rely on the implicit knowledge shared in the community they address. Their proposals are therefore only valid when applied to their particular domain but remain, even in these cases, quite minimal. 4

THE DOLCE FOUNDATIONAL ONTOLOGY

Our next step is to elaborate a formal notion of artefact that is philosophically motivated. To surmount the problems shown above we will make explicit our modeling choices while also relating our approach to major philosophical positions in this area. This analysis will, we hope, be widely applicable. We want it to go beyond a philosophical discussion though. We also want to establish a formal characterization based on a specific formal framework. In the following pages we will therefore try to establish a balance between achieving a general analysis of the notion of artefact and recognizing the constraints imposed by the language and ontological choices behind the specific ontology used. If we leave aside opencyc and sumo which, as pointed out in their respective descriptions, are not proper foundational ontologies, we can choose between bfo, dolce and gfo. These systems all seem quite promising but each has its own particular drawbacks. If we bear in mind that bfo is only partly axiomatized and provides only a few categories, and that gfo is still working out the formalization of its new version, then it would seem that we would be better off working with the dolce ontology. A positive feature of this latter ontology is its rich and flexible framework for modeling qualities which provides an interesting theoretical tool for the capturing of formal and practical distinctions. It will therefore be exploited extensively in our work. On the negative side, the ontology only focuses on particulars (individuals), as it will become clear below.

4.1

An introduction to dolce

The Descriptive Ontology for Linguistic and Cognitive Engineering, dolce [Masolo et al., 2003] (www.loa-cnr.it/DOLCE), concentrates on particulars, that is, endurants, perdurants, qualities and abstract entities. It does not attempt to provide a taxonomy of properties and relations which are only included in the system if deemed crucial for characterizing particulars. We mentioned above that dolce adopts a multiplicative approach: it assumes that different entities can be co-located in the same space-time. For example, a car and its matter are captured in dolce as two distinct entities (as opposed to being different aspects of the same entity). The reason for this lies in the different sets of properties that these entities enjoy: the car ceases to exist if a radical change of shape occurs (e.g. when it is crushed and cannot be repaired) while the amount of

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matter is not affected by this type of change (changes in spatial properties, like in shape or connectedness, is irrelevant for the identity of an amount of matter; only mereological properties can affect it). Going back to the classical example of the statue made of clay, for example, dolce might be said to model the statue and the amount of clay as different entities which share the same spatial (and possibly even temporal) location; the amount of clay used constitutes the statue. This allows the user to capture the strong intuition that a scratched statue is different (since it is scratched) while still remaining the same statue that it was before. In dolce this is possible because the identity of the statue itself might not be affected by minor scratches, but the identity of the clay is because scratches are the result of parts of the clay breaking off. The category Endurant collects objects like cars and bits of matter like steel blocks, while events like the making of this car and the moving of that steel block fall into the category of Perdurant. The term “object” itself is used in the ontology to capture a notion of unity or wholeness as suggested by the partition of the category Physical Endurant into Amount of Matter whose elements are (amounts of) gold, air, etc.; Feature (a hole, a boundary); and Physical Object (a hammer, a human body). See Figure 1. Some of the categories are informally described in Section 4.2.

PT Particular

ED Endurant

PED Physical Endurant

M Amount of Matter

F Feature

POB Physical Object

PD Perdurant

NPED Non-physical Endurant

…

EV Event

AS Arbitrary Sum

NPOB Non-physical Object

Q Quality

TQ Temporal Quality

STV Stative

ACH Achievement

ACC Accomplishment

ST State

PRO Process

…

…

…

…

… TL Temporal Location

PQ Physical Quality

… SL Spatial Location

AB Abstract

AQ Abstract Quality

…

…

TR Temporal Region

… APO Agentive Physical Object

NAPO Non-agentive Physical Object

MOB Mental Object

SOB Social Object

ASO Agentive Social Object

SAG Social Agent

T Time Interval

Fact

Set

PR Physical Region

… S Space Region

R Region

AR Abstract Region

…

NASO Non-agentive Social Object

SC Society

Figure 1. Taxonomy of dolce basic categories. (From [Masolo et al., 2003])


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Every subcategory of Endurant and Perdurant is associated with a group of qualities. Qualities and their values (qualia) form distinct categories of entities in dolce and the distinction between individual quality, quale, and quality space has been established in order to capture several common sense intuitions in a coherent and consistent way. Individual qualities, like the colour of this pen, inhere in specific individuals meaning that the colour of this pen is different from the colour of that pen no matter how similar the two pens may be. These qualities can change over the course of time since the colour of this pen can match the colour red today and the colour pink tomorrow. In contrast to individual qualities, qualia are not entity dependent. An example of a quale is a specific colour, like, for instance, red. Intuitively, these entities are obtained by abstracting individual qualities from time and from their hosts (see the discussion on tropes in Section 2.2). If the colour of this pen and the colour of that pen match the same shade of red, then they have the same (colour-)quale. In this respect, qualia represent perfect and objective similarities between (aspects of) objects. Quality spaces correspond to different ways of arranging qualia. They are motivated by similarities between objects or aspects of objects. By allowing different spaces for the same quality, different structures can be imposed on qualia (for example, a geometry, a metric, or just a topology) and this makes it possible to differentiate several quantitative and qualitative degrees of similarity (consider, for instance, the different ways of classifying and measuring colours). The actual list of qualities associated with an entity depends on the user. Standard examples of qualities are shape and weight (usually taken to be qualities of endurants) and duration and direction (which are usually qualities of perdurants). However, these examples are not enforced by the ontology itself which is indeed neutral on the topic.

4.2

Some categories and relations in dolce

Several of the categories given in Figure 1 will be used to characterize artefacts. Here we shall just consider a few of them (including their relations) by way of informal introduction to the dolce terminology. Their formal names as used in the next sections are given in italicised parenthesis. The interested reader can find in [Masolo et al., 2003] the formal system together with a more detailed discussion. Let us first recall the general category Endurant (ED) which collects entities that are wholly present at any time when they are present like, for instance, Bush, the first car built by Ferrari and the steel of the Eiffel tower. The elements of Physical Endurant (PED) are the endurants located in space-time, e.g. Gandhi’s glasses as opposed to La Divina Commedia poem. This latter entity is classified as a Non-physical Endurant (NPED). Amount of Matter (M ), e.g. some oxygen, Feature (F ), e.g. a curve, and Physical Object (POB ), e.g. a car have already been mentioned. Regarding agency, Non-agentive Physical Object (NAPO) pertains to the physical objects to which one cannot ascribe intentions, beliefs or

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desires (like a product or a ticket). A person falls into the category Agentive Physical Object (APO) which is different from the category of social entities, called Social Object (SOB ), where we find things like organizations, companies, and their institutional artefacts such as constitutions and cheques. Social systems, such as a linguistic community, the people of a village or western society, form a subcategory denoted as Society (SC ). Entities that happen in time fall into the Perdurant (PD) category. Recall that, in informal terms, a perdurant is an entity that is only partially present whenever it is present. In this category we find happenings like football games and productions. These entities have temporal parts (like the first half of the game) as well as spatial parts, i.e. parts that are spatially identified (like the event being restricted to half of the football field during the game). Note that endurants are not parts of perdurants but they participate in them instead (this relation is labelled PC ). Some perdurants (like finishing a race or reaching the top of a mountain) are further classified in the subcategory Achievement (ACH ). They are distinguished according to two properties: they have no temporal parts (e.g. instantaneous events) and their type is not preserved by sum: if we add together two consecutive events consisting in, say, finishing a book we get a new complex event which does not add up to the finishing of a book. Contrast this with events like drilling or walking: if we add together two consecutive walking events we still have a (possibly complex) walking event. Perdurants that have temporal parts but behave in the same way regarding their sum, like football games, fall into the Accomplishment (ACC ) category. Note the distinction between finishing a book (an achievement) and reading a book (an accomplishment). Achievements and accomplishments form the category of eventive perdurants (EV ). Entities of a different type are found in the Quality category (Q), which covers all individual qualities. As discussed above, individual qualities can be seen as instantiations of basic properties of endurants or perdurants (shape, weight, duration, electric charge; usually qualities can be perceived or measured). The term “individual” is used to mark the fundamental role of the inherence relationship between an entity and its own qualities. Every endurant (or perdurant) comes with its physical (or temporal) qualities. Note that qualities are particulars in dolce that are not to be confused with properties (universals). Expression qt(q, x) stands for “q is an individual quality of x”. Qualities are associated with quality spaces and the position an individual quality has in a space is called a quale. We write ql(r, q, t) to indicate that “r is the quale of the endurant’s quality q during time t” while qlT (t, x) stands for “t is the quale of the temporal quality of x”. (Note the temporal parameter in ql(r, q, t). If we want to evaluate “John is 5 feet tall”, we have to be explicit when this sentence is stated as John’s height changes over the course of time. Instead, relation qlT (t, x) describes temporal location and it is used to formalize, e.g. “the party last Sunday lasted from sunrise to sunset”.) Each quale informally identifies a class of equivalence with respect to some individual quality, that is, with respect to an aspect of the entities. For instance, the same weight quale is associated with all the weight


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individual qualities that are ontologically indistinguishable (i.e. independently of any measuring instrument we have). From the remaining relations we will make use of parthood as in “x is part of y”, written as P (x, y). The relationship being present states when an entity exists in the world, thus one writes PRE (x, t) to mean that “x is present in the world at time t”. Earlier, we mentioned participation: expression PC (x, y, t) stands for “endurant x participates in perdurant y during time t”. Constitution, another crucial relation in dolce denotes a strong form of dependence: K(x, y, t) stands for “x constitutes y during t”. That is the relationship that holds between an amount of matter and a statue so that the statue cannot be present unless the material it is made of is also present. Finally, we will use an extension of dolce proposed in [Masolo et al., 2004], and consider the category of concepts (CN ) together with the relationship classification, written CF , that relates concepts and their “instances” at any one time. One writes CF (x, y, t) to state that “at time t, x satisfies the concept y”. Concepts are not standard universals because concepts are individuals that depend on agents who create them or possibly on societies that adopt them. To account for their dependence, concepts are classified as particulars in this extension of dolce. Above all else, they are endurants, not abstracts, since they exist in time. Creating a concept means among other things providing a definition for it; the satisfaction of a concept is characterized by the constraints stated in the description defining a concept. For instance, the concept of Italian President has been created and defined by the Italian constitution which has been adopted by the Italian people. 5

EXTENDING DOLCE TO ARTEFACTS

We now turn to examining how the formal ontology dolce can be extended to include a category of artefacts. As explained above, this endeavour aims at showing that philosophical findings in this little explored domain can be successfully incorporated into an axiomatic first-order theory. This is not to say that we provide a general definition of artefact tout court. Indeed, in the literature the term “artefact” has been associated with a variety of meanings depending on the research domain and on the specific viewpoint of the authors. Here, we formally develop a coherent view formalized in a way that is compatible with the basic dolce choices.

5.1

Artefacts in the taxonomy

Most authors acknowledge that the notion of artefact seems to cover entities in a large variety of basic categories. Following the dolce taxonomy of basic categories depicted in Figure 1, it can be easily argued that artefacts may be either endurants (bottles and laws) or perdurants (judgements, performances and wars).15 Endurants can be physical (bottles, glass and robots) or non-physical 15 There are no abstract artefacts because according to dolce, all abstract entities are out of time (cf. p. 279). All artefacts are created, even non-physical ones like logical theories or novels.

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(amounts of music, laws and social institutions). For some authors, the whole Social Object category appears to fall under the larger class of non-physical artefacts [Searle, 1995]. Included in physical endurants are amounts of matter (pieces of glass or plastic), physical objects, which can be non-agentive (bottles, pens and paperweights) as well as agentive (robots, and arguably, bred animals and perhaps intended babies), and features (folds in a skirt, tunnels in mountains). So it would seem that artefacts are not a separate category in the ontology, but rather a class of entities overlapping a variety of categories. As we will see below though, the identity criteria for artefacts, that is to say, their intentional nature, force us to regard artefacts as entities which actually are in a separate category. As a starting point for providing a more general notion, we will only focus here on a subclass of the larger category of artefacts. This paper considers artefacts that correspond to physical endurants only, and among them, amounts of matter and non-agentive physical objects only.16 For this first step, we will simply add to the dolce categories the category of Physical Artefact which falls under the category of Physical Endurant as a new sibling of Amount of Matter, Physical Object, and Feature. This category contains the most prototypical artefacts (e.g. tools like knives and pens) or, in other words, the least controversial ones so that we can be confident that it is covered by any specific view on artefacts. It arguably is the most studied category in the literature [Baker, 2004; Kroes and Meijers, 2006; Elder, 2007; Thomasson, 2007]. It is also the easiest to grasp in an ontology that is particularly well developed in the domain of material entities and the related fundamental relations, as is dolce. The category of artefacts considered is quite big and presents a variety of interesting subclasses. We will not go into more specialized notions though; that is, we are not going to provide definitions to distinguish, for instance, “technical artefacts” or “works of art” within this class. These are crucial subclasses but a justification and presentation of their specific distinctions would be too detailed for the purposes of the present chapter. Before proceeding further, we should however make it clear that we are dealing primarily with specific tokens, e.g. with the telephone that sits on Mary’s desk, and not with artefact types like the telephone. This implies that we are ignoring here the important process of designing (possible or impossible) artefacts, a process that often precedes the actual creation of any technical artefact token. The focus on tokens is natural within the dolce framework since, as pointed out in Section 4, this ontology is about particulars. Nonetheless, one sees that from the formalization, a notion of artefact type does emerge. We shall introduce and discuss this notion in Section 7.1. Focusing on token artefacts in an ontology theory means being concerned with the nature of these objects, or in other words with the essential properties that make the difference between artefacts and non-artefacts, They all have a creation time before which they don’t exist and after which they do, i.e. they are in time, and thus non abstract. 16 We actually even exclude living entities from Non-agentive Physical Objects (e.g. plants, viruses), but the remaining subclass is not identified as a category in dolce at present.


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and with the relationships between artefacts and other entities. As stated earlier, we are not concerned with the epistemological processes of recognition and categorization by an agent in the presence of a new object which may possibly be an artefact, nor by the process of designing a new artefact for a given purpose.

5.2

The approach

In accordance with the quite limited, although recently significantly increasing, literature on the ontology of artefacts [Dipert, 1993; Baker, 2004; Houkes and Meijers, 2006; Kroes and Meijers, 2006; Elder, 2007; Thomasson, 2007], our approach seeks to do justice to the complex nature of artefacts, which blends a physical substrate (regarding the category of physical artefacts we are concerned with here) with intentional aspects as well as social aspects. We adopt the view that artefacts have an ontological status, in other words that they are full citizens of our ontology, and that artefacts are essentially the result of an intentional act of their creator. As we shall see below, two entities are therefore distinguished, the purely physical object or amount of matter that constitutes the artefact and the artefact itself, which emerges when it is created with both its physical and intention-based properties. Among these artefacts, generated by the private intention of their creators, we can further distinguish social artefacts (or artefacts proper in Dipert’s terminology [Dipert, 1993]), whose intention-based properties take on a social dimension, more specifically, they are artefacts which are recognized as such by members of a specific society, e.g. cars.17 In accounting for these complex aspects, we will insist on developing the minimal formal apparatus required to characterize artefacts. For instance, although we need to model intentional aspects, we will avoid as much as possible the direct reference to theories of mental attitudes, which are not per se the subject of this paper and are by no means consensual. Similarly, we will not deal directly with the extremely elusive and much debated notion of purpose or function but will refer instead to a space of capacities, by making use of a general formal tool for describing the qualities of entities. We will not analyze in this paper the space of capacities itself: the structure of such space and the relationship between capacities and functions are issues that warrant further analysis. Here we shall merely introduce the foundations of the formal machinery.

17 The social character of artefacts is intended here in the broader sense. Social artefacts are not confined to the more restricted class of the physical artefacts that have a marked social purpose and use identity (like money and schools) nor to non-physical artefacts which fall in the category of social objects (e.g. laws and organizations).

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6

MODELING ARTEFACTS

6.1 Intentional selection Let us consider first the matter of agent intention which underlies the creation of an artefact. Two aspects need to be distinguished: the intention of obtaining an entity with some desired property (that perhaps makes it suitable for a particular purpose) and the intention of physically modifying or processing some pre-existing entity or entities in order to produce the artefact. We focus here on the first one only because we do not see the action of physical modification as an essential aspect of the creation of an artefact. In other words, artefacts do not need to be artificial entities: a pebble can make a paperweight, and a fallen tree a bench.18 In addition, residues, such as sawdust, are intentionally processed but not intentionally selected for having certain properties and use. Artefacts are, in nuce, created entities in which “created” refers to a mental event, not to a physical modification. One might remark that we do not base artefacts on actual use. The paperweight exists as soon as the agent selects it on the beach, and not just at the moment when he places it on a pile of loose papers. Similarly, knives coming out of a factory already exist as such; they are sold as knives, even though they have never at that stage cut anything. This approach therefore departs somewhat from Dipert’s views [Dipert, 1993]. Our artefacts are what Dipert terms “contemplated instruments”, in that they may still lack a use to be called “instruments”. In addition, as just explained, we do not restrict artefacts to physically modified entities, so our artefacts may not be termed “tools” (intentionally modified instruments) in Dipert’s terminology. Contemplated instruments, and even only once-used tools, may be far too many for some people’s tastes, but we believe this is not really an issue as one could define and focus on a relevant subclass of artefacts, “stable artefacts”, based on the creator’s repeated use according to her original purpose and/or her maintaining of some specific mental attitudes (memory of the creation, intention to use in the future), regardless of one of the remaining subclasses, the multitude of one-time-used or even one-time-contemplated artefacts. The class of social artefacts is another relevant subclass which will be described in this paper, in Section 7.2. This class arguably overlaps the class of stable artefacts, though one cannot ignore the matter of the many manufactured tools lying, yet unused, in stores and warehouses. So the essence of any artefact lies in the creator’s intention. It is certainly possible to explicitly represent the intentions or goals of the creator in an ontology of mental attitudes [Ferrario and Oltramari, 2004] and to reason about them adopting some dedicated logical formalism, for instance a so-called “Belief, Desire, Intention” logic [Rao and Georgeff, 1991]. As indicated above, for reasons 18 Even though one could argue that transporting the pebble to one’s desk forms some kind of modification, the tree trunk can come to serve as a bench in the very place where it fell. It may furthermore be argued that physical modification has to be restricted to change in intrinsic physical properties, thus disregarding spatial location [Geach, 1969].


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of brevity we prefer to focus on the intentional creation event only and on the product of such an event, the artefact itself. The paperweight is the result of some agent intentionally selecting a pebble and attributing to it certain capacities. The artefact itself is the new entity whose physical realization is the selected object and which has attributed capacities. In particular, the paperweight is a selected pebble together with the attributed capacity to stand firm and hold down paper without damaging it. Ultimately, the artefact might prove not to have the capacities the agent attributed to it, as it could be flawed or malfunctioning. More will be stated on this point later.

6.2

Constitution

We then suppose that the paperweight is not the pebble. The paperweight only starts to exist when it is created, usually well after the pebble has come into existence; the two objects, although co-located when both present, may have different lifetimes and are therefore different. The alternative solution which would be to consider artefactuality as a property that physical endurants may or may not have would avoid the multiplication of entities. But, by not granting artefacts an ontological status, it would not do justice to the notion of creation, intended as a notion distinct from physical modification, nor to the common view that artefacts are sortals [Elder, 2007]. We are thus confronted with two co-located entities, the artefact and the underlying physical object. In addition, the former is dependent on the latter, as the paperweight cannot exist without the pebble. In short, the pebble constitutes the paperweight [Rea, 1997]. The same physical object can constitute two different artefacts, for example the same pebble can constitute both a paperweight and a pestle. Only physical objects or amounts of matter may constitute an artefact, as it is only material artefacts that we are considering here. So, when an artefact is apparently selected from another artefact, e.g. when a coffee-grinder is chosen to be used as a spice-grinder [Scheele, 2005], it is in fact the physical object constituting the first artefact which is selected again. Although we do not dwell here on the special cases of artefacts constituted by aggregates and those which are copies of previously existing models, we agree with Baker and Elder [Baker, 2004; Elder, 2007] that constitution is, in this instance, a powerful tool. As pointed out above, dolce already adopts the corresponding multiplicative approach, in particular to distinguish the statue from the amount of matter that constitutes it. However, in this extension, it is important to note that what directly constitutes the paperweight here is the physical object pebble, and not simply the amount of (rock) matter that in turn constitutes the pebble.19 The pebble is not an amount of rock because it is shape-dependent: the amount of rock persists after crushing, but the pebble does not —we obtain small stones or sand grains. Artefacts therefore bring yet another layer, an intentional level, to the constitution hierarchy. As a result, since the statue is an artefact, we actually need 19 The

amount of matter also constitutes the artefact, as constitution is transitive in dolce.

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to distinguish three co-located entities, and not simply two as argued in dolce and more generally in the literature on material constitution: the intentionally created statue, the specifically-shaped and structured physical object, and the mereologically determined amount of matter.20 We will see below that this further stratification is useful in understanding what happens when an artefact is repaired.

6.3 Capacity What, then, are capacities? Our notion of capacity builds on Cummins’s work on functions [Cummins, 1975]. His behavior-based approach avoids both the etiological account of function often given in philosophy of biology theories and the intentional approach adequate only for artefacts. We do take into account the intention of the agent in the creation event but we characterize, as does Cummins, the function of the artefact in agent-independent ways. To this end, we use the notion of quality in dolce, and assume that all physical endurants, restricted here to the categories Amount of Matter (M ), and Non-agentive Physical Object (NAPO), have a single individual quality named capacity that characterizes all the capacities the physical endurant has. The capacity of an entity is an individual, just as is its colour. This quality maps into a quale that is a region (possibly a sum of atomic qualia) in the capacity space, which can be seen as some sort of functional conceptual space [G¨ ardenfors, 2000]. The quale corresponding to the capacity of an entity at a given time collects all the various dispositions [McLaughlin, 1995; Mumford, 1998] or behaviours the entity is able to express at that time. For instance, the capacity of this pen now has the quale of writing finely in black when drawn over paper, fitting in one’s hand when grasped, and making a certain noise when it contacts the table. The capacity space is certainly complex, possibly founded on more elementary spaces of quality that provide the “bases” to such dispositions. We also assume that this space extends beyond standard (conditional) dispositions to also include structural properties, that is, the internal arrangement of parts. Our purpose here is not to analyse and describe this space in detail but to give the overall architecture of a possible formal ontological view of artefacts. Further study will certainly be required if we are to understand the structure of capacity space. It may in particular assess the need to use several distinct such spaces instead of a single one, and accordingly, several capacity qualities instead of a single one.

6.4 Attributed capacity In addition to the capacity possessed by any physical endurant,21 artefacts also have an attributed capacity, another quality associated with qualia in the same space. The fact that actual dispositions and intended functions are elements of a 20 When the artefact is not selected from a physical object but from an amount of matter, as with an amount of glass, there are of course two layers only. 21 We assume that an artefact’s capacity is inherited from its constituting entity.


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same space has a number of advantages. It first of all permits the definition of malfunctioning (see Section 7.3 below). It furthermore demonstrates how the dual nature of artefacts that are physical endurants can be reconciled as it forms the interface for the mental and the physical aspects of artefacts [Kroes and Meijers, 2006]. This unique space also makes possible a future account of the design process. As the capacity space also covers structural properties, the very design plans of technical artefacts could be conceived as part of the attributed capacity. Although capacity and attributed capacity map into the same space of qualia, the former remains a physical quality whereas the latter is an intentional quality as it is dependent on the intentions of the creator at the time of the creation event.22 Capacity and attributed capacity also differ in the following way: the quale associated with the attributed capacity does not change in time as it is fixed by the creation event; moreover, this quale is a set of regions of the capacity space because the intended behaviour of the artefact need not be specified in a precise way, and may present vagueness. For instance, when one is looking for something to write on a board and one selects a piece of coal the value of the attributed capacity is only to write on the board and fit in the hand. Therefore, the attributed capacity maps into the qualia space in a variety of alternative regions corresponding to different possible writing behaviours, e.g. writing finely in black on the board and fitting in one’s hand, writing thickly in black on the board and fitting in one’s hand, writing finely in brown on the board and fitting in one’s hand, etc. On the other hand, the creator of a commercial pen has probably designed it precisely and has therefore chosen an attributed capacity which maps to a reduced number of regions or even to a unique one. This region, though, is a priori smaller than the capacity’s quale region, e.g. the creator of the pen did not design it for the noise it produces when crushed with a rock. So typically the quale of the artefact’s capacity strictly includes one member of the quale of its attributed capacity. This obviously does not hold in the case of malfunctioning or faulty artefacts: one can create an artefact with an attributed capacity’s quale that the selected physical endurant’s capacity (and the artefact’s capacity) will not always have or may not have from the start and perhaps ever (see Section 7.3).

6.5

Identity criteria

If we are to grant an ontological status to artefacts, a delicate point now needs to be addressed. We need to examine their identity criteria. We have seen that artefacts are distinct from the physical objects (or amounts of matter, in the case of artefactual matter) that constitute them. They should therefore have distinct identity criteria. Indeed, artefacts can be repaired and have some parts substituted, thus changing the entity that constitutes them for another without losing their identity. Such change comes at the cost of the former constituting entity disappearing simultaneously with the newer constituting entity coming into exis22 This dependence will not, however, be formally expressed here, as we deliberately refrained from introducing intentions.

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tence, though maintaining a certain degree of spatio-temporal continuity between the two. In fact, no artefact can “jump” from one material entity to a separate preexisting one at will. If Theseus’s ship [Rea, 1997, Introduction], an artefact, does not disappear when a plank is substituted, the physical object that constitutes it, the planks-and-nails assembly, changes so that the former assembly ceases to exist and a new assembly comes into existence.23 By pointing out the property that an artefact cannot jump from one physical object to another, we can shed some light on the important distinction between artefacts and artefact roles. Roles, in general, can be played by different entities (e.g. different persons at different times can play the role of president of the US) [Masolo et al., 2004] and the change between players can be seen as a “jump”, as the previous player usually survives the change and the successor often already exists. Physical artefacts are more stable. They are not roles. This distinction is evident, for instance, in the house/home contrast. A house is an artefact which can play the role of being someone’s home. One’s home changes, there is a jump from a house to another when one moves house, so “home” is not a type of artefact subsumed by “house”, but rather a role. The gradual change in the constituting material entity may only occur with artefacts selected from physical objects and not with those selected from amounts of matter. It is reasonable to assume that amounts of plastic or of glass cannot switch over just as quantities of matter cannot interchange. Indeed, amounts of matter in dolce have purely mereological identity criteria.24 Non-agentive physical objects have more complex identity criteria, which vary from sortal to sortal. It is not the purpose of this paper to establish those criteria, but as a general guideline, we will take shape and internal structure to be part of these criteria. We assume though that minor changes in shape and in the constituting amount of matter, like those induced by a scratch, are allowed. Granularity is certainly an issue here. With artefacts, an obvious characteristic for determining their identity criteria is their intentional aspects, that is, their attributed capacity. The identity criteria should among other things determine when an artefact disappears all together. Ordinary malfunctioning does not make an artefact disappear, so its identity criteria cannot be simply based on a match between attributed capacity and capacity. Nor is the artefact’s disappearance simply based on its constituting entity’s disappearance, since that can be substituted, as we have just seen. So, the loss of much of the attributed capacity must be involved. We do not intend to solve here the infamous ship-of-Theseus puzzle [Rea, 1997, Introduction], but we believe that we can nevertheless safely assume that the identity criteria of artefacts are based on a combination of significant degree of spatio-temporal continuity of the constituting entities, the existence of all specific essential parts if any (e.g. for a car, its frame), 23 The

term “assembly” denotes here an aggregate in a specific arrangement. does not take into account the nature of the substance of which the amount of matter is made. As a result, it does not consider homogeneity conditions. A different choice would not not affect the present discussion. 24 dolce


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and the actuality of a significant amount of attributed capacity, i.e. a significant overlap between one region member of the quale of the attributed capacity and the region quale of the capacity. Note that since the attributed capacity is not restricted to the overall or main function of the artefact and since it covers structural specifications, a malfunctioning artefact does possess most of its attributed capacity. Even a badly designed artefact, like a medieval flying machine, possesses most of its attributed capacity.

6.6

Axiomatics

We now turn to the formal theory that corresponds to the above choices. A physical artefact or artefact for short is an element of Physical Artefact category. It is dependent on a constituting entity of category Amount of Matter (M ) ∪ Non-agentive Physical Object (NAPO), called for short “material entity” in the remainder. For instance, an amount of glass is an artefact constituted by an entity of M category, while a paperweight is an artefact constituted by an entity of NAPO category. An artefact x is created by an intentional association of a material entity y and a quality q which is of the type AttributedCap, a new primitive predicate denoting attributed capacities.25 The intentional association that generates the artefact is a special event of type CreationEv (creation event). To characterize this we use the IntentionalSel (intentional selection) primitive relation which takes as its arguments an event (EV ) e, an agent p, physical (APO) or not (ASO, e.g. a company), a physical artefact (PhysArt) x, a material entity y, and a quality (Q) q. IntentionalSel(e, p, x, y, q) should be read as “e is the event of p obtaining the artefact x by intentionally selecting y and attributing to it capacity q.” Our first axiom states that artefacts, that is, the elements of the category dubbed Physical Artefact and represented by the primitive predicate PhysArt, are the product of some intentional selection event: (A1) PhysArt(x) ↔ ∃e, p, y, q IntentionalSel(e, p, x, y, q). Next we constrain the primitive IntentionalSel as indicated above: (A2) IntentionalSel(e, p, x, y, q) → EV (e) ∧ (APO(p) ∨ ASO(p)) ∧ PhysArt(x) ∧ (M (y)∨NAPO(y)) ∧ AttributedCap(q) ∧ qt(q, x) ∧ ∃t (ql T (t, e)∧ PC (y, e, t) ∧ PC (x, e, t) ∧ PC (p, e, t) ∧ K (y, x, t))). Axiom (A2), in addition to restricting the arguments of IntentionalSel, specifies a number of assumptions. The quality q is a quality (qt) of the artefact x. The agent, the artefact and the material entity all participate (PC ) in the selection event for the time of the event. One consequence is that these three entities are present (PRE ), that is, exist, during the event. For non-instantaneous events, 25 The new —primitive or defined— predicates introduced to characterize artefacts are given in sans-serif font to distinguish them from the predicates denoting dolce categories and relations.

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in other words accomplishments (ACC ) as opposed to achievements (ACH ), it is nevertheless somehow arbitrary to say that the artefact x exists during the event; one could assume it exists only immediately after the event instead. This decision is not crucial to our approach. Lastly, the artefact x is constituted (K ) by the material entity y during the selection event. Axiom (A5) below will guarantee that this constitution relation lasts until the artefact disappears or until its constituting entity is replaced. The constituting entity y does not need to exist before e, since e could co-occur with a physical creation event, in which case both x and y would be created simultaneously. As far as the existence persistence of the artefact after the creation event is concerned, this can be at best a default rule because nothing prevents its destruction. We assume though that while the original constituting entity is present, the artefact will also be present. This partially underlines the fact that for an artefact to disappear it needs to undergo a major change which cannot happen without altering the identity of the constituting entity. So, as long as the original constituting entity is present, the artefact will also be present. The opposite is not true, though, because the artefact can change its constituting entity as in the case of repairing by substituting a component. While the artefact exists, there is a unique entity of the same category of the material entity originally selected, Amount of Matter or Non-agentive Physical Object, that constitutes it.26 As explained above, if the selected material entity is an amount of matter, this cannot change: (A3) (IntentionalSel(e, p, x, y, q) ∧ M (y) ∧ PRE (x, t)) → (K(y, x, t) ∧ ∀z(¬z = y → ¬K(z, x, t))). (A4) (IntentionalSel(e, p, x, y, q) ∧ NAPO(y) ∧ PRE (x, t)) → ∃!z (K(z, x, t) ∧ NAPO(z)). While the constituting material entity exists (which may or may not be the original entity), it continually constitutes the artefact and so, as a consequence, the artefact still exists: (A5) (PhysArt(x) ∧ K(y, x, t) ∧ (M (y) → ∃e, p, q IntentionalSel(e, p, x, y, q)) ∧ PRE (y, t ) ∧ t < t ) → K(y, x, t ).27 A consequence of the previous axioms is that if the artefact is constituted by different physical objects at different times, these physical objects will not exist simultaneously (thus ruling out “jumps”): 26 It is impossible to simply assert that the entity that constitutes an artefact is unique since an artefact constituted by a physical object is also constituted by the amount of matter that constitutes it. All physical objects are constituted by some amount of matter, and constitution is taken to be transitive in dolce. dolce assumes that Amount of Matter is the lowest substrate, in other words that nothing constitutes an amount of matter. 27 This very partial account of continuity ignores the case of assemblies having an intermittent existence, as in the Theseus’s ship puzzle, in which the original constituting physical object is reassembled. We leave this issue for further developments.


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(PhysArt(x) ∧ K(y, x, t) ∧ NAPO(y) ∧ K(y , x, t ) ∧ NAPO(y ) ∧ ¬y = y ) → (¬PRE (y , t) ∧ ¬PRE (y, t )). Since a material entity may constitute several different artefacts, the formula (IntentionalSel(e, p, x, y, q) ∧ IntentionalSel(e , p , x , y, q )) → x = x is neither a theorem nor an axiom. Further constraints are needed. First, for a given artefact, the attributed capacity is unique and the quale of the attributed capacity does not change in time: (A6) (IntentionalSel(e, p, x, y, q) ∧ IntentionalSel(e , p , x, y , q )) → q = q . (A7) (IntentionalSel(e, p, x, y, q) ∧ ql(v, q, t) ∧ ql(v , q, t )) → v = v . As asserted above, we assume that the same artefact can be selected several times, by possibly different agents (or societies) like, for example, the same “tree trunk bench” in the woods, so the event and the selector are not necessarily unique. This choice is not essential to the approach. However, for a given intentional selection event, the artefact and the selector must be unique (as well as the attributed capacity quality because of the axiom above): (A8) (IntentionalSel(e, p, x, y, q) ∧ IntentionalSel(e, p , x , y , q )) → (x = x ∧ p = p ). We also make sure that attributed capacities are only qualities of artefacts:28 (A9) (AttributedCap(q) ∧ qt(q, x)) → PhysArt(x). On the other hand, all physical endurants, including artefacts, have a capacity (A10). The capacity of physical endurants is inherited through constitution, in the sense that the quale of the capacity of the constituted entity includes that of the constituting entity (A11). Note that the two qualia need not be identical. The capacity of a physical object may include shape-based dispositions, while the capacity of the amount of matter constituting it cannot. Similarly, when creating a socially relevant artefact, like a cheque, new capacities are created that the constituent itself, i.e. the rectangular piece of paper, does not possess. (A10) PED(x) → ∃q (Capacity(q) ∧ qt(q, x)). (A11) (PhysArt(x) ∧ K(y, x, t) ∧ Capacity(q) ∧ qt(q, x) ∧ ql(v, q, t) ∧ Capacity(q ) ∧ qt(q , y) ∧ ql(v , q , t)) → P (v , v). To ensure that capacities and attributed capacities map to qualia in the same space of capacities, we use a unary predicate CR, for capacity region, to characterize those qualia. However, we need to allow the attributed capacity of an artefact to have a set — or some sort of collection — of such regions for quale, as mentioned above. Sets, collections and aggregates are not yet formalized in dolce. We will 28 In

dolce a quality inheres in a unique entity so given q there is a unique x such that qt(q, x).

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nevertheless use a fairly intuitive primitive predicate IN to denote “membership” (its characterization does not concern us here): (A12) (Capacity(q) ∧ ql(v, q, t)) → CR(v). (A13) (AttributedCap(q) ∧ ql(v, q, t) ∧ IN(x, v)) → CR(x). Finally, an intentional selection is a creation event unless the same artefact has already been selected. CreationEv is a defined predicate: (D1) CreationEv(e, x) =def ∃p, y, q IntentionalSel(e, p, x, y, q) ∧ ∃t (qtT (t, e) ∧ ∀t (t < t → ¬PRE (x, t ))). We ensure that there is a creation event for each artefact and, as a consequence, that it does not already exist before the first intentional selection event: (A14) PhysArt(x) → ∃e CreationEv(e, x). The creator of an artefact is the first selector; it is unique as long as there are no simultaneous first intentional selections of the same artefact: (D2) Creator(p, x) =def ∃e, y, q(CreationEv(e, x) ∧ IntentionalSel(e, p, x, y, q)). No additional axiom is introduced to model the conditions in which a given artefact disappears, essentially through lack of means to adequately model the difficult vagueness issues involved in the identity criteria of artefacts as described above. 7

BEYOND THE BASICS

7.1 Artefact types The approach developed so far allows us to characterize a notion of artefact type within dolce. Since agents and societies develop concepts to discriminate between types of entities, it is natural to view concepts about artefacts as providing definitions of artefact types. Artefacts are endurants with a particular quality known as attributed capacity. It suffices for the concept to discriminate between the attributed capacities of the artefacts to coherently collect artefacts “of the same type”. We thus assume that the definition of a concept classifying artefacts, i.e. an artefact type, is based on the comparison of the qualia of these attributed capacities with the attributed capacity of what we would call a prototype. So, we suppose that the definition of an artefact type, say Hammer, isolates the collection of the artefacts whose attributed capacities are such that all the regions in their quale include one of those of a generic or prototypical hammer. The prototypical hammer does not need to exist, but there must be a specific attributed capacity value v, i.e. a set of capacity regions, that characterizes what would count as a prototypical hammer. Evidently, the existence of an artefact type, a concept, is independent of the creation of any artefact token of this type.


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Formally, we use the classification (CF ) relation. In Section 4.2 we asserted that CF (x, y, t) stands for “at t, x satisfies concept y”, which we sometimes write “x is classified by y at t”. (D3) ArtefactType(a) =def CN (a) ∧ ∃v (∃u IN(u, v) ∧ ∀ w(IN(w, v) → CR(w)) ∧ ∀x, t (CF (x, a, t) ↔ ∃qx , vx (PhysArt(x) ∧ AttributedCap(qx ) ∧ qt(qx , x) ∧ ql (vx , qx , t) ∧ PRE (x, t) ∧ ∀w(IN(w, vx ) → ∃w (IN(w , v) ∧ P (w , w)))))). This definition states that all and only the artefacts classified by concept a have an attributed capacity’s quale whose member regions all include a region of the (non-empty) set of regions v characterizing a. Note that the artefacts of a given type do not need to be present at the same time, since, as we have seen, the quale of an attributed capacity cannot change in terms of time. For the same reason, we obtain a theorem which claims that an artefact is classified by the same type or types throughout its existence: ∀x, a, t, t ((ArtefactType(a) ∧ CF (x, a, t) ∧ PRE (x, t )) → CF (x, a, t )). Clearly, using mereological relations between the elements of the sets of capacity regions used to characterize artefact types generates a network of types so that we can relate subtypes to types, e.g. Carpentry hammer or Physician’s hammer to Hammer. As for all other concepts, artefact types are dependent on the agents or societies that define them [Masolo et al., 2004]. In fact, artefact types often apply to social artefacts, a subject to which we can now turn.

7.2

Social artefacts

Informally, a social artefact is an artefact whose type is recognizable by the members of a certain society. A once-used object such as the bench-and-table that you selected from a fallen tree for your last picnic in the woods is not what we commonly call an artefact, nor is it a stable artefact repeatedly but privately used, such as the spice-grinder that was selected from the physical object constituting a coffee-grinder [Scheele, 2005]. Often, artefacts are recognized as such by agents other than their creators: we buy knives assuming that someone has made them suitable for cutting when used in a certain way. Societies share the knowledge of recognizing many different artefacts, that is of recognizing part of the attributed capacity of a given entity through its type: pens and knives, glass and flour, and so on so that most of the time there is no need for the creator to explain their purpose. As Dipert puts it, a proper artefact is in effect an entity for which the attributed capacity (Dipert calls it the creator’s intention) is recognizable [Dipert, 1993]. To emphasize their dependence on a given society we call these items social artefacts. We depart somewhat from Dipert’s proposal by requiring that only the part of the quale of the attributed capacity defining an artefact type, in other words, the attributed capacity value characterizing a prototype, be recognized.

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This makes it possible that certain non-central or idiosyncratic aspects of the attributed capacity to be ignored, avoiding the assumption that members of society have unrealistic mind-reading abilities. A recognition event has to be distinguished from an intentional selection, as the intention (recognized in the event) is attributed to someone else, even if that someone is unknown. Of course, one may assume that someone attributed a capacity to this entity thereby believing that one recognizes an artefact, but one might just be wrong. This is an epistemological issue and as such it is not a subject for ontological analysis. Our aim is to account for the ontological import of the fact that a given society shares knowledge about some kinds of artefacts. As mentioned above, the formal ontology of social reality does not seek to analyze how and on what grounds an agent of society is able to fulfil the recognition of a given artefact. It has to deal with the fact that some artefacts acquire a social status. Note, though, that the existence of the artefact is not affected by having or not having the property of being recognizable: social artefacts are essentially artefacts. So general consensus among a community of archaeologists on the artefactual nature of a collection of entities that are actually only residues makes them neither simple nor social artefacts.29 The recognition of a social artefact relies on at least one of two distinct elements: the object structure and the context. In the first case, the act of recognition is intrinsically related to the material entity constituting the artefact — its structure, its physical qualities, its actual capacity, etc. — and to the structure and properties of artefacts previously encountered. The recognition of knives and cars falls into this category. In the second case, recognition is based on the broader context in which the entity is observed. For instance, if we see a pebble (of a certain size) on someone’s desk on top of a heap of papers, we will assume that the pebble constitutes a paperweight, while generally we do not identify paperweights on the seashore (though we may intentionally select some). Similarly, if we see in a shop a pile of pebbles labelled “paperweight”, we all assume the shop is actually selling paperweights. There are obvious limits to this: a label “paperweight” on a heap of sand will not be sufficient to make us recognize there artefacts because certain constraints on the capacity of the material entity need to be satisfied to convince us that someone did select that material entity and attributed it a certain capacity. We thus distinguish between (intrinsic) social artefacts and contextual social artefacts. To ontologically represent such notions, we shall introduce the new primitive predicate Recognizable(a, x, s, t), that reads as “the type a of artefact x is recognizable by society s at time t”. The basic constraints on this relation are: (A15) Recognizable(a, x, s, t) → (ArtefactType(a) ∧ PhysArt(x) ∧ CF (x, a, t) ∧ SC (s) ∧ PRE (s, t)).

29 The gap between ontology and epistemology is a particularly difficult one to bridge when there is societal discontinuity with knowledge loss, viz. the “Nineveh lens” cf. http://www.badarchaeology.net/data/ooparts/nineveh.php.


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To constrain Recognizable further is not an easy matter if one wants to avoid referring explicitly to the mental attitudes of agents. To gain a better grasp of what this predicate is supposed to mean, we can sketch what could be a definition, if we were using a theory that allows for doxastic modalities (the modal belief operator [Hintikka, 1964] Belx,t φ standing for “x believes proposition φ at t”), as well as the arguably simpler primitives Member (between an agent (APO) and a society (SC ) at some time) and Perceives (describing the event of an agent (APO) perceiving a physical endurant (PED)). The type a of artefact x is recognizable by society s at time t if the artefact and the society are present at t and any member30 of the society s believes the artefact x is an artefact of type a whenever during t the agent perceives31 it: Recognizable(a, x, s, t) =def ArtefactType(a) ∧ PhysArt(x) ∧ CF (x, a, t) ∧ SC (s) ∧ PRE (s, t) ∧ ∀e, y, t ((Member(y, s, t ) ∧ Perceives(e, y, x) ∧ ql T (t , e) ∧ P (t , t)) → Bely,t (PhysArt(x) ∧ CF (x, a, t ))). A(n) (intrinsic) social artefact for society s is then an artefact whose type is recognizable by society s at all times when the artefact and the society are present: (D4) SocialArt(x, s) =def PhysArt(x) ∧ ∃a ∀t ((PRE (x, t) ∧ PRE (s, t)) → Recognizable(a, x, s, t)). Let us now turn to contextual social artefacts. For the sake of presentation, contexts are taken here as a category of entities32 and we introduce a new predicate InContext to relate an endurant (ED) to a context at a time, assuming that InContext(x, c, t) entails PRE (x, t). With these tools, we can define a contextual social artefact (ContextualSocialArt) for context c and society s to be an artefact whose type is recognizable by s whenever the artefact is in the context c: (D5) ContextualSocialArt(x, s, c) =def PhysArt(x) ∧ ∃a ∀t ((InContext(x, c, t) ∧ PRE (s, t)) → Recognizable(a, x, s, t)).

7.3

Malfunction

As mentioned above, our notion of artefact includes malfunctioning or even failed artefacts. With our approach, it is rather straightforward to state that an artefact is malfunctioning at t. It simply does not possess all the capacities attributed to it: 30 There is surely a need here to restrict the conditional antecedent to qualified members of the society and to thus disregard babies, drunk people and so on. 31 The nature of the perceptive events involved (seeing, hearing, touching, etc.) may depend on both the artefact and the society; we can safely assume that perceiving an artefact is equivalent to perceiving the material entity that constitutes it. 32 The real nature of contexts is by no means obvious and the very issue of their reification raises some criticism. Contexts are not currently included in dolce and we could manage without them by using “descriptions” as introduced in [Masolo et al., 2004]. However, as it would be impossible to introduce here the notion of description, we shall rely for the present purposes on the intuitive notion of context.

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(A16) MalFunctioningArt(x, t) =def PhysArt(x) ∧ PRE (x, t) ∧ ∀q, q , v, v , w ((AttributedCap(q) ∧ qt(q, x) ∧ Capacity(q ) ∧ qt(q , x) ∧ ql (v, q, t) ∧ ql (v , q , t) ∧ IN(w, v)) → ¬P (w, v )). This definition is based on the fact that “possessing the attributed capacities” means that at least one of the alternative regions in the quale of the artefact’s attributed capacity is part of the region corresponding to the current quale of the artefact’s capacity. Note that we talk of the capacity of the artefact itself and not of the capacity of its constituent. As posited above in (A11), the capacity of the constituting entity is inherited by the artefact.

7.4 Limitations of the framework We conclude this section by highlighting a couple of open-ended problems that we think should be addressed if we are to understand the advantages and the limitations of this framework. First, our formalization is not compatible (as it stands here) with the intuition that an artefact may gain or lose attributed capacities (more precisely, that the regions in the quale of its attributed capacity might grow or shrink) while it remains the same artefact. We have instead exploited the multiplicative approach of the dolce ontology, assuming that the same material entity can simultaneously constitute different artefacts, e.g. the paperweight and the pestle (both private or contextual social), the (intrinsic social) coffee grinder and the (private) spice grinder, or the (intrinsic social) anvil and the (contextual social) doorstop. Still, it might be possible to adopt a notion of attributed capacity that depends on time and so render, even in this approach, the intuition of artefact evolution. We have not studied that option here. Similarly, concepts like artefact types may evolve (a nice example relates to the evolution of Aspirin from painkiller to painkiller and blood-thinner as detailed in [Houkes and Meijers, 2006]). The evolution of concepts has not been fully addressed in the theory developed in [Masolo et al., 2004] which is exploited here. It is implicitly assumed, however, that we need to distinguish each change in concept as a creation of a new concept historically dependent on the previous one. This appears to be a reasonable solution here too. We have just noticed that the theory developed makes extensive use of the multiplicative approach of dolce, the formal ontology chosen here. As explained on p.281, this feature is rather a specificity of dolce though it is not incompatible with other foundational ontologies. On the other hand, the multiplicative approach developed here is related to the “constitution view” developed by Baker [Baker, 2004] which has been criticized by Houkes and Meijers in [Houkes and Meijers, 2006]. Let us then examine the reasons for rejecting such an approach. Houkes and Meijers’s first criticism of the constitution view is that this approach leads to unnecessary “ontological stacking”. We note that the multiplicative approach has many other applications, giving for instance a straightforward answer to the puzzle of multiple event descriptions which have different causal explanation


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power [Pianesi and Varzi, 2000]. The multiplicative approach does not convince all philosophers, though. There are arguments in favour of and against the reductionist and multiplicative points of view, in all domains. We believe that the artefact domain is not essentially different in this respect. Their second criticism concerns the impossibility of Baker’s proposal to account for the Realizability Constraints (RC), the idea that an ontology of artefacts must provide grounds for constraining the possible entities that may constitute an artefact — given its function — as well as providing grounds to constrain the artefacts an entity could constitute, given its structure. In our opinion, the multiplicative approach does not in itself provide any explanation for this, nor does it prevent a further formal account of RC. Constitution is a generic dependence relation which constrains the existence of the related entity, but that does not explain why such a constitution may or may not hold. As mentioned above, although we have left the designing process out of the picture, we believe that RC could be accounted for by comparing the qualia of the actual and attributed capacities of an artefact, something which is facilitated by the use of a single capacity space. This is not at all straightforward, though. As argued above, the conditions of existence of an artefact are indeed based on a match between its actual and its attributed capacities but such a match is of necessity vague to allow for the existence of malfunctioning artefacts. And vagueness is a notoriously difficult issue, especially when ontological matters are at stake. Finally, we point out once more that our notion of artefact relies on one important quality space, namely the capacity space. This space has not been analyzed here and it is not yet well understood. It includes both functional and structural aspects in an interesting setting that certainly does deserve more attention. Furthermore, it seems necessary to study the dimensions of this space, the relationship to the other quality spaces such as weight, shape and colour, and the overall structure if we are to properly formalize other crucial notions such as that of technical artefact. 8

CONCLUSION

In this chapter we analyzed ontology research on the notion of artefact. After looking at existing ontologies and highlighting several shortcomings, we presented and discussed a new formalization that defines artefacts to be endurants with a special quality known as attributed capacity, which justifies their special status with respect to other endurants. This new quality allowed us to formalize a series of notions which were justified on the basis of philosophical distinctions as well as commonsense intuitions. The theory proposed, although not self-contained and still requiring further development, shows the feasibility of extending a foundational ontology, namely dolce, to grasp the non-trivial notion of artefact. The theory of course reflects certain philosophical choices which will not be palatable for all researchers in the field. Similarly, some of its technical aspects strongly rely on the multiplicative

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THE NATURE OF TECHNOLOGICAL KNOWLEDGE Wybo Houkes

1 FROM APPLIED SCIENCE TO EPISTEMIC EMANCIPATION Two decades ago, John Staudenmaier took stock of twenty-five years of research published in Technology & Culture, a leading journal for historians of technology. He identified three key debates, one of which is the relation between science and technology. This debate was largely shaped by the “technology-is-applied-science” thesis, often attributed to Mario Bunge [1966], and the objections to this thesis. Staudenmeier ends his overview of the debate with an intriguing conjecture, worth quoting in full: Thus, it would appear that a substantial number of [historians who published in Technology & Culture] interpret technological praxis as a form of knowledge rather than as an application of knowledge. By their discussions of scientific concepts, problematic data, engineering theory, and technical skill, the authors have begun to develop a complex and provocative model. If these discussions are, in fact, the beginning of a new theme in [Technology & Culture], we may find that the more limited science-technology question will take its place as a subtheme within the more inclusive model. [Staudenmeier, 1986, p. 120] Twenty years later, the antecedent of the last statement has proved false. Staudenmeier’s conjecture about the start of a new theme, around say 1980, can be supplemented with a statement about the development of this theme after 1986: neither in Technology & Culture, nor elsewhere has this “more inclusive model” been developed. What is worse, after the publication of Walter Vincenti’s What Engineers Know and How They Know It [1990], research concerning the nature of technological knowledge seems to have come to a standstill. Historians of technology have lost interest in the topic. One illustration is Samuel Florman’s [1992] review of Vincenti’s book in Technology & Culture. In the review, Florman complains about Vincenti’s excessive interest in epistemological details at the price of attention to people and organizational issues. Philosophers have not rushed in to fill the gap left by historians. Technological knowledge is Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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not even a minor theme in journals that cover epistemological and methodological issues. The handful of papers that have been published on the topic reverse Staudenmaier’s prediction: they typically address the science-technology relation and treat the nature of technological knowledge as a side issue. Furthermore, all papers are isolated efforts, and often repeat points that have made in the technology-asapplied-science debate before the 1980s. There is no philosophical research tradition regarding technological knowledge, in which authors build upon, or even respond to each other’s work. Even individuals have seldom published more than a few papers on the topic, meaning that there are not even personal research traditions. It cannot be concluded from twenty years of disinterest in technological knowledge that the subject is not interesting. It may, perhaps temporarily, have gone out of fashion among historians and philosophers. The situation does, however, pose an interesting challenge for an overview of the work on technological knowledge. Therefore, in this paper, I review several different, occasionally quite slender bodies of literature to find out whether there are possibilities to revive the interest in technological knowledge. In particular, I consider to what extent the scattered, divergent research on technological knowledge — organized into several themes in this overview — supports a strong, and therefore interesting epistemological claim made at the start of the Staudenmaier quote: that technological praxis may be interpreted as a form of knowledge. This claim is not unique to Staudenmaier. Take, for instance, George Wise’s summary of historical findings as: Treating science and technology as separate spheres of knowledge, both man-made, appears to fit the historical record better than treating science as revealed knowledge and technology as a collection of artifacts once constructed by trial and error but now constructed by applying science. [Wise, 1985, p. 244; emphasis added] Vincenti approvingly quotes Wise and several other researchers, including Barnes and Layton, as concluding that “technology appears, not as derivative from science, but as an autonomous body of knowledge, identifiably different from the scientific knowledge with which it interacts.” [1990, pp.1-2; emphasis added]. Layton in turn seems to derive this view from the work of Alexandre Koyré, writing that [Koyré] held that technology constituted a system of thought essentially different from that of science. Technology generated its own independent rules which came ultimately to constitute a body of technological theory. [Layton, 1974, p. 40] These quotes show two things that are useful for an overview. Firstly, they express an aim that shapes several existing studies of technological knowledge. This aim may be called the epistemic emancipation of technology, i.e., to establish that technology is epistemically distinct from science. This emancipation aim makes sense against the background of the technology-as-applied-science debate in the 1960s and 1970s. In the last half of the 1980s, denying that technology merely

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involves the application of scientific knowledge was no longer in need of further argumentation. Thus, a next step could be considered: that technology involves its own form of knowledge. However — this is the second useful aspect of the quotes — the epistemicemancipation claim can be interpreted in various ways. In one sense, which I call “weak emancipation”, it says that scientific and technological practice result in bodies of knowledge that are as distinct as our knowledge of plants and animals, or perhaps more strongly, as distinct as physics and chemistry. “Distinct” here means no more than that there is, as yet, no way of incorporating one body of knowledge into the other. One might think that this is a defensible, and sufficiently emancipatory claim about the relation between scientific and technological knowledge. Yet many authors might also be interpreted as making a stronger claim. Calling our knowledge of plants and animals “separate spheres”, “autonomous bodies”, or “of different forms” sounds exaggerated: irreducibility does not entail separation, let alone autonomy. If autonomy is taken in its standard sense of “self-government”, or the ability to set one’s own rules, it leads to a far stronger emancipatory claim than the minimal one considered above. Technological praxis results in an autonomous body of knowledge if this knowledge answers to its own epistemic rules, not those of science. Physics and chemistry are not mutually autonomous in this sense, since they answer to approximately the same rules; justifying a claim in physics is not qualitatively different from justifying a claim in chemistry, although specific methods may of course differ. Thus, calling technological knowledge “autonomous” more strongly emancipates it from scientific knowledge. Given the applied-science debate, this strong emancipation seems attractive. Having denied the thesis that technological praxis is epistemically dependent on science, one might be eager to prove that technology is epistemologically self-supporting, and not necessarily related to science. Reviewing the existing literature on technological knowledge shows that the strong autonomy thesis plays an important role in it. But this does not mean that authors have tried to validate the thesis in exactly the same way, nor that they have successfully established it. In this chapter, I give a critical overview of the literature by distinguishing four emancipation strategies that have been developed — without claiming that every author pursues only a single strategy, or that authors have appreciated the differences between the various strategies. These strategies are: • to contrast directly scientific and technological knowledge (Section 2). • to construct a taxonomy of technological knowledge (Section 4). • to appeal to the “tacit” nature of technological knowledge (Section 6). • to appeal to the prescriptive nature of technological knowledge (Section 8).

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After showing how the strategy has been endorsed, expressed and developed, I critically analyze its current success in establishing strong emancipationism.1 Furthermore, most review sections are succeeded by a section that contains a more general argument against the effectiveness of the strategy. To anticipate my conclusion: although the literature on technological knowledge is significantly shaped by the strong-emancipation ideal, efforts to realize it have not only been scattered and idiosyncratic, but also significantly underestimate the difficulties in establishing the ideal. My arguments do not show that strong emancipation is impossible to defend, but they do show that current arguments are ineffective. The critical review is complemented by a short look at one reason why historians and philosophers of technology might have abandoned Staudenmaier’s epistemic theme and the emancipation quest (Section 9). Still, I end the chapter on a more constructive note. In the course of my critical analysis I identify less ambitious and more detailed issues that might be addressed to improve our understanding of technological knowledge. I conclude with offering some suggestions for reviving the study into the nature of technological knowledge (Section 10).

2

CONTRASTING NATURAL AND ENGINEERING SCIENCE

A popular strategy for studying technological knowledge and arguing for epistemic emancipation is to contrast science and technology — more specifically: to look at differences between natural and engineering science. The latter is certainly not equivalent to technology, but I shall show that the narrowing of scope is understandable. Here, I review existing developments of the contrastive strategy. A first thing to note is that most authors who develop this strategy also share a key intuition. This intuition — which is not exclusive to the contrastive strategy — is that technology is, in all its aspects, aimed at practical usefulness. Thus, whether technological knowledge concerns artefacts, processes or other items, whether it is produced by engineers, less socially distinguished designers, or by consumers, the prima facie reason to call such knowledge ‘technological’ lies in its relation to human goals and actions. And just as scientific knowledge is aimed at, or more tenuously related to, the truth, so technological knowledge is shaped by its relation to practical usefulness. This ‘truth vs. usefulness’ intuition — TU-intuition for short — is repeated, in slightly different wordings, in many works, especially those in which a rough-andready characterisation of technology or technological knowledge is sought. Take, for instance: 1 Neither the weak nor the strong emancipation ideal is made explicit in the literature on technological knowledge. Some of the work reviewed in this essay might be interpreted as arguing for weak, rather than strong emancipation. Given my critical analysis, this interpretation might be more charitable. It is also less interesting, since establishing weak emancipation is a rather trivial aim. Thus, I have taken the liberty of reviewing/reconstructing the literature with regard to its effectiveness in achieving a more difficult, perhaps even unattainable, goal.


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Technology. . . aims to be effective rather than true [Jarvie, 1972, p. 55] Science seeks basic understanding (. . . ). Technology seeks means for making and doing things. [Hindle, 1966, p. 4] Science concerns itself with what is, technology with what is to be. [Skolimowski, 1972, p. 44] The TU-intuition also shapes Walter Vincenti’s work. From virtually all his individual case studies, Vincenti draws the conclusion that technological knowledge is distinct from science because it is related to practical purposes. One telling quote is the following:2 [T]he criterion for retaining a variation in engineering must be, in the end, Does it help in designing something that works in solution of some practical problem? The criterion for scientific knowledge, however we put it, must certainly be different . . . Borrowing a phrase used by Alexander Keller . . . I would venture it more or less as follows: Does it help in understanding ‘some peculiar features of the universe’ ? [Vincenti, 1990, p. 254]; (original emphasis) Few authors go beyond expressing the TU-intuition and arguing why it shows that technology involves more than applying scientific knowledge. This is unfortunate, because the intuition alone does not at all establish epistemic emancipation of any variety. For the intuition does not only present a questionable image of science (an objection that shall be considered in Section 3), but it is also unclear on the technology side: does it address engineering practice, engineering science, engineering design and/or technology in one of the possible senses of the term? These meanings can be distinguished more or less clearly (see e.g., [Mitcham, 1978]), and it is often useful to do so. However, a focus on epistemological issues might make the distinctions less relevant. The reason is that not all technological practices are, on the face of it, equally relevant to technological knowledge. Engineering practice, design, and also the use of technical artefacts typically involve knowledge, and might often lead to acquiring knowledge, but they are not primarily knowledge-producing activities. Engineering science is. Therefore, it is a natural starting point for inquiries into the nature of technological knowledge.3 Those who go beyond expressing the TU-intuition frequently focus on the engineering sciences and the role of theories and models in these disciplines. The common supposition is that this role is instrumental. More than natural scientists, 2 Passages

in which Vincenti expresses the TU-intuition in slightly different, artefact-oriented words, are: “In scientific knowledge the purpose is understanding of nature; in engineering science the ultimate goal . . . is the creation of artefacts”[Vincenti, 1990, p. 135] and “Engineers use knowledge primarily to design, produce and operate artefacts, goals that can be taken to define engineering. (. . . ) Scientists, by contrast, use knowledge primarily to generate more knowledge” [ibid., p. 226]. 3 Engineering science is also a risky starting point, because of all technological practices, it is probably closest to science, and therefore least likely to be autonomous — or even in need of autonomy.

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engineering scientists are supposed to be content with theories and models that are practically useful, but known to be incorrect. This way of studying technological knowledge is not only evidently connected to the TU-intuition. It also stands a good chance of non-trivially developing it, because it generates some specific research questions — which test both the TU-intuition and the ideal of strong emancipation. I give only two examples of such questions here, in the form of specific hypotheses. Firstly, if practical usefulness is the central value of technological practice, one would expect this to affect the validation of theories and models in engineering science, given their obvious relation to technological practice. To put it roughly, one would expect theories and models in engineering science to be valued if — although perhaps not only if — they are usefulness-tracking, unlike theories and models in natural science. If this abstract difference, based on the TU-intuition, is not manifested in concrete evaluations of theories and models, it makes little sense to call technology epistemically autonomous, at least in this important respect. Secondly, engineers frequently employ theories from the natural sciences. If we suppose that these theories were previously evaluated for their verisimilitude and that engineering scientists value them for their usefulness, one would expect that changes (if any) to these scientific theories and models reflect this shift of values — and that not all such changes are valuable within the natural sciences. If no such changes are made, or if every change by engineering scientists is hailed as simultaneously advancing scientific research, the distinction between scientific and technological knowledge has no normative bite. Neither these nor other, equally specific hypotheses regarding technological knowledge have been investigated. Most authors who address epistemic differences between science and technology are content to state the TU-intuition, giving some illustrations — usually of artefacts that were developed on the basis of false theories or in the absence of theories. The validation of theories and models used in the engineering sciences is seldom studied. Still, some papers identify or even illuminate the issues raised above. I discuss four in some detail. The first three papers address the first issue, that of the validation of theories and models developed within the engineering sciences. Both Ronald Laymon [1989] and Vincent Hendricks, Arne Jakobsen and Stig Andur Pedersen [2000] relate the development of specific models to the central value of practical usefulness. Their main aim is to show that this value is at work and that engineering is therefore different from science, but they also offer material for the more valuable analysis of how the value affects the evaluation of models. Ronald Laymon examines the role of as-if theories, or fictitious models in engineering science. More specifically, he studies the history of models of a swinging pendulum, as they might be used in instrument building. Such models have to account for buoyancy effects: the textbook harmonic-oscillator idealization is of little use for practical purposes. One way to provide such an account is to correct for the mass of displaced air, and then to correct this by means of an experimentally determined correction factor — which accounts for all non-hydrostatic effects of


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the presence of air. Such empirical data raise a projectibility issue: will they apply to slightly different pendulums in slightly different circumstances? Laymon discusses various responses to this question that may be typical for engineering. One is that, in technological practice, the projectibility issue may be largely avoided by rebuilding successful devices and/or (artificially) recreating the circumstances of successful performance. This does not add significantly to the body of technological knowledge, making this response uninteresting for our current purpose. Another response is more interesting. To understand the behaviour of pendulums in new circumstances, the experimental correction factors may be analysed for continuities and correlated changes, and one may seek explanations of such correlations. The engineering scientist appears to have considerable freedom in seeking explanations: because of the ultimate goal to produce practically useful artefacts, clearly fictitious or as-if theories are just as welcome as realistic ones. Laymon mentions Airy’s theory of “adhesive air” as an example: the presence of air may be accounted for by supposing that a quantity of air adheres to the pendulum while moving — adding to its buoyancy without changing its weight. This theory can be taken seriously within engineering science if it has instrumental value. Moreover, it leaves open a more realistic explanation in terms of the viscosity of air, which can again be corrected for its “viscosity bias”. This cycle of idealized model, correction, explanation and refinement of the model is probably familiar from other experimental sciences. Yet the role that as-if theories play in the cycle presented by Laymon may be characteristic for the engineering sciences.4 And, what is more important to the topic of this paper, by means of his concrete example, Laymon gives considerably more content to the claim that engineers do not seek “true” theories, and are primarily interested in “usefulness”. Another methodological feature of engineering science, the existence of “lumpedparameter models”, is examined in some detail in [Hendricks et al., 2000]. In these models, the behaviour of a system is described by analyzing it as a complex of subsystems, for which idealized models are available. These models may not be realistic. They may even be transferred from a different domain altogether. A mechanical system may, for instance, be modelled in such a way that an isomorphism with the model of an electrical system is revealed. The point of this method of decomposition-cum-isomorphism cannot be veracity. Thus, lumping is another example of the way in which the central value of practical usefulness affects the evaluation of models: because engineering science aims at usefulness rather than truth, (more) lumped-parameter models may be acceptable. This reveals an evaluative difference between the natural and engineering sciences, albeit one that calls for more detailed analysis: as Hendricks, Jakobsen and Pedersen notice, lumping4 Laymon’s expression of this difference relies on the TU-intuition: “The problem created by the use of idealizations for science . . . is to determine whether failures to achieve experimental fit to within experimental error are due to the falsity of the theory or of idealization. (. . . ) For the engineer the problem seems altogether different. If [the closeness of predictive fit achieved by theory and idealization] is good enough for some practical purpose then the engineer’s job is done . . . ” [Laymon, 1989, p. 354].

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parameter models are also found in some parts of physics. They mention models in solid-state physics; the liquid-drop model of nuclear physics may provide another example. A closer comparative study of the roles these models play, and the conditions for accepting or rejecting them may lead to more insight in the relation between usefulness and the engineering sciences. The liquid-drop model of nuclear physics is, for example, not regarded as merely a predictive instrument: it is commonly supposed that nuclei are structurally similar to drops of liquid, and that this explains some aspects of their behaviour. Presumably, engineers do not take the successes of an “electrical” model of a mechanical system to show such a structural similarity. Whether this means that lumped-parameter models in engineering sciences are less tightly constrained, because there are no requirements of truth-likeness, remains to be seen.5 Peter Kroes [1992] takes another perspective on the development of theories in the engineering sciences. Rather than stressing the role of idealized or fictional models, he studies what he calls “engineering theories”, i.e., formally or mathematically structured, experimentally validated systems of knowledge that explain the technological function of a particular class of technical artefacts or technical-artefact-related materials in terms of their design or construction.6 Using Pambour’s theory of the steam engine as an illustration, Kroes argues that design considerations confer a “distinctly technological flavour” [1992, p. 70] on engineering theories. This flavour shows in three features. Firstly, as the characterization already makes clear, the domain of application of an engineering theory is a designable technical artefact or artefact-related material, not a physical phenomenon: Pambour’s theory is about piston-operated steam engines, not about all heat engines. Secondly, engineering theories may contain basic principles related to the design or construction of technical artefacts. These principles, such as Pambour’s principle of the conservation of steam, may be reformulated in terms of physical boundary conditions, but they involve more than an application of physical principles: design considerations, not physical considerations, explain why these conditions are relevant. Thirdly and finally, engineering theories employ technical concepts as well as physical ones. Technical concepts are again related to design characteristics. To confuse matters, some theoretical concepts may be homonyms, referring to either technical or physical characteristics. Examples of concepts with such “dual significance” [Kroes, 1992, p. 91] are “resistance” and “pressure”. 5 Hendricks and his co-authors regard engineering science as combining the values of truthfulness and usefulness: “(...) the objective for engineering science is an optimal degree of theoretical correctness (typically limited by time and resources) combined with pragmatic considerations of practical usability.” [Hendricks et al., 1999, p. 302]. This combination view seems to minimize the difference between natural and engineering science, since the former also seems to combine truthfulness and usefulness. See also section 3. 6 Kroes [1992, p. 69] grafts this characterization on Staudenmaier’s [1985, p. 107] definition of an engineering theory. He modifies it to focus on technical artefacts, and technological functions rather than behavioural characteristics; both modifications are indeed called for, since: (a) many theories in the experimental sciences describe artefacts, viz. artifically induced phenomena; (b) the behaviour of artefacts can be described in physical or chemical terms.


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Of the three papers discussed, Kroes’s is the most specific. It focuses on a clearly circumscribed subset of the total body of engineering knowledge, and identifies several distinguishing features. Moreover, it relates these features to some of the most basic concepts used to describe technology, “artefact”, “(technical) function” and “design”. As I shall argue in the next section, this gives the approach an analytic edge over that in the other two papers, which discuss more general features of engineering models, and appeal merely to the TU-intuition to distinguish the models from scientific ones. Yet Kroes’s approach also has drawbacks, partly because of its specificity. For one thing, Kroes’s approach might only emancipate a very small part of technological knowledge. Some knowledge may have a “distinctly technological flavour” without being related to a specific type of technical artefact; Vincenti’s control-volume analysis, discussed immediately below, comes to mind as an example. Secondly, the basic concepts invoked by Kroes, such as “design” and “function”, are in need of further analysis. If, for instance, “design” may refer to the selection of physical objects for practical purposes, and function to physical behaviour, the distinction between engineering theories and scientific theories may evaporate. Thirdly, Kroes’s focus on distinctions between concepts is innovative and initially plausible, but at further inspection problematic. If, for instance, “pressure” indeed has a dual significance, should Pambour’s theory be disambiguated so that it only contains design parameters? Doing so seems necessary to argue that engineering theories differ from scientific theories “in substance”, as Kroes suggests [1992, p. 93]. However, once disambiguated in this way, it is not clear how engineering theories “exploit scientific theories in solving technical problems” [Kroes, 1992, p. 92], since their content is, strictly speaking, different from that of scientific theories. The second issue, the adoption and adaptation of scientific theories within engineering science, is even more rarely addressed. It is, however, the topic of one of Vincenti’s case studies [1990, Ch.4; the original paper is from 1982]. Vincenti examines the development of control-volume analysis, a technique for solving problems regarding fluid flow by selecting a hypothetical surface and calculating the values of physical quantities on its boundaries. This technique is compatible with thermodynamics and does not add irreducible concepts to it, and it is a standard part of many engineering curricula. It is not, however, found in thermodynamics textbooks for physicists — Vincenti mentions a textbook that presented controlvolume analysis in an edition for physicists and engineering students, but omitted it in a later edition for physicists alone. The reason is that the technique is global. Control-volume analysis only yields overall results regarding the behaviour of a system; the inside of the hypothetical control volume may be regarded as a physical black box.7 Within the confines of this black-boxing, control-volume analysis is a powerful technique, which can be used to describe the behaviour of all kinds of devices that involve fluid flow — including rocket motors and pipes in installa7 A physicist might want to use such a global calculation, if she is interested in predicting fluid flow. It would, however, be remarkable if physicists would develop a systematic technique for such calculations.

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tions. For such systems, the control volume and the relevant quantities are easily determined by the context of use: “what goes in” and “what comes out” are far more relevant to the performance of artefacts than “what goes on inside”. Thus, control-volume analysis shows how engineering scientists adopt a physical theory and make it suitable for their, presumably, different purpose. From this brief review, it can be concluded that the evaluation of theories and models in engineering science points out several interesting, possibly distinctive features of technological knowledge — but that the literature does little more than point out these features, and that emancipatory arguments often appeal to the TU-intuition without explicating it. Furthermore, all efforts to examine evaluative differences between natural and engineering science have been isolated: the papers reviewed have not given rise to sustained discussion or further refinement; they do not even build upon each other.

3

THE INSTRUMENTALIST OBJECTION

The discussion above has shown that the TU-intuition is a recurrent theme in the literature on technological knowledge. As stated above, the TU-intuition understands the difference between natural science and technology (or, more narrowly, the engineering sciences) in terms of a difference in goals: the former aims at finding out true theories, whereas the latter aims at practical usefulness. In this section, I point out that merely appealing to this intuition is not sufficient to emancipate technological knowledge. For the difference in goals appears to presuppose a realist conception of science, on which scientific theories ought to be interpreted as descriptions of (the structure of) reality, and science as a continuing enterprise to construct more accurate theories. There are, of course, many ways of developing this realist view of science and scientific theories,8 and a one-line description may not be representative for all of them. Still, the broad spectrum of realist conceptions can be contrasted with another view of science: instrumentalism.9 Instrumentalists seek to decouple scientific inquiry from truth, and instead emphasize its connection to usefulness. There are several ways to achieve this. Some instrumentalists argue for a re-appraisal of the notion of truth that is relevant to scientific inquiry: instead of the traditional correspondence theory, they propose a “pragmatic” theory of truth. Other instrumentalists prefer an epistemic route to the semantic one. They accept the realist idea that scientific theories are candidates for being true in a correspondence sense, but they deny that scientists may justifiably accept or reject a theory because of its truth-likeness. Instead, they say that theory choice 8 See, for instance, Ladyman’s [2007] review of traditional and contemporary varieties of realism and instrumentalism. 9 The discussion of instrumentalism as an alternative to scientific realism does not reflect an opinion that instrumentalism is the only viable anti-realist conception of science. Rather, instrumentalism is the anti-realist conception that most directly undermines the TU-intuition.


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ought to be dictated by the usefulness of theories for solving the empirical and theoretical problems of science.10 That the instrumentalist conception of science conflicts with the TU-intuition is clear enough. If, like technology, science is concerned with usefulness instead of truth (in the correspondence sense), forging an epistemic distinction between the two activities in terms of their goals seems a questionable enterprise. More specifically, the primary epistemic virtues of science and technology would be the same, making it impossible to emancipate technology from science through distinguishing their primary epistemic virtues. One might try to overcome this obstacle by arguing directly against the instrumentalist conception of science, or at least to decrease its plausibility by attacking the arguments supporting it. Such a maneuver would lead us into the territory of the general philosophy of science, so I do not consider it here. However, its effectiveness seems doubtful. Instrumentalism is a minority position in the philosophy of science, but the arguments used to sustain it, such as the pessimistic induction and underdetermination thesis, are plausible and remain defensible despite numerous attempts to invalidate them. It would be interesting to see whether technology offers a fresh perspective on the entrenched realism debate, but it is hard to feel optimistic about the possibilities of a major breakthrough.11 Another response might be to accept the main thrust of the argument, but to remove its sting by arguing that technological knowledge is appraised, not in terms of usefulness in general, but in terms of practical usefulness. Technology concerns deliberate changes that serve more or less immediate practical purposes, like transportation and hygiene. To these purposes, engineers primarily produce (designs of) technical artefacts, including systems and processes, and they are aided in this by theories. Scientific theories may be understood as instruments, just like technical artefacts, and the construction of theories may be an instrumental activity, just like design. Yet these instruments serve “theoretical” purposes such as predicting or capturing data, rather than the “practical” purposes that shape technology. This response might go some way towards dispelling the instrumentalist objection. Yet it appears that, by accepting the gist of the objection, the goal of epistemic emancipation becomes unattainable. If science and technology are subordinate to the same primary epistemic virtue — namely usefulness — establishing strong emancipation by focussing on more specific goals seems difficult. Theories in particle physics and microbiology serve different specific purposes, e.g., to predict the behaviour of mesons and of enzymes, but since the primary epistemic virtue is the same for both types of theories, we might not want to say that they answer to their own sets of rules; instead, physical and microbiological knowledge 10 [Stanford, 2005] is a recent overview of historical, current and possible instrumentalist conceptions of science. 11 One may build upon Hacking’s [1983] suggestion that scientists treat those objects as real which they can manipulate, and to examine the role of technology in shaping this manipulability, and of engineering science in describing it.

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are typically regarded as species of one epistemic kind, namely scientific knowledge. This worry increases once an additional feature of the response is noticed. No one would want to deny that technical artefacts, such as cars and hand soap, serve immediate practical purposes. But artefacts do not constitute technological knowledge, although their design and production might be based on it. It seems that, to keep the strong-emancipation ideal alive, the TU-intuition must be explicated by focussing on the epistemic products of technology, such as theories and models in the engineering sciences. At this level, making a principled distinction in terms of specific goals is less plausible. The liquid-drop model is known to be unrealistic, but still used to predict the behaviour of nuclei. Airy’s adhesive-air theory is known to be false, but used to predict the behaviour of pendulums. If there are any epistemic differences, they remain to be discovered, below the surface. This renewed objection suggests a third response, which is to bite the bullet. For the moment, instrumentalism regarding scientific theories seems a viable position, which reduces the epistemic contrast between science and technology to the vanishing point. Therefore, philosophers of technology who seek epistemic emancipation cannot rely on a realist image of science — despite multiple attempts, this image has not been shown to be sufficiently reliable, and the naive version that appears to be presupposed in the TU-intuition certainly needs significant refinement. Still, detailed studies into the acceptance of theories and models by engineers — such as those discussed in Section 2 — may lead to additional arguments for a realist image of science, or to an instrumentalist image that retains some contrasts with technology. If it could be shown, for instance, that the contexts in which engineers accept unrealistic models are qualitatively different from the contexts in which scientists would be prepared to do so; or that engineers accept more blatantly false theories than any scientists would be prepared to do, then the apparently contrast-reducing statement that “Both scientists and engineers use theories as instruments” could be explicated into different statements about science and technology. Such a sophisticated response has, to the best of my knowledge, never been given. As indicated above, Laymon considers the need for such a response, offers material that may be helpful, but ultimately relies on the TU-intuition and a realist image of science himself. Alternatively, one could follow Kroes’s example and try to specify the instrumental role of engineering theories and models by more closely circumscribing the practical purpose, e.g., in terms of the design and construction of technical artefacts. This strategy seems promising, in the sense that it might explicate the TU-intuition in terms of several concepts that are fundamental to our descriptions of technology. However, these concepts, such as “design” and “technical artefact” are in need of further analysis. Furthermore, narrowing down the practical context of technology runs the risk of narrowing the scope of one’s analysis of technological knowledge — as pointed out in Section 2, Kroes’s analysis of engineering theories might address only a small portion of what might be called technological knowledge.


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Still, by developing arguments and analyses along the lines suggested here, philosophers of technology could examine the role of theories and models in the engineering sciences and simultaneously contribute to the philosophy of science, instead of (perhaps unconsciously) applying insufficiently sophisticated ideas from the philosophy of science. 4 TAXONOMIES OF TECHNOLOGICAL KNOWLEDGE Inventorizing the contents of technological knowledge would improve our understanding of it. This need not involve an explicit contrast with another type of knowledge, just like an inventory of bears need not involve contrasting them with wolves. Thus, the taxonomical way of analyzing technological knowledge is at least prima facie different from the contrastive analysis outlined in the previous two sections. Yet, like this analysis, making an inventory can serve the purpose of epistemic emancipation:12 if the items on this inventory are sufficiently different from those on an inventory of scientific knowledge, one may take this as evidence that they embody different types of knowledge. The traditional distinction between fields within the engineering sciences is an obvious starting-point for a classification of technological knowledge. In engineering schools and elsewhere, e.g., in library cataloguing systems, we find taxa such as mechanical engineering, chemical engineering, and bio-medical engineering. These disciplines and bodies of knowledge appear to be named after the kind of scientific knowledge that they are thought to apply. Moreover, we find taxa such as software engineering and maritime engineering, which appear to be based on the kinds of artefacts produced within the fields. Neither way of classification seems epistemically informative, and the former might even strike those interested in epistemic emancipation as misleading. It is therefore hardly surprising that attempts at classification seldom start from existing distinctions between engineering fields and sciences. They are even seldom presented as attempts at reconstructing or revising these distinctions. Rather, most classifications present categories that cut across the boundaries between fields and disciplines. Several authors have proposed taxonomies of technological knowledge. I shall give an overview of four efforts: those made by Vincenti [1990], Ropohl [1997], Faulkner [1994], and de Vries [2003].13 Not all of these authors explicitly state the purpose of epistemic emancipation.14 Nevertheless, given the context of this 12 The taxonomies may serve other purposes, for instance aiding engineers in classifying and storing their knowledge. Broens and De Vries [2003] note that engineers find Vincenti’s taxonomy most useful for this purpose — which is compatible with any conclusion regarding the usefulness of this taxonomy for emancipatory purposes. 13 My presentation in the remainder of this section has profited from Broens and De Vries [2003], but differs from it in some details and criticisms. 14 The doubts I raise (especially the general doubts presented in Section 5) might strike some as unfair criticisms of proposed taxonomies. One might reasonably doubt whether a taxonomy could even in principle be used for emancipatory purposes, i.e., to determine the (autonomous) nature of the knowledge that is classified. Still, existing work on technological knowledge often

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paper, I shall review all four in this light. Moreover, I shall assess the taxonomies with regard to their formal merits: as taxonomies, they ought to present categories that are mutually exclusive and jointly complete; every item in the domain should be classified in one and only one category. In the next section, I go on to consider the viability of the taxonomical way of emancipating technology from science. The Table (pp. 324–325) provides an overview of the categories of knowledge introduced by the four authors, along with subcategories, a one-phrase clarification, and/or some examples. Not all labels may be self-explanatory; indeed, key notions in all four taxonomies are in need of further analysis. For the moment, I postpone further clarification and comments. In the remainder of this section, I focus on three aspects of the taxonomies: their formal characteristics; their mutual differences; and the way(s) in which they bring to light the relation between technological and scientific knowledge. Let us start with the formal characteristics, i.e., exclusiveness and completeness. Here, Vincenti’s classification performs badly — as he admits before starting his presentation [1990, p. 208]. To give just one example, his scheme is partly guided by the distinction between codifiable theoretical tools and quantitative data on the one hand, and uncodified practical considerations on the other.15 However, practical considerations may be codified [1990, p. 219], without thereby turning into either tools or data. A similar observation may be made regarding Faulkner’s taxonomy, since she incorporates Vincenti’s distinction, rephrasing it as one between “practical experience” and “engineering theory”. Furthermore, she grounds her distinctions in the possible subjects of technological knowledge, whereas one element of knowledge may have multiple subjects (e.g., performance data about and specifications of material properties). The taxonomies of Ropohl and De Vries seem to fare better in this respect. Neither includes a distinction between knowledge and skills, or between variously codifiable elements of knowledge. Instead, both authors refer, in different ways, to the distinction between structure and function. At first glance, this seems sufficiently principled to support mutual exclusiveness of categories. Yet problems ensue as soon as one looks for a more detailed understanding. For one thing, the notion of artefact function is far from uncontested, as Preston’s contribution to this handbook makes clear; on some views, such as Robert Cummins’ [1975], the function of an artefact may not be distinct from structural features, such as dispositions and other physical behaviour. These views may be contested qua theories of artefact functions, but this holds the two taxonomies hostage to an unresolved philosophical debate. A second set of remarks concerns the manifest differences between the taxonomies, which roughly divide into two pairs. The systems of Vincenti and takes the form of constructing a taxonomy, and is frequently motivated by the quest for epistemic emancipation. It therefore makes sense to evaluate the taxonomical work in the light of this quest. 15 Vincenti distinguishes these practical considerations from both tools and data because they “frequently do not lend themselves to theorizing, tabulation, or programming into a computer” and “they are hard to find written down” [1990, p. 217].


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Faulkner, and those of Ropohl and De Vries, seem similar, but also show some notable differences. For the Vincenti-Faulkner pair, the similarities are easily explained, because Faulkner used Vincenti’s (earlier) work as an explicit guiding line for her own investigation into innovation. Still, she added categories (e.g., knowledge about knowledge) and subcategories (e.g., new product ideas), removed others (e.g., quantitative data), and reshuffled still others (e.g., by combining in one subcategory both operational principles and normal configurations). Matching the taxonomies of Ropohl and De Vries is harder, given their terminological differences. Ropohl’s functional rules, for instance, appear to match De Vries’ process knowledge rather than his functional-nature knowledge. Still, that both authors distinguish a ‘functional’ category makes their taxonomies more alike to each other than to any of the other two. These differences partly reflect differences in guiding principles. As is routinely noted in textbooks that deal with classification and categorizations, items can be grouped together in arbitrarily many ways. Cars, for instance, can be classified in terms of ownership (privately owned, rental, leased, etc.), fuel (gasoline, electrical, hybrid, etc.), engine type, colour, ownership history (first-hand, second-hand, third-hand, etc.) number of dents, etc. To curtail this arbitrariness, some guiding principle should be invoked. For many scientific classifications, it is required that its classes “function in, or facilitate the formation of, scientific laws”.16 This requirement is pointless in the present context, not only because the four taxonomies are reviewed for their emancipatory success, but also because some of them feature a (sub-)category of scientific theories and laws. Ropohl and De Vries instead use perspectives from the philosophy of technology: their taxonomies are guided by systems philosophy and the dual-nature thesis respectively. The other two taxonomies have no clear guiding principle: Vincenti’s taxonomy seems largely the result of personal reflection on a large number of case studies in one, designoriented discipline, namely aeronautical engineering, whereas Faulkner’s additions and adaptations to Vincenti’s system mainly stem from her studies into technological innovation. I will return to this difference in guiding principles, or lack thereof, in the next section. For the moment, I note that this underlying difference means that one resolution of the manifest differences is unavailable. If two biologists agree on the criteria for speciation, but one distinguishes five species of dog, and the other distinguishes six species, a straightforward solution is that the former has overlooked one species. This resolution is probably not available for taxonomies of technological knowledge: the four systems cannot be merged into one super-taxonomy by distinguishing every category that is listed by at least one taxonomy. For one thing, this super-taxonomy would share the formal flaws of any original taxonomy; for another, it would require some possibly arbitrary decisions. De Vries, for instance, does not distinguish competences and know-how from theoretical or propositional knowledge. Given the other systems, he might have done so in two different ways: he might have followed Ropohl’s example in listing know-how as a 16 David

Hull, “Taxonomy”, in the Routledge Encyclopaedia of Philosophy.

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Vincenti [1990]

Ropohl [1997]

Fundamental design concepts

Structural rules

• operational principles • normal configurations

on the assembly and interplay of the components of a technical system

Criteria and specifications

Technological laws

• general, qualitative goalsa • specific, quantitative goals • goal-to-specification translationsb

Theoretical tools

transformation of natural laws with regard to technical processes

Functional rules

• models and theories • intellectual concepts (e.g., ‘boundary layer’)

Quantitative data • descriptive (e.g., operational conditions, human behaviour) • prescriptive (e.g., safety factors)

Practical considerations

what to do if a certain result is to be attained under given circumstances

Technical know-how (implicit knowledge and skills)

Socio-technical understanding

• experience from production, operation, accidents • design rules of thumb

systematic knowledge about the relation between artefacts, natural environment and social practice

Design instrumentalities • structured design procedures • ways of thinking (e.g., controlvolume thinking) • judgemental skills a This b See

subcategory and the next are only implicitly distinguished by Vincenti. Marc de Vries’ contribution to this Handbook for a closer analysis of this subcategory.

Table 1.a


Faulkner [1994]

De Vries [2003]

Related to natural world

Physical-nature knowledge

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• scientific and engineering theory • material properties

Related to design practice • • • • •

Functional-nature knowledge

criteria and specifications instrumentalities fundamental design concpets competence practical experience

Related to experimental R & D • • • •

experimental and test procedures research instrumentalities research competence experimental and test data

Related to final product

Knowlege of physics-function relations

Process knowledge

• new product ideas • operating performance • production competence

Related to knowledge • location of knowledge • availability of equipment, materials, facilities or services

Table 1.b

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separate category, or he might have included the distinction in the form of subcategories. Furthermore, only Ropohl explicitly lists “socio-technical understanding” as a category; this kind of knowledge is either missing from the other taxonomies, or very covertly included. The third and final set of remarks concerns the way in which the taxonomies incorporate possible differences between scientific and technological knowledge. All taxonomies list categories or subcategories that largely, or even exclusively, appear to consist of run-of-the-mill scientific knowledge. Therefore, if the taxonomies serve the purpose of epistemic emancipation at all, they do so by incorporating scientific knowledge, rather than by contrasting an elaborately classified system of technological knowledge with a system of scientific knowledge. So, Vincenti includes models and techniques from mathematics and physics among his examples of theoretical tools; Ropohl’s category of structural rules might, and De Vries’ structural-nature knowledge definitely does, include many statements about physical or geometrical relations between artefact components; and Faulkner explicitly distinguishes scientific and engineering theory as a subcategory. This incorporative strategy seems reasonable, if “technological knowledge” is taken to be the body of knowledge used in engineering science, design and/or practice; after all, engineers routinely use scientific theories and models. Still, the strategy creates at least two problems: one of a formal nature, and the other with respect to the goal of epistemic emancipation. Formally, as soon as one of the four taxonomies (or the super-taxonomy that results from combining them) is combined with a taxonomy of scientific knowledge, a taxonomy results that does not satisfy the demand of mutual exclusivity: some (sub-)categories will feature both in the technological and in the scientific part of the encompassing taxonomy. With regard to emancipation, it makes little sense to include categories of knowledge that answer to scientific standards — the resulting body of technological knowledge will certainly not be (completely) autonomous if these standards apply to part of it. One may think to solve both problems at once by excluding from one’s taxonomy of technological knowledge all categories that feature in a taxonomy of scientific knowledge. In this way, double entries are avoided, and one may still claim that the resulting science-less body of technological knowledge answers only to its own rules. The resulting taxonomy would remain silent on the nature of those rules — making the autonomy claim uninformative. What is worse, it would make the autonomy claim trivially true, by constructing technological knowledge as an epistemic system that is different from science. Thus, this solution might offer only formal consolation, without furthering emancipatory ends.17

17 This can be avoided if the identification of “genuinely technological” elements of technological knowledge is followed by an analysis of their epistemic character. Even then, however, one might do no more than make explicit one’s intuititions regarding the epistemic differences between science and technology, since these intuitions might be presupposed in the identification of the “genuinely technological” elements.


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Another, more roundabout solution would be to argue that the inclusion of knowledge from scientific disciplines such as mathematics and physics is only apparent. One might maintain that this knowledge is either selected from that available within the discipline by criteria that are unique to engineering science and design — meaning that the distinctive nature of technological knowledge features in the criteria of selection; or that scientific knowledge is adapted to engineering purposes — meaning that the distinctive nature of technological knowledge features in the content of every (sub-)category. Both of these options are familiar from Section 2: they are two ways in which the contrastive strategy for epistemic emancipation may be developed. This does not mean that this roundabout solution must come to naught. Yet it does mean that, as soon as “scientific knowledge” is included among the (sub-)categories of a taxonomy of technological knowledge — as in the four reviewed taxonomies — the taxonomical strategy for emancipation reduces to the contrastive strategy. 5 THE DOUBLE-DEMARCATION PROBLEM Apart from the problems with individual taxonomies discussed in the previous section, there is a more general problem that needs addressing. This problem affects both the contrastive and the taxonomical strategy for epistemic emancipation. To appreciate this general problem, it is worthwhile to consider the other end of the epistemic-emancipation problem: scientific knowledge. Suppose someone is interested in making a list of types of scientific knowledge, for instance to distinguish possible contributions to an encyclopaedia of science. There are various ways of organizing this classification, requiring some kind of principled decision, as discussed in the previous section. Suppose this decision is guided by the results of science, such as the lawlike regularities that form the backbone of scientific theories, or the theories themselves. Thus, one obtains entries about Newton’s laws of motion or classical mechanics, about chemical bonds, or the regularities guiding supply and demand. In addition, a second decision is required, one that concerns the boundaries of scientific knowledge. One needs to decide why (not) to include controversial regularities, such as homeopathy’s laws of similars and infinitesimals or the correlation between fossil-fuel consumption and climate change. And one needs to decide whether to include models and phenomenological laws, which merely describe and do not explain by referring to some underlying causal mechanism. Without these decisions, one about the guiding classificatory principle and two about the boundaries of knowledge, a list of scientific knowledge would be arbitrary. Yet at least one of these decisions is notoriously hard to make in a principled way: the decision to exclude, for instance, the central tenets of homeopathy amounts, of course, to the familiar problem of demarcating science from pseudoscience, or unscientific knowledge. The failure of various purported demarcation criteria forms the backbone of many introductions into the philosophy of science. There may be characteristics that many sciences have in common, and some that

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most do not have in common with pseudo-science, nonsense or common sense; but if there is an acceptable, clear-cut criterion to be had, no-one has been able to formulate it. Fallibility, confirmation, prediction and explanation seem central elements, but philosophers cannot even agree on these elements, let alone on a slogan that captures them all. This problem affects taxonomies of technological knowledge, if they are used for epistemic emancipation. Firstly, the negative experiences with demarcation in science provide inductive support for pessimism about similar inquires into technological knowledge. Thus, compiling an encyclopaedia of technological knowledge seems at least as arbitrary as the encyclopaedia-of-science project. A complicating factor is that ‘technological knowledge’ is, to some extent, a technical term. Whereas ‘scientific knowledge’ sees a considerable amount of everyday usage, one seldom comes across descriptions of a model or research result as ‘technological knowledge’. This means that determining the boundaries of this type of knowledge may be, in a sense, easier than determining those of scientific knowledge. There may be entries that are beyond controversy, and some of these have been used as examples of technological knowledge in the literature: Vincenti’s [1990] control-volume analysis, Constant’s [1999] material-balance analysis, finiteelement analysis, and Smith’s [1960] metallurgy all come to mind. Beyond the domain of those examples there is, undoubtedly, a grey area, but if ‘technological knowledge’ is indeed a technical term, this part of its extension may be determined by stipulation. Although stipulations are, in this case, a legitimate way of solving boundary problems, they make fully explicit the arbitrariness of this constitutive rule for compiling a list of technological knowledge. To give two examples: all the paradigmatic entries mentioned above concern knowledge that is produced and employed by engineers, but technological knowledge might also conceivably include the instrumental knowledge that users possess about their cars and computers; and all entries mentioned above concern knowledge that can be expressed verbally, whereas much of our knowledge about technology appears to consist of know-how and competences. To be sure, one might resolve the latter issue by distinguishing ‘technological’ knowledge from ‘technical’ knowledge, where the latter consists of non-codified or non-codifiable techniques for achieving practical purposes. This is not only a stipulation, but also a distinction that does not seem to guide any of the taxonomies of technological knowledge currently on offer — all the examples reviewed in the previous section either explicitly include know-how and competences or, in De Vries’ case, do not exclude them. Thus, a taxonomy based on this distinction would be idiosyncratic, even if there is no rich tradition with which it would break. To make things worse, the demise of demarcation as a philosophical research project affects the taxonomical strategy in another way. If constructing a taxonomy of technological knowledge is to serve the purpose of emancipating it from scientific knowledge, it should at least be clear in what principled way the taxonomy distinguishes both types of knowledge. There should, in other words, be a


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reason why some item is included in one list rather than another. It is possible to construct a list without first explicitly stating some criterion for including items: the list may be constructed extensionally, by including knowledge that is produced in an engineering context, or knowledge that concerns the use and design of technical artefacts. Still, if such a taxonomy of technological knowledge is to serve emancipatory purposes at some point, it encounters a double demarcation problem. It should put clear boundaries to the term ‘technological knowledge’ and, simultaneously, distinguish it from the equally vague term ‘scientific knowledge’. Even philosophers without great sceptical inclinations might feel cagey about such an enterprise. There may be clear and uncontroversial examples of scientific and technological knowledge, and these may serve as prototypes for distinguishing the two epistemic categories. However, the mere existence of paradigmatic examples does not solve a demarcation problem. Hardly any philosopher of science would deny that Newtonian mechanics and the knowledge compiled in your local phonebook may serve as paradigms for scientific and non-scientific knowledge. Still, a criterion is needed for evaluating borderline or otherwise disputed cases. Paradigmatic examples may be used to check candidate criteria, they do not supply them. If one seeks to establish that technological knowledge is autonomous, and if a taxonomy is to be useful for that purpose, one needs to determine what should be classified as technological knowledge, and what should not.18 As an illustration of the double-demarcation problem, consider the Carnot engine. This hypothetical artefact was first introduced by Carnot in his Réflexions sur la Puissance Motrice du Feu (1824). Our present-day description of it is largely based on Clausius’ work in the 1860s. The engine has played a pivotal role in the development of, in particular, the concept of entropy, and it is a standard element of introductory textbooks on thermodynamics. Like any heat engine, the Carnot engine involves the conversion of heat transfer into mechanical work, in a completely reversible cycle (the Carnot cycle). Since, in reality, heat-engine cycles always create entropy and thus cannot be completely reversible, the Carnot engine is an idealization. It is, however, a useful idealization: it increases our fundamental understanding of heat-transfer processes, and it can be used to determine the maximal efficiency of thermodynamic engines. If we were to construct a taxonomy of human knowledge, both scientific and technological, it is not clear how to classify the Carnot engine. That it should be classified is beyond reasonable doubt, since Carnot’s work is generally regarded as a major intellectual achievement. Still, the engine is an idealization, putting Carnot’s work squarely in the gray area of thought experiments. Moreover, it is 18 An alternative would be to examine whether the paradigmatic examples of technological knowledge, say Pambour’s theory of the steam engine, is autonomous from scientific knowledge. This may be a viable and much-needed epistemological project, but it is much less ambitious than examining the autonomy of the entire category of technological knowledge. At most, studies into specific types of technological knowledge yield hypotheses about what might be epistemically distinctive about all technological knowledge. But to check this hypothesis, a complete inventory of technological knowledge would be needed, leading back to the (double) demarcation problem.

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both the cornerstone of an important scientific theory, thermodynamics, and a (fictional) artefact that provides guidelines or limitations for the design of technical artefacts. As such, it seems to have earned its place in both the body of scientific knowledge, and that of technological knowledge. Nevertheless, Kroes [1992] classifies Carnot’s theory about heat engines as scientific, contrasting it with Pambour’s “engineering” theory on the basis of his definition. One may have a principled reason to classify all our knowledge about Carnot engines as either scientific or technological, or some as scientific and some as technological — but the list itself does not make this reason explicit: one needs something like Kroes’s definition. In this sense, a taxonomy requires a solution to the double-demarcation problem rather than providing it. Moreover, it seems that a principled reason should be, or can only be, derived from an in-depth study into the use and structure of idealizations in science and technology, or natural and engineering science. If the Carnot engine would be presented in exactly the same way in textbooks for physicists and engineers, and if statements regarding the engine would have the same epistemic value in both domains, classifying this knowledge as either scientific or technological would be an arbitrary decision: nothing would be at stake. In this sense, the taxonomical strategy for emancipating technological knowledge depends on the contrastive strategy — which was earlier shown to be underdeveloped. 6 TECHNOLOGICAL KNOWLEDGE AS TACIT Using ideas and notions developed by Michael Polanyi [1958; 1966], some authors have emphasized the importance of tacit knowledge in engineering and technology.19 They have argued, or stated, that part of the knowledge produced by technological practice is hard or even impossible to make fully explicit in declarative statements, but can only be acquired through personal experience. Some make tacitness part of their characterization of technological knowledge, e.g.: “(. . . ) the knowledge of techniques, methods and designs that work in certain ways and with certain consequences, even when one cannot explain exactly why.” [Rosenberg, 1982, p. 143] Others use technological practice to characterize tacit knowledge, e.g.: “(. . . ) the implicit, wordless, pictureless knowledge essential to engineering judgement and workers’ skills.” [Vincenti, 1990, p. 198] This emphasis on tacit knowledge is not exclusive to the philosophy of technology. In fact, most work on tacit knowledge and technology is done outside of philosophy. One field where this relation is especially prominent is that of knowledge management, where the communication and sharing of knowledge is a central concern (e.g., [Nonaka and Takeuchi, 1995; Choo, 1998; Baumard, 1999; Firestone and McElroy, 2003]). Other fields where tacit knowledge is an important point of concern are the design of expert systems (e.g., [Berry, 1987]) and studies of 19 Nightingale’s contribution contains more details of the literature on tacit knowledge, and focuses on its possible importance for understanding engineering design, rather than for understanding the nature of technological knowledge.


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technological innovations and technology transfer (e.g., [Nooteboom et al., 1992; Senker, 1993; Howells, 1996; Leonard & Sensiper, 1998; Nightingale, 1998; Wong and Radcliffe, 2000; Salter & Gann, 2003]). Here, the appeal to tacit knowledge is typically used to indicate the firm-specificity and person-dependence of knowledge. It is stressed, for instance, that successful implementation of new technologies requires detailed and specific knowledge about a particular situation, which is — at least at first — only available through personal experience and rules of thumb (e.g., [Arora, 1996]). Other authors point out that, although codified and explicit knowledge is available for more established technologies, effective use still relies on skilled operators and maintenance personnel, arguably showing that there is an irreducibly tacit component in technological knowledge (e.g., [Noble, 1978]). That many contributions to the literature on technological knowledge appeal to tacitness is beyond question; moreover, most do so by pointing out that it has been generally overlooked, because of an exclusive focus on codified knowledge, and that it is essential to a full account of knowledge. Thus, the existing literature seems based on the idea that there is something distinctive about tacit knowledge, and perhaps also something distinctively tacit about technological knowledge. This makes the appeal to tacit knowledge potentially interesting for the epistemic-emancipation project. Yet to see how and to what extent the appeal to tacit knowledge could improve our understanding of the nature of technological knowledge, and emancipate it from scientific knowledge, two questions need to be answered. Firstly: what is the relation between tacit and technological knowledge? Secondly: is the tacitness of technological knowledge more prominent or encompassing than that of other types of knowledge? Insofar as these questions have (implicitly) been answered in the literature, the answer to the crucial second question appears to be negative. Let us tackle them each in turn. Virtually everyone who writes on tacit knowledge, even those who do not ultimately use the term, agrees about one conceptual issue — that there is something about human knowledge that standard, justified-true-belief or propositional, accounts do not capture. Beyond this stage, however, there is considerable disunity about the appropriate concepts, concerning both the phenomenon of “tacitness” and the standard view(s) with which it supposedly contrasts. At least three distinctions are at issue in the literature. These distinctions are related, but different, and they are seldom distinguished as carefully as they should be. Firstly, there is, what might be called, the psychological distinction between implicit and explicit knowledge (e.g., [Dienes and Perner, 1999]; see also [Reber, 1993]).20 One way to phrase this distinction is as follows: when we know a fact, we have an accurate representation of it. On the basis of its functioning and its accuracy, this representation may be identified as “knowledge” (rather than a desire). If we are 20 By calling this distinction ‘psychological’, I do not mean that it is a unanimously accepted part of contemporary cognitive or developmental psychology. This distinction is, however, mainly discussed by cognitive psychologists, and concerns the functioning of representations rather, e.g., than the justification of statements.

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not aware of this representation, and its accuracy, the knowledge is implicit. By contrast, our knowledge concerning this representation is fully explicit. Phrased in this way, the psychological distinction is gradual, and all distinguished states involve representations. Secondly, there is a grammatical or linguistic distinction between two types of statements involving ‘knowing’: knowing that something is the case, and knowing how something can be done. This distinction is, of course, primarily associated with work by Gilbert Ryle [1949], and it is clearly language-relative. In some languages, like German and Dutch, this distinction is expressed by means of similar-sounding words (‘kennen’, as in ‘Ich kenne Der Zauberberg nicht’; and ‘k¨ onnen’, as in ‘Ich kann Schlittschuhlaufen’, respectively) rather than one word; other languages may even use completely different words. Thirdly, types of knowledge may be distinguished social-epistemically, with regard to their communicability: knowledge that can be transferred exclusively through verbal communication, and knowledge that is not or cannot be so transferred — for instance, because it can only be acquired through personal experience. I will call the first type ‘verbal’ knowledge, and the second ‘non-verbal’. When introduced, the term “tacit knowledge” is typically used in the latter sense. However, the interconnections with Ryle’s primarily grammatical distinction are particularly strong in the philosophical literature, so that the actual use of “tacit knowledge” is at least ambiguous in this respect. Even authors who do not explicitly refer to Ryle often use terminology reminiscent of his, and refer to the same stock examples, e.g., of riding a bicycle. Furthermore, connections are forged with the (folk-) psychological distinction between knowledge and skills. This is frequently equated with Ryle’s distinction, and “tacit knowledge” is taken to refer to skills and “know-how”. Much more may be said about this, but I will cut some corners in calling this distinction a red herring. The reason is that, as soon as the unicity and autonomy of technological knowledge is sought by assimilating it to skills, the epistemic-emancipation project becomes open to the objection that it is based on a category mistake. After all, if skills are contrasted with knowledge, and the difference between technology and science is based on this contrast, the sought (and perhaps found) difference cannot be epistemological: it is not a distinction between types of knowledge, but between knowledge and something else, e.g., action. Thus, the frequent appeals to tacitness, and discussions of this phenomenon with regard to technology, suffer from multiple ambiguities in the very notion of “tacit knowledge”, which affect its usefulness for the epistemic-emancipation project. One may think that, while these ambiguities are being sorted out, a preliminary epistemological distinction may be made between fully explicit, propositional, verbal knowledge on the one hand, and the overlooked “tacitness” phenomenon on the other hand. This would, however, be naive, since a major epistemological distinction is concealed beneath the conceptual distinctions.21 21 This epistemological distinction is only occasionally made in the literature; see, e.g., [Gorman, 2002].


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One possible understanding of tacit knowledge is as a supplement of the traditionally analyzed body of propositional knowledge. The associations with Ryle’s sharp grammatical distinction, and with the knowledge-skill distinction facilitate this understanding. From a psychological perspective, this understanding is highly problematic: one may think up all kinds of cognitive processes that share characteristics of both types of knowledge, undermining the idea of supplementary, but autonomous bodies of knowledge. The internalization of calculation rules is, for instance, a process that shows how explicit, and highly verbalized procedures can turn into implicit routines through frequent exercise. This does not mean that the distinction is conceptually indefensible, but it does not have the immediate plausibility of Polanyi’s original appeal to tacitness. Moreover, this understanding has the disadvantage of inviting the category-mistake objection mentioned above: if tacit knowledge is this skill-based supplement to propositional knowledge, why call it “knowledge” at all? Therefore, some psychologists — and researchers in other disciplines who take psychological studies into account — prefer another understanding of the appeal to tacitness (e.g., [Wagner, 1987]). On it, our body of knowledge contains a tacit element, in all senses distinguished above: explicit knowledge must be based on implicit knowledge, which is at least conceptually prior; knowledge-that always involves knowing-how, since it involves, among other things, competence in reasoning; and verbal knowledge presupposes non-verbal knowledge, if only in the trivial sense that we cannot make fully explicit our speech patterns, including rules for appropriate utterances and other pragmatic aspects of language. Some passages in the writings of both Ryle and Polanyi suggest this understanding of tacitness — as a general aspect, component or ‘dimension’ of knowledge. And although this view requires substantial elaboration, it does not have the abovementioned disadvantages of the first understanding. Gradualism can be captured by analyzing knowledge as having a more or less prominent tacit component; and since tacitness is an integral part of all knowledge, it is an appropriate subject for epistemology. This understanding of tacit knowledge answers the two questions posed earlier. Firstly, technological knowledge may be said to have a strong relation to tacitness. Both the knowledge possessed by designers and that possessed by users, and even the more theoretical models of engineering sciences involve a tacit component. Indeed, some examples in the general literature on tacit knowledge, such as Ryle’s bicycle riding, are derived from the technological domain broadly conceived (albeit mainly from the user’s perspective); and both design and use are clearly competence-based activities, easily described in terms of knowing-how. That they also involve knowing-that, and can in various degrees be verbalized does not run counter to the appeal to tacitness in this sense.22

22 On a gradualist understanding of tacit knowledge it is problematic to make in one’s taxonomy a sharp distinction between competences and know-how on the one hand and “theoretical” knowledge on the other hand.

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However, on this understanding of tacitness, there is almost by definition no special relation between it and technological knowledge. If the arguments of Ryle and Polanyi are sound, they would show that all knowledge contains a tacit component. And their general arguments have been supported by a host of more specific studies in cognitive psychology (e.g., [Reber, 1993]) and the epistemology of science. Like engineers, scientists are said to rely on rules of thumb in designing experiments and interpreting data, and to require personal experience in addition to theoretical education (e.g., [Collins, 1973, 1982; Senker, 1993; Sternberg and Horvath, 1999]). The works of Donald Sch¨ on [1983; 1988] also illustrate this generality. Some of Sch¨ on’s examples are drawn from domains that might be called technological; and he often phrases his general claims by referring to “technical” on’s claims regarding the importance problems or “design” contexts.23 Yet Sch¨ of personal experience and improvisation concern professional practice in general, not engineering design in particular. In sum, the literature on tacitness in technological knowledge shows a lack of conceptual clarity. Furthermore, insofar as clarity can be obtained, appealing to tacitness does not further the end of epistemic emancipation. Instead, it increases the burden of proof resting on those who want to establish emancipation through pointing out the role of tacit knowledge in technology: rather than showing that such knowledge plays a role, they should show that it plays a distinctive role. 7 SOCIAL SCEPTICISM In this section, I will follow up my observations about ambiguities and lack of emancipatory arguments in the current literature with a general argument. This argument concerns the social-epistemic understanding of tacit knowledge, i.e., as knowledge that is not communicable by verbal means. This incommunicability may, in itself, not be a distinctive characteristic of technological knowledge; if all knowledge contains a tacit component, it is all impossible to make fully explicit by verbal means. What is more, there seems to be hardly any knowledge that cannot be made partially explicit. Even in the standard example of cycling, it is possible to state some rules concerning the use of a bicycle (e.g., “Sit on the saddle, and put your feet on the pedals”). Thus, there appears to be a spectrum, ranging from knowledge that can be almost fully expressed verbally to knowledge that is virtually inexpressible by verbal means. All knowledge claims, scientific, technological and other, are somewhere on this spectrum. One might argue that technological knowledge is, on the average, more toward the inexpressible end of this spectrum than scientific knowledge; or that it occupies an interval more to the inexpressible end. Then, tacitness would be more characteristic for technological knowledge than for scientific knowledge, even though it 23 E.g., “It is not by technical problem solving that we convert problematic situations to wellformed problems; rather, it is through naming and framing that technical problem solving becomes possible.” [Sch¨ on, 1988, p. 5; emphasis added]. Here, the context makes clear that Sch¨ on refers to problem solving in domains such as medicine and law as well as engineering.


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is not a distinctive mark. To establish this, one might point to empirical research regarding technological innovations or technology transfer. In the literature, examples of which were referred to above, it is often stressed that tacitness creates transfer problems — problems that are key issues for knowledge management and the development of expert systems. If similar empirical results have been found for scientific knowledge, they have neither gained the same prominence nor set a similar agenda for new subdisciplines. Thus, one might conclude that these empirical studies show at least a gradual distinction between the two types of knowledge. This empirical conjecture might very well be falsified. But let’s accept it for the sake of the argument. For even if technological knowledge were relatively illexpressed and scientific knowledge relatively well-expressed, this does not establish epistemic emancipation. The reason is that this empirical difference might not be the result of the nature of technological and scientific knowledge, but of the social organization of science and technology. If every bit of knowledge is verbally expressible to some extent, verbally expressing it becomes not just a matter of degree, but also of practical interest. Take, again, the example of riding a bicycle. If someone is the only bicycle rider in the world, expressing one’s knowledge of how to ride a bicycle would be of no, or at most of personal, interest. However, as soon as someone wants to learn another person how to ride a bicycle, verbal expression becomes relevant. Yet how relevant it is depends on a number of factors, including the capacity of the educated person to respond to verbal instructions, the difficulty of acquiring the competence without any verbal instructions (if anyone could ride a bicycle on first trial, verbal instructions for it become as useful as breathing instructions), and the educator’s willingness to teach the competence without trying any verbal “shortcuts”. The extent to which cycling know-how is expressible enters the equation somewhere, but it is hard to say where exactly. Assume that someone lives in a society where there is a high demand for cycling instruction manuals. In these circumstances, verbal expression of cycling competence is a socially, perhaps even financially, rewarding enterprise. It would be reasonable for cyclists to invest considerable time and effort into moving their knowledge of cycling further towards the fully-expressed end of the knowledge scale; if someone would succeed in making her implicit knowledge slightly more explicit, she might acquire an edge over competitors on the market for cycling manuals. This example is fictional and rather trivial.24 It does show, however, the close connection between epistemic, social and even economic aspects of the tacit component of knowledge. This connection was to be expected, since “tacit knowledge” can be defined as a social-epistemic concept. For this concept, verbal expressibility of knowledge, its actual degree of expression, the social need for expressing it 24 To take another example: “being a successful manager” is a difficult skill (if it is even one skill) to express verbally. Yet there is a substantial market for even the most partial verbal expressions, in the form of lectures and books about management. Thus, the amount of “expression attempts” may say little about the expressibility of skills and knowledge, and much about economic viability and social need of attempts.

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(given a division of labour or of expertise), and economic interests in expressing it can and should be kept apart analytically. Yet it is difficult to decide which of the four factors must be invoked to decide where a knowledge claim is to be placed on the tacitness scale. This difficulty arises in full force if one wants to explain the different places that scientific and technological knowledge take on this scale (remember that above, we assumed that they take different places, or occupy different intervals). Technological knowledge is acquired in a certain social context, in which it is more or less profitable to express this knowledge verbally. To take an extreme example: if engineering were an exclusively one-person enterprise, and if practically useful items were a highly valued commodity, verbally expressing one’s knowledge of how to design these items would not be worthwhile and might even be disadvantageous. Suppose that, by contrast, there were no scarcity: all material needs were fulfilled by means of imperishable or very easily replaced artefacts, and human beings were virtually immortal. Then, the design of new artefacts could be an activity for artificers who merely want to satisfy their curiosity. These artificers might verbally describe to each other their design knowledge in excruciating detail — supposing they had nothing better to do. To put it very roughly: the current social circumstances of engineering involve considerable scarcity, a marked division of labour between professional designers and end-users, heavy commercialization, an increasing amount of teamwork in design, a decreasing loyalty of employees to companies, and heavy competition between companies that design new artefacts. On the one hand, in these circumstances, verbally expressing design know-how (an important element of technological knowledge) is advantageous to companies to some extent, since it facilitates teamwork, and improves the continuity of design work despite job-hopping employees. Hence, knowledge management is an economically interesting enterprise. On the other hand, there is a point at which further verbal expression of design knowledge becomes economically uninteresting — the costs of further expression outweigh its benefits — or even potentially disadvantageous, because another company could conceivably steal the entire body of design knowledge. Thus, the actual degree of expression (or codification) of technological knowledge may be largely due to socio-economic circumstances, not to the nature of the knowledge involved. The same argument may be given for scientific knowledge. Science shares many of the features of technology indicated above: there is scarcity of (epistemic) resources, a division of labour between researchers and laypeople, at least some commercialization, an increasing amount of teamwork in most disciplines, transfer of researchers between institutions, and competition between researchers and institutions. Yet there may also be differences. Following Merton’s (1973) identification of instutional norms in science, one could maintain that scientists should communicate their results and the way in which they achieved them. Furthermore, the market for scientific research results probably has a different structure from the market for technical artefacts, especially if (again following Merton) one thinks that scientists cannot claim ownership of knowledge. As a result, the competition


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involved in science would be different from that in engineering. Consequently, the cost-benefit analysis for the verbal expression of scientific knowledge may also be different: the professional obligation to share results, combined with a possibly milder form of competition, may suffice to pull scientific knowledge towards the well-expressed end of the tacitness scale. Again, this place would then be explained by appealing to social circumstances rather than to any epistemically distinctive features of science. A similar argument has been expressed by economists who are interested in tacit knowledge (e.g., [Cowan et al., 2000; Balconi, 2002]),25 and criticized by others [Johnson et al., 2002]. In this section, I have stated this in a more general form, as a counterargument to epistemically emancipating technology from science by appealing to tacitness. This “social-skepticism” argument is vulnerable to several objections, including charges of misrepresenting and oversimplifying the sociology of both science and technology. Although it is probably guilty of those charges, the argument does not require empirical adequacy: it only purports to show that, even if science and technology might be on different ends of the sliding “tacitness” scale, this difference might be a result of the social organization of science and technology. Some sociological storytelling suffices to show this. As a consequence, this alleged difference in tacitness does not entail that science and technology are epistemically different. To counter this argument, one needs to show that the alleged prominence of tacitness in technology is not only real, but also a matter of epistemic necessity rather than a social contingency. Given the state of confusion concerning tacit knowledge and the unwillingness of many sociologists of science and technology to make a clear distinction between social and epistemic matters, such a counterargument may be a long time in coming.

8

PRESCRIPTIVE KNOWLEDGE

One existing strategy for epistemically emancipating technology from science remains to be discussed. Consider the following quote: The engineer, and more generally the designer, is concerned with how things ought to be — how they ought to be in order to attain goals, and to function. [Simon, 1981, p. 7] Science is allegedly descriptive because it is aimed at truth or empirical adequacy; by contrast, engineering is supposed to be at least partly prescriptive because it is aimed at changing reality: “(. . . ) The modal mood of a pure scientist is largely 25 The former paper includes the following telling quote: “Any individual or group makes decisions about what kind of knowledge activity to pursue and how it will be carried on. Should the output be codified or remain uncodified? Are the inputs to be made manifest or latent in the production process? For an economist, there is a simple, one-line answer: the choices will depend on the perceived costs and benefits” [Cowan et al., 2000, p. 214].

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descriptive, while the mood of engineering is generally prescriptive” [Hendricks et al., 2000, p. 278]. Some specifications of this difference reveal puzzlement and confusion rather than characteristics of technological knowledge. To give an example, Vladimir Hubka and W. Ernst Eder [1990] present a variety of types and forms of what they call “design knowledge” — an epistemic category that seems to overlap significantly with technological knowledge. Besides presenting a list of types and a diagram depicting connections between design knowledge and other areas, Hubka and Eder classify the types by means of two distinctions: that of product versus process, and that of descriptive versus prescriptive statements. These two distinctions are both useful and relevant.26 However, Hubka and Eder undermine the quality of their analysis by next presenting “maps” of statements and knowledge [1990, Figs. 4 and 6] in which the distinctions are represented by orthogonal continuous lines, and individual contributions to design knowledge by areas within the graph. Representing the distinction between descriptive and prescriptive statements as a sharp dichotomy might be an oversimplification, but representing it as a continuous scale without any argumentation or even examples of intermediate cases “resolves” some thorny philosophical issues with, literally, a single stroke.27 Furthermore, some authors presume that pointing out the presence of prescriptive statements in technological knowledge suffices to differentiate it from scientific knowledge. Taken literally, this is incorrect. Paradigmatic examples of scientific knowledge, such as physics, comprise prescriptive as well as descriptive statements. A widely used textbook on electrodynamics,28 for instance, contains prescriptive statements such as: “It is useful to keep track explicitly of the total fields propagating in the two directions”, “Because of the generality of the contribution from the shadow region, it is desirable to consider it separately” [Jackson, 1975, p. 372, p. 448; emphasis added]. One might object that engineering texts contain a greater proportion of prescriptive statements, or more prominent ones. Indeed, the statements above were collected from a substantial sample of a large textbook. Yet making this supposed feature of technological knowledge into a topic of textual statistics is not exactly clarifying the issue at hand. Alternatively, one might attempt to convert the prescriptive statements in the physics textbook into descriptive ones, such as: “An accurate description of the propagation depends on A(ω) as a function of complex ω” or “A general model is obtained once one considers separately the contribution from the shadow re26 This essay does not consider the product-process distinction. Yet an analysis of technological knowledge is bound to include it, given the product-process ambiguity of the central notion of “design” and of “technology” itself. 27 To do Hubka and Eder justice, it should be remarked that their [1990] paper is a brief summary of a significant body of work on design knowledge. Yet the continuous-line diagrams also appear in other work, e.g., [Hubka and Eder, 1988], without lengthier arguments for choosing this particular representation. 28 A handbook on a highly theoretical part of physics was chosen to prevent the objection that all sample prescriptive statements are engineering intrusions in science, related to the design of experiments or the interpretation of their results.


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gion”. This conversion does not show that these statements given above were pseudo-prescriptive, i.e., that they can be reduced to descriptive statements. For to capture their meaning fully, explicitly prescriptive statements concerning accuracy and generality should be added: knowledge of A(ω) as a function of complex ω is only required if accuracy is a guiding value, and separate consideration of the shadow region is only desirable if generality is a desideratum. Hence, closer analysis of the prescriptive statements from physics shows that they presuppose scientific values such as empirical adequacy and generality. Therefore, analysis of prescriptive statements from physics and engineering may well return us to familiar grounds, namely the TU-intuition that science is directed towards truth and technology towards usefulness (see Section 2). Because this intuition is primarily one of values, it is only to be expected that handbooks from both physics and engineering contain prescriptive statements, but that these are related to the different central values of the disciplines. To go beyond restating the TU-intuition, one should do more than note prescriptive statements in technology, or their relation to the goal of usefulness or changing reality. One way to do this is to analyze the fact that technological knowledge is not about just any change in reality, including the diffusion of gases or the construction of theories, but about deliberate changes that serve practical purposes. This analysis starts from the seemingly trivial observation that technology is related to human, intentional actions. Most technical artefacts and processes do not occur naturally, but need to be designed and manufactured. Few artefacts realise their functions automatically, but require active manipulation by a user. And even artefacts that function more or less automatically, such as fire alarms or assembly-line robots, require monitoring and maintenance. Because technology is intimately action-related, it makes sense to assume that technological knowledge is related to designing, using and other actions as well. Moreover, since the goal of technology is to make useful changes in reality, these actions cannot just be described, but they must also be prescribed. To employ a car or an assembly-line robot, a user has to know not only for which purposes the artefact may reliably be used, but also which actions he or she should take, might profitably take when certain situations arise, or how to recognize undesirable behaviour of the artefact. In short: the practical aim of technology implies that technological knowledge prescribes and recommends intentional actions. It does not just describe what is the case, or what is desirable, but also what human beings should do to bring this desirable state of affairs about. This forges an intuitive distinction between technological knowledge and knowledge gained in the behavioural and social sciences, which seem primarily descriptive. Given this starting point, a closer analysis of technological knowledge may employ (and require) action-theoretical resources rather than notions and perspectives borrowed from traditional epistemology or philosophy of science. Hence, one may look for teleological notions such as “goal” and “function”;29 one may 29 See

Preston’s contribution to this Handbook.

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study the role of practical reasoning;30 and one may investigate the status and justification of rules and recommendations in technological knowledge. This shift of perspective is non-trivial, especially given the goal of epistemic emancipation. Studying the distinction between scientific and technological knowledge by using notions and perspectives developed for understanding the former leads to questions like those posed at the end of section 2. As said there, few enough attempts have been made to answer these questions. Moreover, emancipation may require a different perspective altogether: action-theoretical terminology might be more appropriate for an understanding of technological knowledge than it is for an understanding of scientific knowledge. One attempt to develop such an understanding is made by Mario Bunge. According to Bunge, one characteristic product of the engineering sciences is a technological rule, “an instruction to perform a finite number of acts in a given order and with a given aim” [1967, p. 132]. An example would be “If you are interested in comfortable private transportation, drive a car”, where driving a car is a specific series of actions: getting in the driver’s seat, starting the car, etc. Similar rules may be specified for other goals and action types, including design and maintenance. As it stands, this way of characterising the prescriptive content of technological knowledge is rather broad and non-specific. The description given in the quote above applies to all practical rules, including: “When you are caught in a thunderstorm, avoid trees and large bodies of water and roll up in a ball”. Taking technological knowledge as a part or a continuation of such common-sense practical knowledge may be correct as a first approximation, like taking science to be the continuation of common sense, but much work remains to be done to go beyond this first approximation. indent Bunge does that by explaining how technological rules are, in the engineering sciences, grounded in scientific knowledge and elaborately tested, leading to a tremendous growth in reliable and productive rules after the Industrial Revolution. In this way, technological knowledge may indeed be distinguished from run-of-themill practical rules,31 but Bunge’s choice has a high price: the “grounding” claim regarding technological rules has made Bunge’s work a standard target in the applied-science debate. Moreover, it seems to have made people so suspicious of the notion of technological rule that critical analyses have been screened-off by criticisms of Bunge’s supposed applied-science thesis. Yet it may be possible to employ the notion of technological rule without accepting Bunge’s claims regarding grounding. One possibility is to consider the role of artefacts in such rules. Many practical rules, like the one concerning thunderstorms, involve only our own body; others, like “Do not drink salt water, even if 30 See

Hughes’ contribution to this Handbook. if being grounded in scientific knowledge is to serve as a distinguishing characteristic, it should subsequently be clarified how instructions for driving a car are so grounded, whereas instructions for avoiding death by lightning are not. It is not clear whether even a gradual distinction may be gained in this way. 31 Still,


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you are very thirsty”, involve our bodies and natural objects. Whether these rules are grounded in scientific knowledge or not, they seem to involve techniques, rather than technology. By introducing artefacts, Bunge’s characterization of technological rules may be amended into “instructions to perform a finite set of actions, including manipulations of one or more artefacts, in a given order and with a given aim”. This idea of technological rules has been developed, in different terms from Bunge’s, by Pieter E. Vermaas and myself. The central notion in this line of research is that of use plans, “goal-directed series of considered actions, including manipulations of one or more artefacts” [Houkes and Vermaas, 2004]. Both use — the execution of use plans — and design — the construction and communication of use plans — can be analysed in terms of this notion [Houkes et al., 2002]. The resulting perspective on technology and technological knowledge emphasizes goal-directed, intentional actions and the standards of (instrumental) rationality for these actions rather than the objects employed in such actions. It considers descriptive knowledge only insofar as it plays a role in intentional actions. Consequently, it provides action-theoretical resources for analysing the prescriptive content of technological knowledge. The use-plan account provides a picture of prescriptive technological knowledge that is richer than the notion of technological rules alone. Knowledge regarding use plans need not consist only of instructions: they might carry both stronger and weaker normativity. Artefacts may be used in many different ways, not all of which may or can have been envisaged by their designers. The use-plan account incorporates this by a liberal notion of design. Everyone, engineers and consumers alike, can design in the sense of constructing and communicating use plans. One need not have a degree in engineering to use an empty milk bottle as a vase — use that is as effective and efficient as it is widespread. Knowledge regarding this use may be regarded as technological, in the minimal sense that it concerns use of an artefact for a practical purpose. The corresponding knowledge, that milk bottles can be used for holding flowers, is normative [Houkes, 2006], but involves a recommendation in some circumstances rather than an instruction.32 Other knowledge regarding artefact functionalities involves requirements, which are considerably stronger than instructions. To give an example: some use is regarded as (im)proper, meaning that it is privileged over other ways of using an artefact. Such privileges may be analysed by referring to the fact that, although many agents are capable of designing, only some of them are professionally engaged in it. Their use plans are standardized and often even embedded in legal systems: many warranties, for instance, are declared void in cases of improper use. Thus, the use of artefacts is embedded in a (largely un-analyzed) system of rules, recommendations and requirements that is far richer than mere sets of instructions for attaining a goal. 32 See Franssen’s contribution to this Handbook on artefacts and normative judgements for a more detailed analysis.

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Furthermore, the use-plan account may be employed to study the relation between prescriptive and descriptive statements regarding artefacts. That there is such a relation seems beyond doubt: prescriptive statements that are not, somehow, related to accurate, propositional knowledge are at best very risky recommendations. Moreover, professional designers, engineers in particular, often possess knowledge about the physicochemical composition of artefacts, and design artefacts on the basis of this knowledge. On the use-plan analysis, one way in which prescriptive and descriptive statements regarding artefacts are related is by means of a specific type of explanation for the function of an artefact [Houkes, 2006; De Ridder, 2006]. In such ‘technological’ explanations, descriptions of the structure of an artefact are related to descriptions of the actions included in the use plan of the artefact, to show that these actions can be rationally expected to lead to realization of the goal state. That there are these explanations does not mean that prescriptive statements should be grounded in scientific knowledge, let alone that they are little more than “applications” of this knowledge. Some communicated use plans are, for instance, based on successful tests in a variety of circumstances, on trial-and-error, or simply handed down through generations of users [Vermaas and Houkes, 2006]. To conclude, the distinction between descriptive and prescriptive statements is in itself insufficiently specific for epistemic emancipation of technology. However, a closer analysis of some prescriptive statements made within a technological context — technological rules or recommendations and requirements regarding artefact use — might reveal a connection to intentional actions and practical (instrumental) rationality specific to technology. This analysis of prescriptive statements is still rudimentary, and it warrants further attention, even independently from the quest for epistemic emancipation. 9 OUTDATED EMANCIPATIONISM The review of the existing literature in the previous sections shows that there are several ways in which authors have tried to establish epistemic emancipation. Few ways are developed beyond the embryonic stage, none have given rise to elaborate discussions and refinement of points of view and arguments. What is perhaps most important, all have so far failed to establish strong emancipation. For some attempts, general arguments can be offered that appear to show that they are bound to fail; for others, analysis shows that specific issues need to be addressed — more specific issues than those covered by existing efforts. The results of the review are, in short, not encouraging. Establishing epistemic emancipation appears to require a concentrated, collective effort, aimed in part at overcoming some general counterarguments. It might, therefore, be understandable that historians and philosophers of technology have shifted their attention towards other topics: substantial effort would be needed to get the topic of the nature of technological knowledge off the ground, and the benefits might be so small that research time is more efficiently spent otherwise.


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Additional discouragement is given by an increasingly powerful movement in the literature on the history, philosophy and sociology of science and technology. The 1970s and 1980s not only saw a decline in the interest for technological knowledge, and the conceptual and epistemic distinctions between science and technology. During these decades, an alternative perspective and research agenda was promoted in the newly developed field of Science and Technology Studies (STS). This chapter is not about the history, central characteristics and many divergent results and approaches within this field. Yet it is beyond doubt that traditional epistemic issues are not high on the agenda of research in STS,33 and that many conceptual distinctions are typically regarded as outdated, or topics for deconstruction. Of particular interest here is that the distinction between science and technology has been subjected to criticism and revision, on the basis of both empirical, sociological research and more conceptual and methodological concerns. Many authors, including Don Ihde [1979; 1991], Bruno Latour [1987; 1993] and Andrew Pickering [1995], have pointed out or argued that scientific knowledge is not just historically and socially situated, but that it is acquired, distributed and defended in an increasingly intricate technological context. Scientists use technology to perform experiments, to manipulate and store data, to write research papers, and to communicate with other scientists. Many of these technological aspects of science are not merely contingent characteristics, but appear to be essential for science as it is conducted nowadays.34 Since the 17th and 18th century, science has been experimental and mathematical — but since the 1950s experimentation and mathematization increasingly depend on technologies such as lasers, computers, and satellites. For the authors mentioned above, and many other STS researchers, the role of technologies in scientific research is so prominent and inalienable that they prefer to speak of “technoscience” rather than “science”.35 Suppose that the main idea behind this neologism is correct, and that scientific knowledge can indeed not be studied in isolation from its technological context, because it is necessarily embedded in it. Then it may still be possible to emancipate technological knowledge with respect to scientific knowledge. After all, technology is not equated with science. There may be reason, also from a sociological point of view, to assume that technological knowledge is acquired, distributed and defended independently from scientific research.36 There may be institutional 33 Here, “traditional epistemic issues” means the issues regarding (among other things) truth, justification and epistemic virtues that characterize epistemology as studied in the AngloAmerican analytical tradition, and as reviewed in introductory books such as Audi [2002] and the essays in [Greco and Sosa, 1998]. Parts of the STS research agenda are and can be labeled as “epistemology” as well; take, e.g., many of the papers published in a journal such as Social Epistemology. 34 One clear expression of this sentiment is: “Modern Science, in contrast to its ancient and more contemplative origins, [is] essentially and necessary embodied in technologies, instruments.” [Ihde, 1993, p. 74; original emphasis). 35 Here, “technoscience” indicates specifically a system in which scientific research cannot be studied in isolation from its technological context. The notion is used in a broader variety of senses in the literature. 36 Many technoscience scholars claim that there is a reciprocal dependence relation between

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cross-connections, but if anything, these show that technology deserves closer epistemological attention, because it is so important for scientific knowledge. Yet epistemic emancipation of technology from science no longer makes much sense: the model of science as epistemically autonomous from technology needs to go, along with any epistemology based on this model. Therefore, there is no standard epistemology left with which to contrast an analysis of technological knowledge. To put it crassly: why argue that technological knowledge is autonomous from science, when scientific knowledge is thoroughly technological? The same point may be made by looking at the thesis that technology is applied science, which shaped so much of the literature on technological knowledge. If research on technoscience is correct, the thesis is at least unilluminating. The thesis conceals that (techno-)scientific research consists of the application of technologies, and may be shaped to a large extent by promises and expectations of future technological rewards. Technology may, in turn, be based in part of applying scientific theories, but this feature cannot be used as its most basic characteristic. The typical argument offered against the applied-science thesis is that some technologies have been developed without the aid of scientific theories. From the technoscience perspective, the argument is correct, but it ignores the deeper insight that scientific research is thoroughly technological — and it might in this way reinforce the mistaken epistemology that regards science as autonomous. These observations offer plenty of reasons to revise our understanding of the relation between science and technology, but no reasons to ignore the study of technological knowledge. On the contrary, they make analyzing the acquisition, distribution and defence of this kind of knowledge far more important than it would be on either the applied-science image or the autonomy image. After all, understanding the epistemology of contemporary technology, together with that of science, would be crucial to understanding technoscience. However, the quest for emancipation, which shapes virtually all work on technological knowledge, should be abandoned: it involves a false assumption about present-day scientific and technological research, and is therefore outdated. This line of thought offers a sociological or “empirical” counterpart to the more analytical counterarguments and problems presented in earlier sections. Together, I think they give ample reasons to abandon the quest for epistemic emancipation: whatever technological knowledge is, and from whatever perspective one wants to study it, one should not try to understand it as an epistemic category that is different from that of scientific knowledge. Before I tentatively suggest an alternative research agenda in the concluding section, let me address a question that might be raised by the previous reflections on technoscience. The question is: why did technoscience scholars not start to study technological knowledge afresh, given its importance for understanding the very phenomenon that they describe? They may have reasons to abandon contemporary science and technology, so that modern technology cannot be studied in isolation from scientific research. This claim may be true, but it is stronger than the earlier claim about science alone.


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the applied-science thesis, the ideal of epistemic emancipation, and perhaps traditional epistemology. Yet they have not replaced these views with an empirically informed, up-to-date epistemology of technology. Indeed, the very notion of “technological knowledge” is sorely lacking in the technoscience literature. Given the previous reflections, this is an oversight. One might speculate about the causes of this oversight. Perhaps the analysis of technological knowledge was so firmly associated with the misguided applied-science debate and the isolationist model of science that, in promoting a different perspective, STS researchers unwittingly threw away the baby with the bathwater. Surely, the abandonment of other traditional philosophical views, such as the fact-value distinction, makes it difficult or outright impossible to develop some of the more promising routes considered in this chapter. Understanding technological knowledge as prescriptive would, for instance, become as misguided as the applied-science thesis. Whatever the causes may be, I do not think that they offer sufficient reasons: technoscience scholars ought to analyze both technological and scientific knowledge, and their mutual dependence, just like philosophers of science and philosophers of technology should. It is time to give some indication how this analysis is still possible and useful in spite of the criticisms levelled at previous attempts. 10

AFFIRMATIVE ACTION AND FUTURE PROSPECTS

To conclude, I review the possibilities for making a fresh start in studying the nature of technological knowledge. I firmly believe that these possibilities exist. Besides criticisms, the preceding sections already contained some suggestions for future research. This section lists them again, by way of recapitulation, and adds several more general topics of research as well. Work on one or more of these topics might achieve weak epistemic emancipation of technology. The problems raised in the previous sections may not amount to fatal counterarguments and, in any case, mainly raise obstacles for strong emancipation. This leaves room for establishing a weaker claim. Even if one feels justified in abandoning the emancipation project altogether, there is still sufficient reason to develop the topics below. Not putting emancipation as first — or even only — item on the research agenda, but showing that epistemically interesting results may be gained by studying technology would constitute affirmative action. It would show philosophers that technology has been unwisely ignored, not because it is fundamentally different from science, but because good philosophical work can be done on it. Topics for further research proposed in previous sections include: • The role of practical usefulness (rather than truthlikeness) in validating theories and models developed in the engineering sciences (Section 2). • The role of practical usefulness in explaining the way in which theories and models from the natural sciences are adapted for use in the engineering sciences (Section 2).

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• Possible distinctions between scientific concepts and technical concepts in engineering theories (Section 2) • The different role of idealizations and hypothetical objects such as the Carnot engine in the natural and engineering sciences (Section 2; Section 5). • The relation between features of technological knowledge (e.g., tacitness) and the social organization of engineering (Section 7). • The distinction between technological rules and everyday techniques (Section 8). • The inherently prescriptive or, more broadly, normative content of technological knowledge (Section 8). In addition, the following two topics may be explored: Technology and the nature of knowledge The epistemology of technology has mainly been studied by considering technology as knowledge. Yet this does not exhaust the possible relations between technology and knowledge. As the sketchy review of research in science and technology studies in section 9 shows, technology is also related to knowledge, scientific and other, by providing much of the context in which knowledge is acquired, distributed and defended. These roles may be regarded as belonging to the context of discovery, and therefore rejected as a proper subject of epistemological studies. Yet they also require evaluation. Some of this evaluative work is done in what has been called the “philosophy of scientific experimentation” (e.g., [Radder, 2003]), in which the epistemic role of experiments and the technological devices used therein are studied. An even less explored topic is the extent to which new technologies allow researchers to acquire and support knowledge in hitherto unprecedented ways. The sciences nowadays do not only rely on technologically ever more complicated experiments. Scientific observation is not just theory-laden, but has become, perhaps irreversibly, technology-laden as well. Software is used to gather, manipulate and graphically represent data, and both natural and social scientists are trained in using computers to solve mathematical problems. Some of these problems might have been solved, with considerable effort, by some unaided human brains; in other cases, computers apply approximation techniques on a scale that is at least practically impossible to achieve for human beings; and in a growing number of cases, computers solve problems that have proved humanly intractable. This epistemic use of technology resembles its use for, e.g., transportation: in some cases, it is merely convenient, like driving to the supermarket instead of walking; in others, it is clearly more effective, like crossing the Channel — which some gifted individuals can do swimming, but most of us cannot; in still others, like flying to the moon, it is indispensable.


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The use of technology in scientific research might lead to particular methodological questions, e.g., concerning automated proof systems. It also leads to epistemological issues. One such issue concerns possible new, technologically generated sources of justification. A prominent example is the use of simulations to support scientific hypotheses, e.g., about climate change. Intuitively, computeraided simulations do not provide observational data, like radio telescopes; nor do they involve approximation techniques that could, in principle or to a limited extent, be applied by human researchers. Instead, simulations are partly based on the theories and models used to construct them; but they also offer new insight into, and possibly evidence about, complex phenomena like turbulence [Winsberg, 2001; 2003]. Yet since simulation techniques occupy some middle ground between theory and observation, whatever evidence they offer is of an ill-understood type. More attention to the mathematics and technology of simulations is needed to clarify their epistemological status. The present neglect of simulation techniques in the philosophy of science jars with their increasing importance in all kinds of sciences. The grounding of technological knowledge In the philosophy of technology, studies of the relation between science and technology have been dominated by the applied-science debate. One unfortunate consequence of this domination, noted above, is that the notion of “technological rule” has become firmly associated with the thesis that engineers merely apply scientific knowledge. This has screened off this notion from further development. It has also precluded the development of alternative models of the relation between science and technology — models that might incorporate the fact that engineers frequently do apply scientific knowledge, or are at least trained in understanding and applying theories like thermodynamics and classical mechanics. Outside of philosophy, however, interest in such models continues. To give one example, in The Gifts of Athena [2002], Joel Mokyr seeks to explain the sustained economic growth since the Industrial Revolution — a project that is squarely outside philosophy. However, the basis of his explanation is that science and engineering have since the early 19th century undergone an unprecedented period of mutually re-inforcing progress. To develop this explanation, Mokyr uses both evolutionary terminology, which need not concern us here, and an epistemological model. In this model, he distinguishes two types of knowledge, in a way that is reminiscent of both Ryle and Bunge: knowledge can be propositional or prescriptive, where the former involves any proposition, and the latter both rules and skills. Moreover, prescriptive knowledge can be grounded in propositional knowledge, either minimally (we support adding some old leavened dough to fresh dough because we know that this procedure has successfully produced leavened bread in the past) or more elaborately (we know that there are starter cultures of yeast in the old dough, which cause fermentation). Mokyr’s hypothesis is that the Industrial Revolution came about when more prescriptive knowledge was grounded in

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an elaborate way, leading to new techniques (e.g., the isolation of pure cultures of yeast), which in turn led to new scientific developments, which allowed additional grounding of techniques, etc. Let us assume that this model fulfills a real need in its field. Then, a closer philosophical analysis of technological knowledge contributes directly to non-philosophical aims. For Mokyr’s model does not analyze the grounding relation in detail. Moreover, it suffers from overly tolerant definitions of both propositional and prescriptive knowledge, making the problem of their relation almost into an artefact of the classification.37 Because of these unclarities and idiosyncracies, the model is vulnerable to the objection that it merely revives the applied-science thesis, and that it is based on epistemologically problematic distinctions, such as Ryle’s. This should not lead epistemologists and philosophers of science and technology to ignore models such as Mokyr’s. Instead, I think these models show that there is a need to develop a realistic, epistemologically refined analysis of the grounding relation — and of the ways in which technological knowledge and rules may not be grounded in scientific knowledge. This analysis should not be grafted on either the applied-science thesis or the epistemic-emancipation ideal. The previous section may have shown that these influences are hard to avoid. Yet this does not make the analysis any less needed: it only makes it more of a philosophical challenge. ACKNOWLEDGMENTS Research by Wybo Houkes was supported by the Netherlands Organization for Scientific Research (NWO). BIBLIOGRAPHY [Arora, 1996] A. Arora. Contracting for tacit knowledge. Journal of Development Economics, 50, 233—256, 1996. [Audi, 2002] R. Audi. Epistemology: A Contemporary Introduction. 2nd ed., Routledge, 2002. [Balconi, 2002] M. Balconi. Tacitness, codification of technological knowledge and the organization of industry. Research Policy, 31, 357—379, 2002. [Baumard, 1999] P. Baumard. Tacit Knowledge in Organizations. Sage, 1999. [Berry, 1987] D. C. Berry. The problem of implicit knowledge. Expert Systems, 4, 144—151, 1987. [Broens and de Vries, 2003] R. C. J. A. M. Broens and M. J. de Vries. Classifying technological knowledge for presentation to mechanical engineering designers. Design Studies, 24: 457—471, 2003. [Bunge, 1966] M. Bunge. Technology as applied science. Technology and Culture, 7, 329—347, 1966. [Bunge, 1967] M. Bunge. Scientific Research II: The Search for Truth. Springer, 1967. [Choo, 1998] C. W. Choo. The Knowing Organization. Oxford University Press, 1998. [Collins, 1973] H. M. Collins. The TEA set: Tacit knowledge and scientific networks. Science Studies 4: 165—186, 1973. [Collins, 1982] H. M. Collins. Tacit knowledge and scientific networks. In: Science in Context: Readings in the Sociology of Science, B. Barnes and D. Edge, eds., MIT, 1982. 37 Mokyr admits that his model might not stand up to critical analysis, which only makes the need for such an analysis more apparent.


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TACIT KNOWLEDGE AND ENGINEERING DESIGN Paul Nightingale

1

INTRODUCTION

In 1752 a French official lamented the slow diffusion of technology from England during the first industrial revolution by noting that “the arts never pass by writing from one country to another, eye and practice alone can train men in these activities” [Harris, 1988, p. 42]. In doing so, he was emphasising the importance of un-codified, person-embodied tacit knowledge to the engineering arts. This short chapter briefly reviews the role of this tacit knowledge in engineering design, highlighting a series of issues of importance to the philosophy of technology. The chapter aims to show how tacit knowledge as a concept is used: firstly, as an empirical description of knowledge that is impossible or difficult to articulate and codify; secondly, to explain phenomena not accounted for in other ways of thinking about engineering design; and, lastly, as a way of thinking about engineering design that is linked to broader and potentially more interesting concepts within the philosophy of technology. Understanding what tacit knowledge is, and particularly how the concept is used, is important for philosophers of technology because it is now a central concept in policy discussions related to engineering. It is used to explain why knowledge production is localised, cumulative and path-dependent, and therefore why designers, design teams, firms and regions differ in their technological performance. Given the impact of public policy related to the ‘knowledge economy’ there is a legitimate role for philosophers of technology to investigate the foundations of these ideas in more detail. This is particularly important because the terminology of tacit knowledge is applied very widely, but is rarely explicitly explained [Tsoukas, 2003]. Just what tacit knowledge is, and how it is valuable during the development of technology, is often itself a ‘tacit’ concept. This is unfortunate, because, as this chapter will argue, while tacit knowledge is a useful empirical descriptor, it is probably too broadly defined to carry the theoretical weight thrust upon it. All the same, the concept usefully points to interesting problems with the dominant conception of technology within modern culture. The remaining part of this introduction defines engineering, while section two explores what tacit knowledge is and how it is used to explain technological change in the social sciences. Section three proposes an alternative way of thinking about Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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tacit knowledge that is argued to be more in tune with its philosophical origins. This is used to explore the process of design. Finally the conclusion points to some of the strengths and weaknesses of the concept of tacit knowledge. Within this chapter engineering is defined as the art of organising and negotiating the design, production, operation and decommissioning of artefacts, devices, systems and processes that fulfil useful functions by transforming the world to solve recognised problems. This hopefully highlights the practical, creative nature of engineering, with a clear connection to judgements and choices about solutions that achieve a balance between potentially conflicting outcomes in terms of their aesthetic, economic, environmental, technical and other criteria [Tang and Leifer 1988; Sch¨ on, 1982; Bucciarelli, 1994]. For an elaborate account of how to define technology and the engineering sciences, see Mitcham and Schatzberg’s chapter in Part I of this Volume. The emphasis on organisation differentiates engineering from other tasks in the production of artefacts [Vincenti, 1990, p.7]. During this production process designing is only one among many roles played by engineers. While design may be one of the most glamorous of engineers’ roles, and an emphasis on creativity helps legitimise engineers as professionals, their other tasks remain important even if they are not addressed in this chapter. This definition is similar to, but slightly more comprehensive than, Dym’s in which “Engineering is a systematic, intelligent process in which designers generate, evaluate, and specify concepts for devices, systems or processes whose form and function achieve clients’ objectives or users’ needs while satisfying a specified set of constraints” [1993, p.17]. It is also similar to G. F. C. Rogers’ definition of engineering as “the practice of organising the design and construction of any artefact which transforms the physical world around it to meet some recognised need” (quoted in [Vincenti, 1990, p.5]). Within all three definitions is a shared focus on a temporal process of creating solutions to problems, assessing and selecting them and bringing them to fruition in order that they might effect some change. As such, these definitions reflect academic interests, and may differ from practitioners’ perceptions or the reality of engineers’ day to day activities identified in ethnographic studies [Jagodizinski et al., 2000]. The specific concern in this chapter is design — widely seen as a central core of engineering practice — which refers to both the content of a set of plans and the process that produced those plans [Vincenti, 1990, p.7]. For Herbert Simon, design simply involves “changing existing situations into preferred ones” [1969, p.111] which blurs the distinction between designing and building a technology. However, the concern in this chapter is specifically with engineering design which Ferguson [1977; 1978; 1993] highlights is differentiated from artisan design by its use of drawings that now mediate the previously direct link between the artisan’s mind and the materials they are working with. This introduction of visual diagrams has had profound implications for engineering design and has led to new kinds of visual thinking, new tools, new forms of communication, and a greater division of labour between the people who design

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and the people who build technology [Ferguson, 1993; Arnheim, 2004]. Once diagrams had opened up a space between designers’ minds and their artefacts, many more processes of design became possible. In particular, technology itself could be more easily applied to design to help formulate, analyse, communicate and test designs. As Constant [1980] argues, what distinguishes modern engineering from the 15th century engineering of Filippo Brunelleschi is the development of regimes of testing that further intermediate between a designer’s mind and the final product. In craft production, improvements in technology occur slowly and in a haphazard fashion, while with modern engineering the specialisation and professionalisation of testing allows a faster, more accurate and much more public comparison of alternatives [Constant, 1980, p.23]. Two changes were vital here: first, the emergence of specialised academic engineering science, such as chemical and electronic engineering, in the early 20th century. These new academic disciplines engaged in research that generated new theories, frameworks, data, tools and particularly a new generation of professionally trained engineers who were able to use new testing technologies [Rosenberg, 1998]. The second important change was the development and widespread use of testing-technology that was often provided as a service by the new engineering consultancies that emerged at the turn of the 20th century. While this might appear at first as a simple Weberian shift from local, tacit knowledge to more global, scientific, visual and articulated technology, the rest of the chapter will argue that such changes have not been so simple. 2

TACIT KNOWLEDGE: FROM THE MARGINS TO THE MAINSTREAM

The notion that tacit knowledge was something more important than just unarticulated elements of conversations appears in the work of the Hungarian doctor and chemist Michael Polanyi (1891–1976). Polanyi moved into the philosophy of science in response to the dominance of positivism, and in particular the potentially totalitarian dangers that he saw in its legitimisation of the centralised control of science. In doing so, he drew on his experience of hands-on experimentation in physical chemistry to argue against conceptions of knowledge that saw it as abstract, mechanical, deterministic and therefore possible to centrally plan. Instead Polanyi stressed how all knowledge is centred on an agent and her body that is constantly interacting with the world [Polanyi, 1969, p.147]. This interaction, including the use of words and symbols, requires creativity, skill, imagination and personal knowledge. These are essential to our ability to learn through unconscious trial and error when we “feel our way to success” [Polanyi, 1958, p.62]. More importantly, he suggests that our conscious actions are dependent on creative, preconscious processes of integration that produce new emergent cognitive phenomena that were not previously present in its components. Consequently, our knowledge is more than the sum of its parts and, while it can be described by rules, it cannot be reduced to rules, with the implication that “we know more than we can say” [Polanyi, 1969; Nisbett and Wilson, 1977].

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To explain these ideas Polanyi used an example of the new stereo-image formed by looking at two stereoscopic photographs with different eyes. He argues we become focally aware of this stereo-image by being subsidiarily aware of the two separate pictures. Subsidiary awareness functions by bearing on the focus of our attention and making us conscious of merged meanings. Tacit knowing therefore involves a process of integration rather than a [reversible] inference or deduction: it is “knowing a focal object by attending subsidiarily to the clues that bear on it” and this knowledge is lost by focusing on clues in isolation [Polanyi, 1965, p.799]. All aspects of knowing, for Polanyi, share this anti-reductionist character and are based on bodily interactions and creativity. For Polanyi, this applies as much to tools as it does to ideas and concepts [Polanyi, 1969, p.148; 1968]. While tool-users initially have to focus their attention on their tools, after a period of practice they develop the subsidiary awareness that allows them to use the tools with skill. Focusing on particular features of our experience, such as turning when cycling or on the hammer when hammering, brings them out of subsidiary awareness into focal awareness. This isolates them from our wider tacit understanding and destroys the coherence and meaning of our actions [Polanyi, 1966a, p.10]. This is why focusing on words when speaking, or finger movements when playing the piano, disrupts the flow of these actions [Polanyi, 1969, p.144]. As a consequence, description of comprehensive entities based only on their parts, or on the laws of nature which apply to their parts, can never reveal the operation of the higher principles that define what they are [Polanyi, 1965; 1968]. Polanyi [1965, p.799] argues that: to go back to the premises of a tacit inference brings about its reversal. It is not to retrace our steps, but to efface them. Suppose we take out the stereo-pictures from the viewer and look at them with both eyes. All the effects of the integration are cancelled; the two pictures no longer function as clues, their joint meaning has vanished. Because such tacit knowledge is holistic and non-reducible it cannot be simply built up from components or learnt by following rules [Polanyi, 1966a; 1968]. Polanyi [1966b; 1967; 1969] therefore places great emphasis on what he calls ‘indwelling’ for comprehension and learning. When we learn, we have to dwell within the concepts we are using for a period of time until they move into subsidiary awareness. This enables us to creatively see the broader coherence of what we are studying and appreciate that body of knowledge as a whole. This can be seen in apprenticeships where students must initially take everything on trust and follow examples until they build up the knowledge needed to understand the activity as a coherent whole. As Polanyi put it: An art that cannot be specified in detail cannot be transmitted by prescription, since no prescription for it exists. It can be passed on only by example from master to apprentice. This restricts the range of diffusion to that of personal contacts, and we find accordingly that


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craftsmanship tends to survive in closely circumscribed local settings. [1958, p. 52] Using more modern terminology, we might say that rules and descriptions of how to perform actions are imposed from the outside, rather than being intrinsic to the actions. As a consequence, they can never fully transmit knowledge without the mediation of a background of cognitive dispositions [Searle, 1995]. As a result, “all knowledge is either tacit or rooted in tacit knowledge. A wholly explicit knowledge is unthinkable” [Polanyi, 1969, p.144].

2.1

Cognitive and social scientists on tacit knowledge

While empirical observations about the difficulty of transmitting some kinds of knowledge may seem trivial, Polanyi argues that they show the implausibility of ‘objective’ knowledge that is detached from human action and of various theories built on such ideas [Polanyi, 1962; 1969]. Given that explaining how such knowledge is possible has been a central focus of the philosophy of science, Polanyi has had an important, if not always positive, influence on a number of philosophers of science such as Feyerabend, Lakatos, and Agassi. More recently, Searle [1995] has argued that a range of implicit cognitive dispositions, much like tacit knowledge, that he terms the Background provides structure to our thoughts and actions and prevents them from being reducible to rules. Similar ideas have been important in critical attacks on the largely over-inflated claims of proponents of Artificial Intelligence [Dreyfus and Dreyfus, 1986; Collins, 1974; 1990; 2001]. Given how positivist ideas about knowledge have been foundational to many social sciences such as psychology and economics, the concept of tacit knowledge would seem to have the potential to be widely applied [Gill, 2000; Lakoff, 1987]. However, within psychology it is not widely used [Reber, 1989; 1993; Marcel, 1983] and is often considered to be too broad to be analytically useful. It does, however, help explain implicit learning, for example, how experimental subjects learn to anticipate electric shocks without being able to articulate what triggered them and types of knowledge that can only be recalled by doing [Lazarus and McCleary, 1949; Reber, 1989; Underwood, 1996; Lewicki and Czyzewska, 1992; Schacter, 1992]. A considerable amount of empirical work supports Polanyi’s view that much of our learning and problem-solving ability is tacit [Sternberg, 1986; Lihlstrom, 1987; Reber, 1989; Dixon, 1971; Merikle, 1992; Berry, 1994; 1997; and Buckner, 1995] as well as his assertions about the roles of cognitive gestalts in structuring perception [Pylyshyn, 1981]. These allow parts of an image to be seen as a whole (as when we recognise a face) even though our eyes only focus on one bit at a time [Reber, 1989]. Recent advances in genetics and neurology seem to support Reber’s [1989] conjecture that tacit knowledge is an older, more primitive form of ‘knowledge’ that supports later evolutionary developments like consciousness and language [Damasio, 1994; 2000]. Much of our cognition is tacit in the sense of not being accessible

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by the mind, and conscious thought is dependent on neural systems that either cannot be, or are not, part of consciousness. These neural systems generate images of you changing in response to an object, allowing you to feel changes produced by external objects as a subjective, inner, qualitative state [Damasio, 2000]. This seems to fit Polanyi’s account — “I shall say that we observe external objects by being subsidiarily aware of the impact they make on our body and of the responses our body makes to them” — very well [1965, p. 805]. Such neural systems allow images to be brought from subsidiary awareness to focal awareness [Posner, 1994] enabling concentrated attention that can be linked to memory and categorisation to allow learning from errors (see Tononi and Edelman, [1999] for a mechanism). Brain imaging technology has shown that as we learn neural images are gradually moved to areas of the brain that cannot be accessed by consciousness. This functional isolation produces a “gain in speed and precision, but a loss in context-sensitivity, accessibility, and flexibility” [Tononi and Edelman, 1998, p. 1847] and makes expert knowledge generated by repeated practice difficult to articulate. Beyond the cognitive sciences there is also a substantial literature on tacit knowledge that begins to address technology. Again it plays a supporting role for heterodox approaches that contest more positivistic paradigms. For example, tacit knowledge has been a central idea for many years within the heterodox economics literature that places emphasis on technological learning [Nelson and Winter, 1982; Freeman, 1982]. Nelson and Winter [1982, p. 77-79] for example, in a very influential work, highlight the importance of procedural tacit skills in the design and development of technology, and the consequent difficulties involved in creating, diffusing and using technology. In doing so, they build on a body of work by writers on engineering, such as [Constant, 1980, p. 22-27; 2000; Court et al., 1997; Donovan, 1986; Ferguson, 1977; Gille, 1986, p. 1156-61; Stapleton et al., 2005] and [Rogers, 1983] who have reflected on empirical examples of the tacit nature of engineering knowledge, with [Vincenti, 1990] as the seminal work on engineering knowledge. Because engineering knowledge is partly tacit, it tends to be private [Dosi, 1988, p. 242] and mainly transmitted through face-to-face interaction [Leonard-Barton, 1995; Leonard and Sensiper, 1998]. Its specificity to particular technologies and environments enables firms to develop capabilities that differentiate them from their peers [Pavitt, 1986; 1996; Freeman, 1982; Nelson and Winter, 1982; Pavitt, 1984, p. 343; Dosi et al., 1989; Nelson, 1991; Dosi, 1988, p. 224]. Since these capabilities are associated with improved performance, tacit knowledge is a central focus of the organisational learning literature [Argyris and Schon, 1974; Tsoukas, 1996; Spender, 1995; 1998; Lam, 2000]. Professional organisations, such as engineering design offices, are particularly dependent on the accumulation of tacit knowledge [Becher, 1999; Howells, 1996; Benner, 1984; Eraut, 1999; Megginson, 1996; Veshosky, 1998]. Schön [1982] has highlighted that professional learning involves building up tacit knowledge through critical reflection on actions. This, he argues, makes the practice of design in-


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herently interactive. These ideas have been influential within the management literature, which has sought to understand how tacit knowledge can be built up and used for economic advantage [Teece, 2000; Dougherty, 1992; Leonard-Barton, 1995; Leonard and Sensiper, 1998; Tsoukas, 2003; Brown and Duguid, 2000; Kogut and Zander, 1992]. These ideas have also been applied at the regional and national level within the economic geography literature where the difficulties of transmitting tacit knowledge, and its importance to technological development, are used to explain regional diversity and the geographic clustering of industries [Pavitt, 1996; Asheim and Gertler, 2005; Gertler, 2003; Howells, 2002; Lawson and Lorenz, 1999; Audretsch and Feldman, 1996; Maskell and Malmberg, 1999]. As tacit knowledge became an important concept within economics, management and geography, more critical voices began to emerge that questioned its empirical and theoretical value. Tsoukas [2003] is supportive of the analytical value of tacit knowledge, but suggests that the notion (prominent in the knowledge management literature) that tacit knowledge can be codified misunderstands what tacit knowledge is. As Tsoukas [2003; 416] noted, “tacit and explicit knowledge are not the two ends of a continuum but the two sides of the same coin: even the most explicit kind of knowledge is underlain by tacit knowledge.” Breschi and Lissoni [2001] similarly argued that just because tacit knowledge can explain regional agglomeration it does not follow that it is in fact the correct explanation. Cowan et al. [2000] expressed extreme scepticism that tacit knowledge was a strong enough concept to explain every deviation from the predictions of neo-classical theory in economics, while Nightingale [2003] likened tacit knowledge to physicists’ “dark matter” that explains away the empirical failures of existing theory, but is rarely critically explored. These criticisms suggest that tacit knowledge has been useful for highlighting the empirical failures of social sciences that build on objectivist conceptions of knowledge, such as neo-classical theory in economics, but the idea itself covers a range of distinct features of cognition that are probably better kept distinct. Even within the literature just reviewed, tacit knowledge covers the embodied nature of knowledge; unconscious knowledge; implicit learning; subsidiary (and focal) knowledge; knowledge that is simply unsaid; knowledge that can never be articulated; and gestalts that structure cognition. Similarly, neurologists distinguish between neural mechanisms; neural mechanisms that produce neural images; neural images that can be potentially brought to conscious attention, i.e. preconscious or potentially conscious mental images; and mental images that are currently being consciously attended to. Being such a broad concept, tacit knowledge has tended to be used as the name for empirical counter-examples to theories of learning or technical change that reduce knowledge to easily transmittable information. This, however, does not exhaust Polanyi’s ideas and potentially, as the next section will argue, overlooks a more insightful side of Polanyi’s thought.

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3 AN ALTERNATIVE VIEW OF TACIT KNOWLEDGE From the perspective in this chapter on engineering design, tacit knowledge is interesting because Polanyi suggests it is a component of technology, rather than just a kind of knowledge needed to create technology. In much of the social science literature just reviewed, technology and tacit knowledge are distinct and one (tacit knowledge) plays a role in the development of the other. However, for Polanyi tacit knowledge is part of technology in the sense that the function of a technology (which is what a technology is) is realised through a process of tacit inference and, like a stereoscopic image, ceases to be what it is in the absence of tacit knowledge. Being a technology is an imposed rather than intrinsic property [Searle, 1995]. Polanyi writes [1958, p. 52] in a quote picked up by Nelson and Winter [1982, p. 119] that “even in modern industries the indefinable knowledge is still an essential part of technology. I have myself watched in Hungary a new, imported machine for blowing electric lamp bulbs, the exact counterpart of which was operating successfully in Germany, failing for a whole year to produce a single flawless bulb.” In this quote Polanyi says that indefinable knowledge is “an essential part of technology” rather than “is needed to get technology to work”. While we must be cautious of taking phrases out of context, seeing tacit knowledge as part of technology, in the strong ontological sense that tacit knowledge makes technologies what they are, fits with Polanyi’s non-reductionist view of the world and his emphasis on creativity. This is more than the weak epistemological sense in which tacit instrumental knowledge is just needed to get technologies to function. As a chemist Polanyi understood the inherent implausibility of reductionism [1965; 1968], more recently, see [Dupré, 1993]. For chemists, reductionism is misleading because many qualities exist within chemistry that cannot be reduced to, let alone explained by, the behaviour of their component parts. This is why you cannot explain why Gold (the metal) is gold (the colour) or why mercury is a liquid using only quantum mechanics [Scerri and McIntyre, 1997; Dupré, 1993]. Such emergent phenomena do not contradict the laws of nature [Barrow, 1988], but exist within Polanyi’s [1965] “boundary conditions” of potential behaviour that is consistent with those laws. Tacit knowledge adds something to artefacts in the ontological sense because in some instances these boundary conditions can be governed from above: the possibilities opened up by the rules of chess, for example, can be controlled by the strategies of the players. Similarly, the laws of mechanics may be controlled by the operational principles of a machine which are imposed by designers and are not reducible to the machine’s components. These higher principles make technologies what they are, and are distinct from the lower principles which remain in operation even if the machine is smashed up. This again highlights Polanyi’s point that comprehensive entities, in this case technologies, are more than the sum of their parts. For Polanyi, the property of being a technology, like the property of being a beautiful painting, is not purely intrinsic. It reflects, in part, a coherence the


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viewer imposes on an object. Just as paintings are more than blobs of paint, so technologies are more than their components. For Polanyi these additional features are created through a process of tacit inference generated by indwelling. The same ideas are used by Polanyi to explain why science is inherently creative: because complex entities cannot be reduced to their parts, scientists have to dwell within their subjects to build up understanding and creatively come up with theories that explain them. With technology, however, designers don’t just understand features of the world that cannot be reduced to lower order principles — they actively create those features. They create new solutions to problems through a process of tacit inference and then change the world to impose those solutions on technological artefacts to create new behaviour that is not reducible to its components. The idea that higher operational principles, imposed by designers, define what technologies are, is similar to ideas presented more recently by Searle [1995], Kroes and Meijers [2006] and Vermaas and Houkes [2003; 2006]. For Searle [1995, p. 19] technologies have an intrinsic physics — that appropriates Polanyi’s boundary conditions — and an imposed function that determines how the technology should behave (i.e. drugs should cure diseases and umbrellas should keep you dry) — that approximates Polanyi’s operational principle. This imposed function is ontologically prior to the intrinsic physics and determines what a technology is [Searle, 1995, p. 19]. This is why a safety valve is still a safety valve with the function of stopping explosions, even if it malfunctions and fails to do so [ibid ]. Because technical functions are not intrinsic, technologies can have multiple functions — which is why a computer disc can both store data and stop a coffee cup marking the table. However, the range of possible functions of a given technology is constrained, as a technology’s physics has to be able to match its imposed function. If the epistemic idea that tacit knowledge is needed to get technology to work is the first step away from just seeing technology as artefacts and the Searlean idea that imposed functions are ontologically prior to technologies’ intrinsic properties is the second step, then Polanyi makes a further much more controversial step. Polanyi suggests that technologies’ intrinsic properties come to embody imposed higher order principles that are generated by tacit inference. Presumably for Searle engineers would understand a function and impose it on the world by changing the world until the technology’s intrinsic properties matched the desired function. As a simple theory, this has much to recommend it, but from Polanyi’s perspective it doesn’t address his concerns about reductionism and would work in a world where reductionism was true. For example, in a world where technological artefacts could be reduced to their component parts, knowledge of those components and their interactions would be sufficient to generate a desired function. Polanyi’s position is more contentious and suggests that because reductionism doesn’t hold, the function of the artefact isn’t implicit in the functions of its components. Instead, higher order boundary conditions define the function and have to be creatively developed through a process of tacit inference. Once the world is changed to match this function, the tacitly created boundary conditions become embodied in the technology. In more Searlean language, the

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intrinsic properties of the technology are modified to match an imposed function that is not implicit in the intrinsic properties of its components. The technology therefore comes to embody tacitly created boundary conditions. While dollar bills function as money because society accepts the institutional fact of their value [Searle, 1995], technologies embody tacitly created functions in their physical make up. The resulting behaviour, unlike being money, can continue even if society stops believing in it. For example, currencies become worthless when societies stop trusting them, but an unmanned space probe sent out from earth continues to behave in ways that match an imposed function even when it is out of sight. If in millions of years time, long after the earth has been engulfed by the sun, the probe was found by an alien anthropologist, they might decipher something about our culture from its behaviour because part of our culture is embodied in what the thing actually is.

3.1 The difference between science and technology Polanyi is particularly interesting to those concerned about engineering design because he extends his ideas about tacit knowledge, the imposed nature of functions, and the irreducibility of comprehensive entities to draw out the differences between science and technology. He writes [1958, p. 177]: [T]he beauty of an invention differs . . . from the beauty of a scientific discovery. Originality is appreciated in both, but in science originality lies in the power of seeing more deeply than others into the nature of things [i.e. the non-reducible emergent order in chemistry that cannot be reduced to physics, yet is not incompatible with it], while in technology it consists in the ingenuity of the artificer in turning known facts to a surprising advantage. The . . . technician . . . follows the intimations, not of a natural order, but of a possibility for making things work in a new way for an acceptable purpose, and cheaply enough to show a profit. In feeling his way towards new problems, in collecting clues and pondering perspectives, the technologist must keep in mind a whole panorama of advantages and disadvantages which the scientist ignores. He must be keenly susceptible to people’s wants and able to assess the price at which they would be prepared to satisfy them. A passionate interest in such momentary constellations is foreign to the scientist, whose eye is fixed on the inner law of nature. As this passage shows, when it comes to science Polanyi is a realist and for him scientific theories and explanations are meant to be true. However, when it comes to technology, to use anachronistic terminology, Polanyi is much more of a constructivist [Polanyi, 1967; 1969]. This is because technologies are meant to be useful and usefulness reflects inherently subjective, time-dependent assessments of value. As a consequence, the particular trade-offs made during design are entirely alien to his (very purist) view of science. Moreover, they give design a particular


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cognitive element not found in science that helps distinguish the philosophy of technology from the philosophy of science. Today such a clear cut separation between science and technology overlooks the role of design in experimental sciences, in both the design of experiments and the design of experimental apparatus, and also the increasing role played by scientific knowledge in design processes. The idea that science and technology are distinct but closely interacting finds support in the work of scholars of technology such as Pavitt [1998] and Layton [1974; 1976] who distinguish technology from science because technical behaviour has to be (1) reliably created, (2) for users, and (3) in the complexity of the outside world, rather than in the atypical purified conditions of the laboratory as a one-off, largely private, and not necessarily reliable phenomenon. This means that engineers (defined as professionals who are held legally responsible for producing products that are ‘fit for use’) have to understand the environment in which products are used [Parnas, 1999, p. 3]. This differentiates them from scientists and is why engineers focus on what works reliably rather than on new knowledge, require a broad understanding of how their products will be used, and normally rely on a legal process of accreditation, based on an established and formalised body of knowledge, to ensure the quality of their work, unlike scientists who need to be up-to-date with the latest findings in their field, can be narrow in their specialisation and can let external referees determine the quality of their work [Layton, 1979, p. 77–78; 1976; Parnas, 1999]. As Pavitt notes [1998, p. 795] this creates important differences between the purposes of science and technology and the nature of the knowledge they generate: One of the main purposes of academic research is to produce codified theories and models that explain and predict natural reality. To achieve analytical tractability, this requires simplification and reduction of the number of variables (e.g., ‘Under laboratory conditions . . .’, ‘Other things being equal . . .’). On the other hand, the main purpose of business R&D is to design and develop producible and useful artefacts. These are often complex, involving numerous components, materials, performance constraints and interactions, and are therefore analytically intractable (i.e. theory and formal models are an insufficient guide to, and predictor of, practice). Knowledge is therefore accumulated through trial and error. These differences, in turn, relate to the nature and location of the knowledge production processes: Academic research is mainly basic research; business research is mainly the development and testing of prototypes and pilot plants. Academic institutions dominate in the publication of scientific papers, and business firms in the granting of patents. And despite examples of spectacularly close links between basic research and technology (i.e. biotechnology), basic research builds mainly on basic research (scientific papers cite other scientific papers much more frequently than patents)

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and technology builds mainly on technology (e.g., patents cite other patents much more frequently than scientific papers). [Pavitt, 1998, p. 795] Polanyi’s conceptual framework of a non-reductionist view of nature, an emphasis on tacit inference and creativity in generating both new scientific theories and new technologies, and the corresponding emphasis on in-dwelling, have important implications for understanding engineering design that are much deeper than a simple empirical observation that some knowledge used in engineering cannot be easily articulated. Dividing design processes into understanding problems, formulating solutions and testing, provides a way to explore how some of the existing history and philosophy of technology relates to Polanyi’s ideas.

3.2 The process of design: understanding problems and negotiating solutions Focusing first on framing problems, most design — even for simple technologies — involves very complex and often conflicting demands that have to be negotiated and clarified: a process that has been nicely illuminated within the history and sociology of technology literatures [Nye, 2006; Hughes, 2004]. These multiple and potentially conflicting demands form part of designers’ subsidiary awareness and are often unstated. For example, if I was asked to design a hammer, and produced one made from the horn of the last black rhino calf, there is a very real sense in which I did not understand what was intended, even though at an explicit level my response perfectly matches the requirements. More importantly, the unstated background assumptions are not fixed and change as engineers creatively merge conflicting and often open-ended requirements. This often involves understanding the wider impacts of their proposed solutions. Gardiner and Rothwell [1990; Rothwell and Gardiner, 1988] for example, highlight the importance of considering manufacturability in early design, and how sharing components within a family of designs can simplify production and generate economies of scope. Rothwell [1992] found that the ability to consider these factors, while also paying attention to consumers’ needs (which may not be the same as what they think they need) is a vital part of successful design-led innovation. Formulating design problems is therefore open-ended and cannot be reduced to simple rule following [Dym, 2000, p. 17]. It requires the integration of knowledge, as judgements have to be made about which problems to address and what relative weights to give to conflicting demands [Hacker, 1997]. Many of these multiple criteria will typically have to be considered, merged and explored during the design process. The difficulties of sharing tacit understanding of problems and the uncertainties associated with their exploration make design a negotiated process rather than a simple creative event [Burcarelli, 1994]. Designers will have subsidiary awareness of many of these issues and bring them in and out of focal awareness as they explore different design options and make explicit their concerns to other members of the design team [Henderson, 1999]. This makes design more


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complex than simply recognising a problem, matching a solution to that problem, and creating that solution. There is ample empirical evidence that this process involves knowledge that is difficult to articulate [Vincenti, 1990]. However, Polanyi’s framework suggests tacit knowledge plays a role in structuring the design process. Because “being a problem” is an imposed rather than intrinsic property, it is understood contextually, which for Polanyi involves a process of tacit inference. For Polanyi, technologies’ coherence is understood through “indwelling”, as when Vincenti’s [1990] aircraft designers had to get into aeroplanes and sit on pilots’ laps because they had been unable to understand pilots’ experience of stability without experiencing it as a coherent whole. This knowledge was something that could not be reduced to information. This may help to explain Cross’ [2004, p. 432] findings in his review of studies of design choices which show that experienced designers often approach design tasks through ‘solution conjectures, rather than through problem analysis’. Rather than working through the problem in great detail to generate a solution, they use their experience to conjecture design solutions that might work and then try them, using the results of their experiments to better understand the problem they are faced with and how potential solutions might address it. In doing so they select particular features of the problem to attend to and identify potential solutions that they wish to explore. This “imposes on the situation a coherence that guides subsequent moves” [Cross, 2004, p. 423]. Because design choices are open-ended, designers have a degree of choice in how problems are framed, and expert designers have been observed to deliberately define problems in difficult and challenging ways [Cross and Clayburn, 1998; Ho, 2001]. Given the inherent uncertainty of design implicit in Polanyi’s non-reductionist ontology, and his emphasis on indwelling, it does not seem surprising that expert designers might proceed in this solutionled trial and error way. Or rather, it would be surprising if they only approached design through the analysis of problems, as by breaking comprehensive entities into parts, analysis loses the imposed coherence that designers are trying to impose.

3.3

Generating solutions

During their training, engineers pick up an understanding of various design options and a contextual understanding of when and where tried and tested solutions can be applied [Nightingale, 1998]. Despite substantial investments, these choices have not been reduced to technical rule-following. Partly, this is because engineers rely on what Vincenti [1990] calls Fundamental Design Concepts that sit in the back of designers’ minds and are implicit in their design choices. The first of these are operational principles that show how the components of a design will “fulfil their special function in combining to an overall operation which achieves the purpose of the device” [Vincenti, 1990, p. 208; Polanyi, 1958, p. 328]. A classic example of such an operational principle would be Sir George Cayley’s definition of the operational principle of an aeroplane involving making “a surface support a given

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weight by the application of power to the resistance of air” [Vincenti, 1990, p. 9]. Once designers have this idea in the back of their mind they no longer have to consider creating aircraft that flap their wings. Polanyi argued that these operational principles define what technologies are and exist outside scientific knowledge. As a result, “the complete [scientific] knowledge of a machine as an object tells us nothing about it as a machine” [Polanyi, 1958, p. 330]. This is supported by Vincenti who notes that operational principles “originate outside the body of scientific knowledge and come into being to serve innately technological purposes. The laws of physics may be used to analyze such things as airfoils, propellers and rivets once their operational principles have been devised, and they may even help in devising it; they in no way, however, contain or by themselves imply the principle” [1990, p. 209]. Scientific knowledge can explain why a particular solution produces the result it does, but, because imposed functions are linked to the intentional plans of technologies’ designers, scientific knowledge that is divorced or unconnected to these plans will not provide those solutions [Nightingale, 1998]. Vermaas and Houkes [2006, p. 16] make a similar point when they highlight how “technological functions ... create a conceptual bridge between the intentional and structural natures of artefacts; function ascriptions connect the intentional description of the use plan [what the technology will do] with a physical description of the artefacts themselves via the physical capacities of the artefacts that explain why this plan is effective”. When scientific theories are used to understand technology they can help explain why a particular design produces the effects it is intended to. However, they cannot explain why those particular effects were intended in the first place. Vincenti’s [1990] second fundamental design concept is the normal configuration of a device which refers to the general arrangement of components that allows artefacts to generate their operational principle [1990, p. 209, 102–110]. Car designers, for example, will be able to draw on a paradigm case of a car with four wheels, a front-mounted, water-cooled, petrol-driven engine, and four doors [Vincenti, 1990, p. 209]. Again, such concepts are implicit and rarely articulated during design. These fundamental design concepts define the structure and direction of the problem-solving process by addressing certain key problems, while leaving a penumbra of flexibility to address the wide variety of other design issues that arise. In doing so, their application re-defines the design problem and makes it more specific, setting up the conditions for the next round of design. The iterative application of operational principles can therefore generate a hierarchy of structurally related, increasingly specific sub-problems that form the basis for the design process [Nightingale, 1998; 2000]. Vincenti [1990, p. 9] nicely highlights this process in which design moves from very general problem definition that translates ill-defined problems into more concrete technical problems, after which the process shifts to overall design which provides an overarching layout of the system, then moves to the design of major components, which is then followed by further subdivision of the project (see also [Bucciarelli, 1994]).


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Testing and modification

Analysis and testing are important in modern engineering design because operational principles only provide rough guidance, and mark the first step in a long trial and error journey to construct a predictable final product. As Dupré [2001, p. 171] notes of internal combustion engines, a first approximation of the operational principle is that: a mixture of air and petrol is exploded in a cylinder, pushing a piston down the cylinder; the cylinder is connected to a shaft which is rotated by the moving piston. A number of similar cylinders are connected to this shaft, and a sequence of explosions keeps the shaft rotating continuously . . . But if, on the basis of this explanation, someone lined up some coffee cans partially filled with petrol on the kitchen floor, stuck toilet plungers in the cans, tied the ends of the plungers to a broomstick, and then posted lighted matches through the holes in the sides of the coffee cans, they would certainly not have built an internal combustion engine. Initial designs are therefore only potential solutions and as Constant [1980; 2000] has argued the mediation of regimes of testing, based around widely-used testing technologies, has transformed engineering and the ability of designers to produce complex technology. A considerable amount of modern engineering design involves working out criteria and specifications that help define how a technological system will achieve its desired function in more detail. The production of specifications involves translating very “general, qualitative goals for the device into specific, quantitative goals couched in concrete technical terms” [Vincenti, 1990, p. 211]. Typically this is a complex process involving the production of diagrams, models, mock-ups and back of the envelope calculations. These artefacts allow knowledge to be shared between the various actors involved in design, and the negotiation (or not) of conflicts within the inherent trade-offs between different design choices. As such, the model or mock-up acts as a ‘boundary object’ [Henderson, 1998; 1999] to allow shared understanding of the design and design process. This helps mediate between different groups’ understanding of the design, and the validity of the ‘facts’ that make it up. Such models also play a key role in facilitating learning during design. The complexity of many engineered artefacts, together with their interactions with a changing environment, make working out the effects of many design changes either analytically intractable or analytically very difficult [Pavitt, 1984; Nightingale, 2004]. It is therefore misleading to see design as a simple linear process, particularly with multi-component systems where the appropriate design of one component is sensitive to the design of others. These interdependencies mitigate against trying to change many components at once [Nelson, 1982, p. 463]. Consequently, the design, development and production of complex artefacts involves learning, experimentation, testing, and numerous modification and feed-back loops.

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Henderson [1995; 1998; 1999] has shown in a series of wonderful case studies how sketches and models are used interactively at both the individual and group levels “to work out and negotiate various perspectives and to draw in, literally and figuratively, a wealth of tacit knowledge” [1998, p. 141]. Component designers, for example, can show production engineers their designs, who can in turn then articulate their ‘gut feelings’ why particular parts might be hard to machine, and what design changes might improve them, without having to articulate exactly why. D’Adderio [2001] similarly reflects on the very visual nature of the knowledge used in these negotiations, and the way graphical tools are used by designers to communicate with one another. While Henderson’s sociological approach focuses on social groups, her Actor Network Theory approach is consistent with seeing these models as part of a negotiation with nature, in which nature refuses to negotiate on designs that do not work. As a consequence, a lot of engineering design work involves finding out what behaviour nature finds acceptable. While it is possible to rely on purely empirical methods and unguided changes to produce improvements to designs, such approaches tend to be costly and timeconsuming. Instead, design is guided by tacit understanding and rules of thumb that are specific to local situations and technological configurations [Vincenti, 1990]. Given the complexity of most designs, the experimental processes involved in engineering design typically involve creating simplified (i.e. artificially predictable) conditions where the assumptions underpinning these local explanations are true [Nightingale, 2004]. This allows explanations that are too simple to work in the real world to be used to guide the design process. As knowledge is accumulated, the simplifying conditions can be relaxed and the design process can proceed from ‘laboratory conditions’ to models, prototypes, field tests and eventually real-world applications. This guidance (hopefully) reduces the number of experimental dead-ends and improves final designs. As this process proceeds, designers take practical considerations, such as the clearance needed for maintenance, or the idiosyncrasies of the staff that will eventually operate the technology, into account. Much of this practical knowledge is unarticulated, context-dependent and defies codification, making testing prototypes an essential part of design [Vincenti, 1990]. In carrying out this testing and modification, designers rely on shared, but unarticulated, ways of thinking and implicit models and analogies. These analogies and models — for example, thinking about the stability of an aircraft about its vertical axis as a ‘weathercock’ — are again not always easily expressible in words. They often involve a very visual form of thinking, and need to be articulated on diagrams and drawings to be worked on and transmitted [Vincenti, 1990; Henderson, 2000]. Such models are analysed to produce descriptive information about how the design will behave as well as prescriptive data about what is needed for the design to achieve its desired function. Academic and industrial engineering research has developed a series of theories, theoretical tools, mathematical methods and intellectual concepts for analysing designs. Like Polanyi’s operational principles, some of these intellectual tools are specific to engineering, for example, concepts


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like propulsive efficiency and feedback enable quantitative analysis, but are not scientific terms [Mayr, 1976, p. 882; Vincenti, 1990, p. 216; Ferguson, 1978, p. 450]. Such tools allow engineers to investigate how well designs and design options match, or mismatch, design criteria and specifications. During the process of testing artefacts and components, engineers switch between seeing technologies in functional terms as part of a wider system of use, and seeing them in terms of their intrinsic physics which can be subject to empirical analysis. In each instance, the alternative is left implicit and the new knowledge generated through testing integrated back into the process of design. Designers therefore have to reflect on their designs and the results of tests, negotiate changes to inter-dependent components, and work out prescriptive performance criteria, often using models and diagrams as tools for what Hutchins [1995] has called “external cognition”, that are modified in an attempt to capture implicit, background understanding and tacit knowledge [Henderson, 1995]. The role played by tacit knowledge in Polanyi’s thought contrasts with a strong tradition of understanding engineering design in terms of a means-ends practical reason. Simon [1969], for example, is an influential exponent of the view that design is a “science of the artificial” in which decomposable problems are analysed and fitted back together. For Polanyi design can’t be about taking problems apart and fitting them back together again because coherent entities cannot be reduced to the sum of their parts. Instead, as Sch¨ on [1982] has shown, it is inherently creative and involves interactions, practice and reflection on actions. For Polanyi, design can’t only be about adapting means to well-defined ends because those ends and means are not always at hand. They will often have to be created, and this creative process involves tacit inference. Seeing design as a clean “science of the artificial” often misses the inherently creative, messy and open-ended processes of developing and adjudicating between conflicting demands and benefits. The tacit nature of the knowledge involved in creating the novel boundary conditions that make technologies behave in particular ways cannot be reduced to a simple calculation. Reducing design to a science of design leaves un-explored the complex, creative processes used by designers, and the role of diagrams, models and visual thinking in exploring design options. 4

CONCLUSION AND SYNTHESIS

This chapter has hopefully shown that tacit knowledge is a useful, but probably over-encompassing, concept that nevertheless helps illuminate important features of engineering design. While most of the literature that uses the concept of tacit knowledge does little more than report that there are features of engineers’ knowledge that are difficult, if not impossible, to articulate, this chapter has highlighted that Polanyi’s original ideas are substantially more interesting. Polanyi begins with an ontology that rejects reductionism and asserts that many entities are more than the sum of their parts. This, he implies, has implications for how we understand the world, as coherent entities cannot be understood by understanding

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their components; hence Polanyi’s insistence that we know more than we can tell. Instead, Polanyi stresses the importance of tacit inference for perceiving coherence. Learning, therefore, often requires a process of indwelling that builds up knowledge and gradually moves it into subsidiary awareness to enable tacit inference to take place. Polanyi extends these ideas beyond science to the design of technology to highlight how scientific knowledge does not encompass the entirety of what can be known. Because designed technology has a coherence beyond its component parts, the design and production of technologies involves knowledge that is distinct from scientific understanding of those components. Scientific understanding, for Polanyi, focuses on truth, but technological knowledge instead focuses on usefulness. As such, it reflects inherently social concerns about practical applications and judgements about the inherent trade-offs that must be made during design. Operational principles, that imply how a technology will achieve its function, are inherently technological. Their selection and application closes down the number of possible alternative design routes and focuses the design process in a particular direction. In doing so, their selection structures the design process by making the design problem more specific. For Polanyi, tacit knowledge is therefore an essential feature of design and is what allows designers to creatively generate new solutions. It helps explain the creative nature of design, the limited success of attempts to automate design (and weaknesses with the outputs of AI more generally), the importance of diagrams and visual knowledge, and why good design practice is so hard to learn, articulate and teach. In applying Polanyi’s ideas to design, it is difficult to avoid the feeling that tacit knowledge is too broad a concept for the theoretical burdens that have been imposed on it. The cognitive sciences have broken tacit knowledge into a series of distinct, but interacting, phenomena. Similarly, Vincenti and other historians of technology have tended to use more precise and more applicable concepts like operational principles, engineering research, implicit knowledge, etc. While these concepts often draw heavily on Polanyi’s original ideas, they allow a deeper exploration of design. For example, they help us understand how flexible Polanyi’s operational principles are, and how much additional testing has to be undertaken to move from ideas in designer’s minds to final, working artefacts [Vincenti, 1990]. Much of this more recent work, particularly by authors such as Bucciarelli and Henderson, also adopts a much more social understanding of design than the often very individualistic approach taken by Polanyi. While Polanyi’s philosophy of science often presents a historically misleading picture of the heroic lone scientist, his philosophy of technology similarly too often presents design as something that occurs within one person’s head. The ability of tacit knowledge to explain a host of very diverse phenomena, which on closer inspection actually turn out to involve something else, suggests a substantial weakness in how the concept is used. For example, tacit knowledge might explain the localised nature of design capabilities, or localisation might be the result of specialised designers simply having to interact


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more often with one another. Too often the fact that tacit knowledge might explain a phenomenon is used to draw the incorrect inference that it provides the correct explanation. There is no doubt that the concept of tacit knowledge provides useful ways of thinking about design, particularly empirically important aspects of design that are often overlooked elsewhere (visual knowledge, for example). All the same, more work needs to be done towards clarifying what tacit knowledge is and how or if it plays a role in design. Such a conclusion is open to two substantial criticisms. On the one hand, it allows the concept of tacit knowledge to get away with too much. To anyone trained within the Anglo-Saxon analytical tradition, Polanyi’s ideas can be difficult to follow as he jumps between different meanings of the term tacit knowledge. Too often, one gets the feeling that difficult problems are being explained away, rather than explained. Concepts like tacit knowledge, tacit inference and indwelling are rarely clearly defined and it is often difficult to see what they do, and, more importantly, what they do not, encompass. Polanyi might respond that he is correct and many of the problems that seem to exist are simply metaphysical hangovers from assuming that reductionism is true and knowledge is a ‘mirror of nature’, to use Rorty’s phrase. Admittedly, if one thought that all entities in the universe were reducible to the sum of their parts then Polanyi’s ideas may seem magical or mysterious, but he knows as a scientist that the universe isn’t like that. He is therefore simply explaining empirical events. Such a response would seem to be provided with substantial empirical support by historians of technology: much engineering knowledge is difficult to articulate, codified information is rarely sufficient to generate technology, many design concepts are implicit and much of the knowledge used in design involves interaction with material objects, such as drawings, and reflection upon their changed meaning. On the other hand, an alternative critique might be that this chapter has not gone far enough. In trying to explain tacit knowledge and engineering design the chapter has dissolved, and therefore lost, the inherent interconnections between the two. The two have to be understood together, through a process of tacit inference, in order to be understood at all. Like the stereoscopic images, by bringing each into focal awareness the coherence that links them has been lost. Such a criticism should not be dismissed too easily, as intellectual figures as diverse as Raymond Aron and Charles Taylor have found Polanyi’s ideas extremely profound. In response, hopefully this chapter at least hints at this possibility; however, a full integration is beyond the capabilities of the author and the length constraints of an introductory chapter. To reach a conclusion that would placate both sides does not seem easy. There do seem to be good grounds for scepticism about the value of the concept of tacit knowledge. Where it is used, it tends to be used to explain empirical phenomena that are not explained within existing frameworks in the social sciences. However, the explanations often don’t seem particularly robust. Rather than providing a way to change or radically reformulate existing ways of thinking about technical

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change, economics or design, tacit knowledge seems to plug the gaps in existing theories and allow them to proceed onwards unchallenged. Rarely, if ever, are questions raised about the deeper compatibility between the resulting theoretical chimeras [Tsoukas, 2003]. All the same, tacit knowledge as a concept does hint at something more substantial. It was put forward by Polanyi as part of a very radical attempt to challenge the foundations of 20th century social thought. Hopefully, this chapter has shown that, rather than reinforcing existing ways of thinking, Polanyi’s ideas can help understand their very real limitations. By highlighting the emergent nature of phenomena it stresses the unpredictable nature of the world we inhabit, and the failures of reductionism and strong determinism. Polanyi’s ideas can be used to attack the legitimisation of ‘scientism’ without being anti-science [Gill, 2000]. By showing that technological phenomena cannot be reduced to scientific phenomena, even if they can be explained by science, tacit knowledge as a concept can help to highlight the distinct nature of the philosophy of technology [Vincenti, 1990]. Given the ever-increasing importance of technology to society, this suggests a continuing and growing importance for the philosophy of technology in helping society understand what technology is, how it generates unintended consequences, and how it can be directed along more fruitful paths. Polanyi’s ideas may raise more questions than they answer for the philosophy of technology, but those questions are important enough to deserve more time than they have received so far.

ACKNOWLEDGEMENTS The research for this chapter was supported by the ESRC, EPSRC and NESTA.

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PRACTICAL REASONING AND ENGINEERING Jesse Hughes

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INTRODUCTION

Engineering is to science as practical reasoning is to theoretical reasoning. This is a crude analogy, but it has the kernel of truth to it. Roughly, science aims to learn facts about the world around us, while engineering aims to change that world in order to further our aims and goals. Science is successful if it generates true beliefs — in other words if it is successful as a method of theoretical reasoning — and engineering is successful to the extent that it provides means to satisfy our needs and desires, i.e. if it is successful as a method of practical reasoning. Philosophy of science has accordingly adapted epistemology to its analysis of science. If we want to analyze scientific practice, we must understand it in terms of its ultimate goal: the discovery of facts about the world around us. The scientific method is a prescription for reliable judgments about scientific hypotheses and so it is natural to analyze and evaluate this method, both in its general form and in particular applications, in epistemological terms. Similarly, engineering is a method for creating practical devices and processes. Indeed, a recent text [Dym and Little, 2004] offers the following definition: “Engineering design is the systematic, intelligent generation and evaluation of specifications for artifacts whose form and function achieve stated objectives and satisfy specified constraints.” The engineering method — that is, the design process1 — has an explicit practical end. And it makes sense to understand the process, both in its general terms and in specific applications, in terms of practical philosophy. To put it crudely, the design process is analogous to the scientific method and an engineering need — the problem to be solved — is analogous to a scientific hypothesis. The scientific method works to the extent that it generates correct judgments on individual (scientific) hypotheses and the design process works to the extent that it solves the problem at hand.2 1 It

is a bit naive to refer to the design process, since each author offers his own version of it. Similar concerns could be raised about the scientific method, but let us pass over these issues. Whatever design process we have in mind, the aim is the same: to produce useful artifacts or processes. 2 The design process also works if it correctly judges that the problem at hand is unfeasible, either in principle or with current technology. Handbook of the Philosophy of Science. Volume 9: Philosophy of Technology and Engineering Sciences. Volume editor: Anthonie Meijers. General editors: Dov M. Gabbay, Paul Thagard and John Woods. c 2009 Elsevier BV. All rights reserved.

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In particular, instrumentalist terminology fits remarkably well with engineering, on at least three levels. First, the design process is the method engineers use in order to create artifacts (or processes, methods, etc.). This process is divided into steps, each of which aims at a particular goal through the use of various means. This is quite explicit, for example, in [Dym and Little, 2004]: “Each phase requires an input, has design tasks that must be performed, and produces an output or product.” As they describe each phase in detail, they list the “means and methods” used to produce the desired output. Thus, the design process itself is presented in steps, each of which is described in terminology familiar to an instrumentalist. Second, the tools engineers use in design process (that is, the methods in Dym and Little’s terminology) are often instrumental in nature. To take one example, function-means trees are used to generate early proposals for solutions. These trees identify tasks which must be accomplished and alternative ways to achieve these tasks, i.e. it is a matter of identifying ends and sufficient means to those ends. Thus, engineers analyze their problems in instrumental terms. Finally, the final end of the design process is the creation of an artifact (typically) and artifacts are evaluated primarily in terms of their instrumental value (see [Franssen, 2006]). The artifact itself is a means to ends its user may have. If we know how to use the artifact, then we know how it may help us realize certain ends we have now or may adopt later and in this way, artifact functions induce associated means-end claims [Hughes, 2008]. Knowing that fire extinguishers are for putting out fires provides one with a practical consequence. If you happen to have a fire you’d like extinguished, you can use a fire extinguisher to do so.3 This is what artifact functions are about: they are about how and when to use the artifact and for what purpose. Artifacts are typically categorized according to function. As Karen Neander [1995] writes, “a brake is a brake in virtue of what it is supposed to do — was intended or designed to do — not in virtue of having some specific structure or disposition.” Roughly, then, artifacts are categorized according to the ends for which they are intended as means. Furthermore, artifacts — both types and tokens — are evaluated in terms of their effectiveness and reliability, that is, in terms of their efficacy as means. An artifact token that cannot do what it should do is malfunctioning. A type that does not perform as well as it should is badly designed. In both cases, our judgments regarding artifacts are in practical terms.4 In sum, engineering is a process for creating certain kinds of means. We will focus our attention on the design process rather than its end products, since engineering design is so very clearly related to practical reasoning. This is because both the design process and its tools are naturally understood in the familiar language of instrumentalism. It is also true, of course, that the end results 3 Provided, of course, that the fire is not too large or the wrong kind of fire for the extinguisher at hand. Knowing the artifact’s function also suggests when it should not be used. 4 Franssen [2006] states this point most explicitly when he argues that a token is malfunctioning just in case one has a (practical) reason not to use it.

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(the artifacts created by engineers) are related to practical reasoning for the reasons noted above, but this relationship has been explored elsewhere [Franssen, 2006; Hughes, 2008]. Philosophy of engineering thus restricted is interested in what it is that engineers do (when designing artifacts), why they do it and whether we philosophers can tell them how to do it better. Practical philosophy is interested in more or less the same issues, but with particular emphasis on personal rather than professional action. That is, instrumentalist accounts typically focus on desires and their motivational efficacy, but it is not at all clear that desires are the right sort of pro-attitude for a philosophy of engineering.5 Engineering design is about accomplishing certain stable, clearly specified ends, not satisfying personal desires. So, we will have to do a bit of work projecting current practical philosophy onto philosophy of engineering, but in the end, we will see that the adjustments are not drastic and the benefits are clear: our application of practical philosophy will be natural and insightful. Practical philosophy serves two distinct purposes. In its explanatory role, the theory gives reasons why agents behave as they do, while in its normative aspect, it offers an evaluation of an agent’s behavior in terms of practical rationality. Philosophy of engineering similarly has two roles. In its so-called descriptive form, philosophy of engineering provides both descriptions and explanations for engineering practice, while in its normative role, we aim to evaluate engineering practice according to some standard of practical rationality. We will focus primarily on the explanatory role in what follows, since explanation seems a more modest and feasible goal than evaluation. In Section 2, we will give a fairly lengthy introduction to instrumentalism, the dominant theory of practical reasoning. We argue that there is a natural fit between means-end reasoning and engineering and we will provide some rudimentary development in this direction. We will discuss coherence and rationality as they apply to practical goals in Section 3. Sections 4 and 5 discuss promising alternatives to instrumentalism as they apply to engineering. Specificationism is the view that all the real work in deliberation occurs in specifying one’s vague goals more precisely, in particular in the context of other, conflicting goals. We discuss Bratman’s theory of planning in Section 5, indicating similarities it shares with models of the design process. 2

2.1

INSTRUMENTALISM

A brief primer on instrumentalism

The de facto theory of practical reasoning today is instrumentalism.6 We will also argue that instrumentalism is eminently applicable as a theory of practical 5 Doing philosophy of engineering in terms of desire would be something like doing philosophy of science with an epistemology based on hearsay and prejudice. 6 Sometimes called Humeanism, but see [Millgram, 1995] for a discussion of the aptness of this term.

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reasoning in engineering design. We begin with a summary of the characteristic features of instrumentalism and a discussion of their application to engineering. Following this, we will distinguish beliefs and desires in terms of direction of fit and discuss their role in the design process. In Section 2.2, we distinguish the different kinds of ends by means of a simple example and in Section 2.3, we use practical syllogisms to introduce a typology of means. The fundamental thesis of instrumentalism is that practical reasoning consists of nothing but means-end reasoning. That is, practical reasoning is fundamentally about using one’s knowledge of causal relations, constitutive relations, etc., in order to achieve one’s goals and desires — and there is nothing more to practical reasoning than this. As [Audi, 2006] describes Hume’s theory, “Roughly, desire, guided by belief is what produces action.” An agent aims to realize his desires — or at least as many as he can — and applies reason in order to do so. Put differently, practical reasoning is about selecting a course of action given certain beliefs about causal relations and certain prior desires. As Aristotle famously says [Ross, 1908], “For the end cannot be a subject of deliberation, but only the means.”7 To be sure, as a result of such deliberation, we may select new ends, namely those instrumental for (or constitutive of) our prior ends: if I want to earn a comfortable living, I may choose to do so by pursuing a business career. In order to get a good job in business, I may choose to enter a university with a well-regarded business school, and so on. Thus, my original end (to have a comfortable living) has led to the adoption of new ends (to work in business, to enter a good university, etc.). But some ends must be given at the start of this process. Thus, another essential claim of instrumentalism is that there are certain ends (the so-called final ends) which are not adopted for instrumental value but rather motivate the agent prior to deliberation. As Hume [1777/1975] (§244) writes: Ask a man why he uses exercise; he will answer, because he desires to keep his health. If you then enquire, why he desires health, he will rapidly reply, because sickness is painful. If you push your enquiries farther, and desire a reason why he hates pain, it is impossible he can ever give any. This is an ultimate end, and is never referred to any other object. . . . It is impossible there can be a progress [of reasons] in infinitum; and that thing can always be a reason why another is desired. Something must be desirable on its own account, and because of its immediate accord or agreement with human sentiment. For Aristotle [1908] (Book I, Ch. 7), there was ultimately one final end, namely happiness. This is the only end that is chosen “always for self and never for the sake of something else.” Other ends are chosen for their ability to lead to happiness. 7 Although [Millgram, 1995] disputes the claim that Hume was an instrumentalist and [Wiggins, 2001] argues that Aristotle is a specificationist, we will nonetheless take Hume and Aristotle as standard figures in instrumentalism.


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Even though ends such as honor and reason are chosen for themselves, they are also chosen “also for the sake of happiness, judging that by means of them we shall be happy.” Peter Railton [1997] calls this approach, in which all action aims for some single, final end — the good — the High Brow account. Hume, by contrast, is the exemplary proponent of the Low Brow account: Some things other than the good are desired for their own worth. Satisfying such “intrinsic” desires (as [Audi, 2006] calls them) provides pleasure, but nonetheless these things are pursued for their own sake and not for the consequent pleasure. These include such desires as to punish one’s enemies and provide happiness for one’s friends [Hume, 1739]. In summary, then, the two characteristic claims of instrumentalism are: (1) all practical reasoning is means-end reasoning and (2) some ends admit no further justification. As one might expect, these claims are also the source of much controversy. In particular, many authors ([Millgram, 1997; Kolnai, 1978; Schmidtz, 2001], among others) dispute the claim that chains of ends necessarily come to a rationally unjustified “final” end. Since the argument for such final ends follows from the instrumentalist claim that all practical reasoning is ultimately means-end reasoning, such criticisms aim at the heart of instrumentalism.8 Instrumentalism and engineering By and large, however, philosophers of engineering may sidestep the controversies regarding instrumentalism. For the “pure” practical philosopher, whether practical justifications necessarily come to unjustified final ends is an essential question, but it’s not so relevant in understanding engineering practice. If we want to understand how a mechanical engineer chooses the materials for her project, say, we would certainly stop asking about her motivation before she answers in terms of personal pleasure or pain. For our purposes, a final-in-context end will suffice. We take for granted that our engineer has chosen to design, e.g., a springloaded bar mousetrap (the common mousetrap found in any hardware store). We investigate her later decisions given the project at hand.9 As Aristotle says of the professional classes, “They assume the end and consider how and by what means it is to be attained” [Ross, 1908](Book III, Ch. 3). Similarly, we may avoid the difficult question of whether all practical reasoning is means-end reasoning. We are not interested in all practical reasoning, but rather practical reasoning of a narrow sort: the kind that engineers use in designing 8 Schmidtz is an exception here, since he works in an instrumental setting while denying the necessity of unjustified final ends. 9 Of course, one may be interested in how the project at hand was selected as well. For instance, many issues in engineering ethics are concerned with the morality of designing certain kinds of artifacts. Regardless, the point is that our enquiry into motive will be limited and so we will not fall into Hume’s infinite descent.

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their products. And it seems undeniable that at least some practical reasoning is means-end reasoning. In fact, [Millgram, 2001a] calls means-end reasoning the “least controversial form” of practical reasoning: at least sometimes, we reason about how to achieve some goal we have. So let us skip broad questions regarding whether this least controversial form is the only form and instead see whether an analysis of engineering design in terms of means-end reasoning bears fruit.10 It is tempting to suggest that means-end reasoning plays the same role in understanding engineering as it does for understanding any occupation. According to Aristotle, every profession assumes an end and deliberates about the best means to that end. Nonetheless, we are not arguing that instrumentalism is the best way to understand what financial consultants, poets or economists do. But engineering practice is inextricably bound with the practical methods of engineering, including the design process. Indeed, the point of studying engineering design is to acquire the skills to reliably deliberate about what should be done and to select the design that will best satisfy our needs. To be sure, an engineer’s education includes other topics. A bulk of the curriculum is devoted to learning engineering science, but this is understandable in practical terms as well: reliable deliberation requires true beliefs as well as a clearly stated end to be accomplished, and engineering science is aimed at the former. In summary, the various design methods are methods of practical deliberation and engineering is an inherently practical discipline. And since it is natural to interpret both the process and end products of engineering in terms of means-end relations, instrumentalism seems an appropriate first consideration for a theory of practical reasoning in engineering. Beliefs and desires in instrumentalism Instrumentalism is appealing for its emphasis on basic folk psychology that most of us accept in our daily reasoning. In seeking explanations for actions, it is usual to refer to an agent’s beliefs and desires at the time. She chooses to fetch a key, say, because she wants to unlock the door and she believes that the key provides a means to do so. These two kinds of mental states play different roles in our explanation: she chooses her action in order to realize her end, but her choice is informed by her beliefs. The most popular current analysis of the difference between beliefs and desires is in terms of direction of fit, due to [Anscombe, 1989]. A belief aims at being true, and if one’s beliefs are not true, then they should be changed to “fit” the world. False beliefs are discarded and beliefs which more closely fit the world are adopted. A desire, on the other hand, is not discarded just because it does not fit the world. Rather, if one desires ϕ, one wants to change the world to make ϕ true. As [Platts, 1979] says, “beliefs should be changed to fit with the world, not vice versa. Desires aim at realization and their realization is the world fitting with them.” 10 One may nonetheless ask a narrower form of the instrumentalist question: Is means-end reasoning the only kind of practical reasoning that engineers use?


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This intriguing metaphor is considered in some detail by [Smith, 1987], [Humberstone, 1992] and elsewhere. Humberstone gives a useful clarification of direction of fit in terms of conditional intentions. It is constitutive of beliefs, he says, that one intends not to believe ϕ given that ϕ is false. Symbolically, Intend(¬Bϕ | ¬ϕ),

(1)

but note that the conditional intention operator cannot be contraposed on this account. That is, (1) is not equivalent to Intend(ϕ | Bϕ). The latter expresses that I intend that ϕ is true, given that I believe it, but this is not a feature of belief. Rather, it is a feature of desires: I intend ϕ given that I want that ϕ. Consequently, Humberstone says that the following condition is constitutive of desire: Intend(ϕ | W ϕ). (2) Because science is a theoretical endeavor and engineering a practical one, there is a loose analogy of directions of fit for the science/engineering distinction. Put crudely, science aims to learn about the world and hence takes the world as input to produce a system of beliefs. Engineering aims to manipulate the world, and hence takes ends as input to make changes in our circumstances. As [Simon, 1969] says, “The natural sciences are concerned with how things are. . . Design, on the other hand, is concerned with how things ought to be, with devising artifacts to achieve goals.” The dictionary definition of engineering also reflects this picture: engineering: . . . the art and science by which the properties of matter are made useful to man, whether in structures, machines, chemical substances, or living organisms. [Webster, 1913] This rough adaptation of direction of fit is intended as metaphor, of course, and we have used a fairly naive view of both science and engineering. It is nonetheless suggestive of the crux of the theoretical/practical divide. How, then, does the belief/desire terminology fit into an analysis of engineering? The role of beliefs in engineering is reasonably clear: the beliefs that an engineer brings to his deliberations include knowledge of engineering science (including material science, physics and other causal laws and particular results deduced from relevant scientific theories) as well as beliefs regarding the desires of his client or potential consumers. Desires, on the other hand, are a personal attribute, a feature of one’s psychological state, and that is an awkward level on which to analyze professional performance. We say that the engineer engaged in building a better mousetrap is directed towards an end or goal, rather than that she is driven by her desires for a better mousetrap. Some authors (e.g. [Audi, 2006; Millgram, 1997]) use the term “desire” broadly enough to include the pursuit of ends, while others (e.g. [Richardson, 1994]) take desires to involve weaker commitments, so that “a desire is a particular psychological state with motivational efficacy” while an end “is something for the sake of which an action is to be done.” To put it differently, “although one simply has

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desires, one adopts or pursues ends.”11 Thus, while it may be rational to desire something while being unwilling to do what is necessary to attain it, one cannot have an end without doing what is necessary to realize it. This distinction is plausible and useful in the traditional domain of practical philosophy, but Richardson’s notion of desire will play little role in our understanding of engineering. Thus, we will follow Robert Audi’s lead and use the term desire to indicate any sort of want — including the pursuit of an end. When we use the term in an engineering context, we will generally interpret “desire” as the pursuit of a relevant engineering goal.

2.2

A crash course in applied instrumentalism

In this section, we will introduce a basic typology of ends by offering an account of mousetrap design. Our engineer has decided to design a springloaded bar mousetrap. This is, broadly, the end of her current deliberation, but there are many related (and conflicting) goals. She would like the mousetrap to be durable, reliable, inexpensive and safe. She may not yet have a clear idea how these objectives conflict with one another or how she ought to strike a balance, but she seeks to realize her end in a way that satisfies each of these qualities as far as possible.12 She begins the process now by identifying certain steps which advance her final end. For instance, to design a standard springloaded bar mousetrap, among other goals, one must choose the materials to be used and design the spring mechanism. In order to design the spring mechanism, our engineer must decide how hard the spring should strike the mouse. It should strike hard enough to kill the mouse (i.e., it should be reliable) but not so hard that it poses a danger to the person setting the trap (i.e., it should be safe). It should also at least give the appearance of humanely killing the mouse. Our engineer must determine, then, what the optimal force is for her needs. Assuming that she does not know this at the start, this will require investigation.13 Thus, she acquires a new goal: investigate to find the proper force for the springloaded bar. To do this, she may decide that a literature search is appropriate, and so she adopts another end: consult the literature. She continues in this manner until she reaches, at last, a task that she can perform at will — one which needs no further preparation. Thus, from her initial end of designing the mousetrap, our engineer has found means that advance her end and come to acquire at least some of these means as new ends to pursue. The situation is represented in Figure 1. An arrow from one task to another indicates that the former is done in order to advance the latter, 11 The

emphasis in this and all other quotes appear in the originals. specificationist terms, her end is currently only vaguely identified and will become more sharply defined as she proceeds. See Section 4 for an overview of this alternative theory of practical reasoning. 13 Let us hope that research into published studies and maybe some calculations are sufficient, so that she does not have to experiment with live rodents and vulnerable human fingers! 12 In


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Design a mousetrap 7 dII II ppp p II p p II c c ppp II p p II p pp Choose materials

Design spring mechanism O i

Investigate proper force

Figure 1. Designing a mousetrap generates new ends. i.e. is a means to the latter. The topmost task (“Design a mousetrap”) has no outgoing arrows and is final in this context. The bottommost task (“Investigate proper force”) is instrumental to the task of designing the spring mechanism. It is neither necessary nor sufficient, but investigation will promote the design of the mechanism: our engineer will achieve better results by performing this task than otherwise. In contrast, neither of the tasks on the second level are instrumental. One does not choose the materials in order to facilitate the end of designing the mousetrap. On the contrary, these tasks are part of that goal. They are constitutive means,14 as in Ackrill’s putting example [1980]: “One does not putt in order to play golf as one buys a club in order to play golf. . . It will be ‘because’ you wanted to play golf that you are putting.” A game without putting is not a golf game. A means is constitutive just in case it is a constituent of or an ingredient in its goal (ibid.). Just as an instrumental means can be necessary or sufficient (or neither), so too with constitutive means. One cannot design a springloaded-bar mousetrap without designing the spring mechanism, and hence it is necessary to our task. Clearly, it is not sufficient, since one must also (among other things) choose materials. Going for a jog15 is a sufficient constitutive means for exercising. By jogging, one has exercised; there is nothing more to do. Castling while playing chess is an example of a constitutive end which is neither necessary nor sufficient. We will discuss sufficiency and necessity in more detail in the next section.

14 See 15 An

also [Schmidtz, 2001] for a discussion of the constitutive/instrumental distinction. example by [Schmidtz, 2001].

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Adapting the notation from [Schmidtz, 2001],16 we have labeled the arrows in Figure 1 with “i” if the source is instrumental for the target and “c” if it is constitutive. These are the two primary kinds of means generated from more final ends. Each means serves a dual role as an end for further means, until we reach an action which can be performed straightaway (say, reaching for a book in order to pursue our investigation). When an instrumental (constitutive, resp.) means is adopted as an end, then we also refer to it as an instrumental (constitutive, resp.) end. Thus, we have the following three kinds of end: • Final (in context): The ultimate end of the project underway (e.g., designing a mousetrap). • Instrumental: An end which may be pursued because it advances another (more final) end (e.g. investigating the proper force needed). • Constitutive: An end which may be pursued because it is a constituent of or ingredient in another (more final) end (e.g. designing the spring mechanism). This will be our crude model of an engineer at work, then. But means-end reasoning is relevant to engineering in another way: for an artifact to work properly, its components must serve as means to the functional end. For instance, a television remote must deliver signals to the television set. There are various ways this could be done, including infrared or radio transmissions or through a wire. An engineer designs an artifact by selecting appropriate means to perform these various functions. See the discussion of function-means trees in [Dym and Little, 2004] for the importance of this kind of analysis. Maieutic ends in engineering There are various complications to this basic model, but perhaps the most interesting for our purposes is found in [Schmidtz, 2001]. Schmidtz accepts the basic instrumentalist assumption — practical reasoning is about means to ends — while arguing that some final ends are nonetheless rationally chosen. There is, says Schmidtz, a fourth kind of end, namely a maieutic end — a goal of coming to acquire new final ends. To take Schmidtz’s example, as Kate nears young adulthood, she may feel a need to settle on a career path. Once Kate settles on a career in medicine, her maieutic end has been realized while her career goal is now pursued for its own sake. It is not instrumental to the maieutic end, since that goal was to choose a career and pursuing a medical career is not a means to choosing a career. Neither is it constitutive, for the same reason: pursuing and choosing are different activities. Notice that a maieutic end, in turn, could be either final, instrumental or constitutive. It may be that Kate feels pressure to choose a career path just to satisfy 16 This article is a shorter adaptation of [Schmidtz, 1994], omitting many examples of reasoning with maieutic ends, but the later version includes the diagrams that motivate our discussion here.


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her parents and school counselors. In that case, she has adopted a maieutic end for its instrumental value. Schmidtz’s interest here is to show that certain ends — those mandated by maieutic ends — are both final and rational. If adopting an end satisfied a maieutic end had at the time, then it is rational. This is true even if the end was not consciously chosen for this purpose. Perhaps Kate simply found herself drawn to a medical career independently of any felt need to choose a career at all. Nonetheless, the adoption of this career goal is rational, since she had a need to choose a career. Indeed, Schmidtz argues that one may reach a state in which every final end one possesses is rational in this sense: chosen to satisfy a maieutic goal she had at the time. Do maieutic ends occur naturally in engineering design? Arguably, they occur regularly, at the start of the design process. Each year, automobile manufacturers find ways to change their offerings so that the 2008 GMW Tranquestor attracts attention that is waning for the 2007 model. Similar “progress” is evident in software. New versions come out with new features (and occasional bug fixes) in order to entice new purchases. Each of these projects begins with a basic question: How shall we update our product to encourage sales? Thus, software engineers are faced with a maieutic goal: finding something to do to make Turbo Word++ 2008 better than Turbo Word++ 3.0. And once the changes are selected, they get to work on the new product.

GF

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76 /01 Existing knowledge 54 23

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State of the art

=< :;

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Scientific curiosity

=< :;

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76 01

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76 Feasability analysis 23 54 01

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Figure 2. Hill’s comparison of the scientific and engineering methods.

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Indeed, the process of engineering, especially in its entrepreneurial forms, begins with broad maieutic goals: find a need and satisfy it. This is how software engineering projects, both proprietary and free, often start: with a search for a problem to be solved. For commercial efforts, these maieutic goals are motivated at least in part by the desire for monetary reward, but some commercial (and perhaps most free) software developers also desire projects for the intellectual challenge and the joy of creation. The Linux operating system famously began as a hobby for then graduate student Linus Torvalds [Torvalds, Linux, 1991]. Philosophers of engineering, however, start their investigation after a need has been found. They follow the lead of engineering educators in this respect, since design models also assume at the start that a need has been identified, at least in rough form. For instance, [Dym and Little, 2004] discuss four descriptive and one prescriptive model of the design process, but each of these take for granted that a need has been identified. Similarly, Dieter’s [1983] simpler model of the design process begins with the recognition of a need. An exception is Hill’s [1970] comparison of the scientific and engineering methods (Figure 2, adapted from [Dieter, 1983]). This cyclical model includes an “identification of needs” step. At least for Hill, identification is something that happens during engineering rather than prior to it. One wonders whether there is a “recipe for rational choice” (as in [Schmidtz, 2001]) with an engineering focus to apply to the mostly overlooked task of identifying a need.17

2.3 Syllogisms and means In the previous section, we distinguished different kinds of ends according to how they contribute to more final ends. In this section, we introduce a classification of means in terms of their motivational efficacy. We will do so by examining the roles of means in practical syllogisms. Means-end reasoning seems particularly well-suited to a syllogistic presentation,18 since it is fairly uniform in its structure. An instance of means-end reasoning typically includes the following features: 1. a statement that a particular condition is desired or aimed for (the major premise), 2. a proposition or belief, either about an instrumental or constitutive means to the desired position (the minor premise), 3. an intention or pro-attitude towards an action or an action itself (the conclusion). This is the way in which means-end reasoning combines beliefs and pro-attitudes in order to generate practical consequences. 17 Such inquiries have analogues in philosophy of science, namely in the logic of scientific discovery (i.e. hypothesis generation). 18 This point is not without controversy. See, e.g., [Richardson, 1994].


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Roughly, then, a practical syllogism has the form: A wants to realize ϕ. α is a means to ϕ. Therefore A has reason to do α.

(3)

Variations on this form are due to the exact relation of the means to its end. Of course, a means may be either instrumental or constitutive, but this is not the distinction we have in mind here. Rather, we are primarily interested in whether the means is necessary, sufficient, optimal or none of the above.19 A means α to an end ϕ is necessary if ϕ cannot be realized without doing α. In this case, the conclusion of (3) should be in the strongest normative terms: in this situation, A has overwhelming reason to do or must do α. See, for instance, von Wright’s [1963] example of a valid syllogism: A wants to make the hut habitable. Unless A heats the hut, it will not become habitable. Therefore A must heat the hut.

(4)

Von Wright expresses the normative force of the conclusion in very strong terms: “if action does not follow, we should have to describe the subject’s case by saying either that he did not in fact want his professed object of desire or did not, after all, think it necessary to do the act in order to get the wanted thing.” A cannot recognize the truth of the premises without thereafter heating the hut. We must take care with this conclusion on two points, however.20 First, it is always possible that I did want ϕ until I realized that I must do α. Once I realized that α is necessary, I may change my mind about wanting ϕ after all — at least assuming that my desire for ϕ is the sort that I can change my mind about. This is analogous to the situation with theoretical syllogisms. When I realize that my beliefs in A and A → B require that I accept B (which I find plainly false), I may adjust my beliefs about A or A → B. The same adjustment may occur in any practical syllogism which gives strong reason to perform an action I am unwilling to perform. It would be irrational, of course, to decide that, because I don’t want to do α, I will refuse to believe that it is necessary, but I have the freedom to revise (at least some of) my desires and goals. Audi [2006] raises the second issue regarding the strong consequences for this kind of syllogism, namely that the conclusion is not valid without some further assumptions on the means α. The fact that ϕ won’t be realized unless we do α is not enough to motivate me to do α. I should also be convinced that, having done α, there is a real chance to bring about ϕ. I am unable to realize my goal of 19 These

distinctions are suggested in [Audi, 2006]. [1994] criticizes the syllogisms on other grounds. The term “unless” must be interpreted in the logical sense or else the syllogism is not valid, but it is extremely rare that one has sufficient evidence that a means is necessary in this strong sense. Thus, the syllogism is valid only if it is almost never applicable. 20 Richardson

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drinking the entire Atlantic Ocean through a straw unless I first acquire a straw, but that should not give me an overriding reason to acquire a straw.21 My goal ought to be attainable and doing α should put me in a reasonable position for achieving ϕ before I accept von Wright’s syllogism.22 But necessary means are the easy case. If one wants to accomplish his goal, he has little choice but to do what is necessary. In other cases, the conclusion is rather more vague. A typical example regarding sufficient means is found in [Railton, 1997]23 :

ϕ is an end of mine. Means α would secure ϕ.

(5)

There is that much to be said deliberatively in favour of my doing α, or against my having ϕ. In this case, we assume that doing α will bring about ϕ, i.e. that α is a sufficient means to ϕ. This is a fairly good reason to do α: it would realize the goal under consideration. Of course, I may have many other options which would bring about ϕ, so this reason is not overwhelming. In fact, I might choose a means which is not sufficient, but which is nonetheless preferable for other reasons. I want to avoid illnesses found in contaminated meats. I can be sure to do so by eliminating meat from my diet. Any other alternative is less reliable, but nonetheless I choose a less onerous alternative, such as exercising care in the selection and preparation of my meats and choosing reputable, clean restaurants. These other means (which are not guaranteed to secure my aim) are preferable to the sufficient means of avoiding meat. This brings us to optimal means. I have decided against the sufficient means (stop eating meat) because it is not as good in my estimation as an alternative. For various reasons, I prefer to keep eating meat but to change my habits to decrease risk. Let us suppose that I have some criteria in mind so that I can compare two actions and evaluate whether one is better or preferable to another. This criteria may include effectiveness, reliability, cost in some broad sense and coherence with other ends I may hold. In case a means α is superior to all others, then it seems plausible that I should do α. A syllogism to this effect is found in [Churchland, 1970]:24

21 Perhaps, of course, I should give up my desire to quaff an ocean, but not for the reasons discussed above. Getting a straw is not onerous to me and presumably neither is the act of drinking the ocean. The problem is that my end is irrational, but an instrumentalist cannot complain about irrationality unless I have chosen to drink the ocean as a means to some other end. For final ends, we want what we want. 22 See also [Hughes et al., 2007] for a semantics of necessary means that includes attainability. 23 Variables changed to match mine. 24 Adapted here from [Audi, 2006], substituting my variable names.


I want ϕ. Doing α is a way for me to bring about ϕ under these circumstances. There is no other way to bring about ϕ now which is preferable to me as, or more preferable to me than, doing α.

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(6)

There is no sufficient reason for me not to bring about ϕ under these circumstances. Therefore, let me do α. The third premise, missing from our previous examples, is an important consideration. As Audi [2006] points out, one should not choose an optimal means to an end ϕ if there is an overriding end ψ in conflict with ϕ. Such an overriding end would provide sufficient reason not to do α and hence Churchland’s syllogism would not apply. We often reason about means which are rather weaker than any of these examples. We may be faced with a situation in which no means is clearly optimal, necessary or sufficient. In these cases, we generate weaker reasons to act: each means which advances our end produces a motivational (and perhaps normative) reason to act accordingly.25 It is difficult to spell out clearly how one measures the strength of each of these reasons and, indeed, whether practical reasoning is really captured by an internal calculation based on comparisons of reasons’ strength. Let us pass by these more complicated arguments at present and examine practical syllogisms in engineering. Certainly, engineers deal with each of the different kinds of means discussed above. The function-means trees found in [Dym and Little, 2004] are a tool for enumerating alternative sufficient means to the project goal, for instance. Physical restrictions create certain necessary means: any internal combustion engine must include a device that mixes oxygen and fuel. And engineering design books are chock full of different measurements of optimality as well as methods for calculating the optimal outcome. As [Dieter, 1983] puts it, “in engineering design we have a situation in which there is a search for the best answer. In other words, optimization is inherent in the design process.” If something like the above syllogisms are correct models of human reasoning, it follows that engineers implicitly use syllogistic reasoning in their deliberations. But these syllogisms apply only when the real intellectual work has been done. As Richardson [1994] says, “Once it has endorsed an action as both the best means to an end and not productive of too much countervailing bad, then deliberation has largely done.” Indeed, [Velleman, 1996] suggests that such syllogisms cannot 25 In such situations, we may rely on Simon’s “satisficing”: a solution which is good enough is good enough — especially in cases where the costs of searching for a better solution are significant and the likelihood of failure non-negligible. See Chapter Five on The Science of Design in [Simon, 1969].

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Design objectives

Weight (%)

Polythelene bottle with twist cap

Mylar sack with straw

Eval.

Score

Eval.

Score

Environmentally benign

33

0.9

29.7%

0.1

3.3%

Easy to distribute

09

0.5

4.5%

0.6

5.4%

Preserves taste

22

0.9

19.8%

1.0

22%

Appeals to parents

18

0.8

14.4%

0.5

9.0%

Permits marketing flexibility

04

0.5

2.0%

0.5

2.0%

Generates brand identity

13

0.2

2.6%

1.0

13%

Totals

99

73.0%

54.7%

Table 1. A numerical evaluation matrix adapted from Dym and Little. count as practical reasoning at all, since they explain reflexive behaviors as well as “full-blooded actions”. Nonetheless, these syllogisms provide a good account of the motivational content of the different kinds of means encountered in engineering and other practical deliberations. Furthermore, we can explain the role of various evaluative tools used by engineers. Recall the difficulty in interpreting the consequence of the sufficient means syllogism (5). If α is sufficient, then we have some reason to do α, but this conclusion gives no hint whether it is a strong or weak reason or how one should choose from among the many different sufficient means available. What one wants is a way to evaluate these different options, so that one may be chosen as superior to the others, that is, one wants to be able to apply reasoning like that of the optimal means syllogism (6). And this is precisely what engineers are taught to find. When the design process suggests several different options, designers find a standard of evaluation by which to compare them so that they can, as [Dym and Little, 2004] say, “pick a winner”.26 The authors suggest three methods for selecting the best design. A typical example of such methods, the numerical evaluation matrices method (Table 1) rates the alternatives as follows: 1. Assign weights wo to each of the objectives o ∈ O (low cost, environmentally benign, appealing, etc.) identified earlier in the design process. The weights indicate importance.27 26 Interestingly, the standard of optimality is chosen after the set of alternative means has been identified! 27 Dym and Little give some advice [2004] on how these weights are chosen. These methods for minimizing subjectivity deserve some examination themselves, but we will not do so here.


391

2. Evaluate each alternative design d with respect to each objective, assigning a value vod indicating the degree to which the design meets the objective. 3. For each alternative d, calculate o∈O vod · wo . This is the value v d of the design. The best alternative is that design d with the highest value v d . These methods aim at removing subjective judgments from the selection process, at least as far as possible. Engineers must decide the importance of each of the previously identified objectives and the degree to which each design meets these objectives. Note that these tasks are not deliberation, in the sense of practical reasoning. The engineers are not choosing what to do (nor what ends to pursue) when they rank the objectives or evaluate the designs. In the first case, they are ascertaining the degree to which the objectives are relevant for the problem being solved and in the second, they are judging (hypothetical) performance with respect to well-defined criteria. Both tasks are strictly in the realm of theoretical reasoning. The evaluation techniques in standard design theory, then, give one tools to transform a decision involving several competing sufficient means to a situation in which one means is optimal. This practice is motivated by basic instrumental reasoning. A means-end analysis thus serves to explain this method. Practical philosophy thus provides an insight into this piece of what engineers do and why they do it. 3 COHERENCE We turn now from a purely instrumentalist account to consider three alternative views of practical coherence and rationality. The first is a familiar and welldeveloped topic, decision theory. The second, due to [Edvardsson and Hansson, 2005], proposes four criteria a goal must meet in order to count as rational. The final account is offered by [Millgram and Thagard, 1996], and evaluates the coherence between means and end in terms of facilitation. We will examine each of these theories with attention to its relevance to engineering. The numerical evaluation matrices described at the end of the last section resemble decision theory, an influential theory of practical reasoning. Decision theory begins by assuming that an agent has a utility function u, mapping circumstances to real numbers. The function encodes the degree to which the agent desires to be in that circumstance. I prefer a hot bath to being roasted alive, so my utility function assigns a rather higher value to the hot bath than to the painful ordeal at the stake. In order to determine which action is the best course to take, I determine the probabilities of the outcomes of the various actions and take a weighted average of the utilities of these outcomes.28 This calculates the expected utility of each action and I should perform that action with the highest expected utility. 28 Since decision theory is not our primary interest here, I will describe only the simplest version. I will ignore the much more usual case, in which we do not know these probabilities with any specificity.

392

Jesse Hughes

One of the charms of decision theory is that it provides a clear account of rational preferences. There is a small set of axioms that one’s preference relation must satisfy so that it generates a sensible utility function.29 This set of axioms gives a well-motivated notion of coherence for preferences, by ensuring, for example, that if one prefers ϕ to ψ and ψ to ϑ, then one prefers ϕ to ϑ. One would like to have similar criteria on the goals engineers aim for. One option, then, is to consider whether decision theory is a reasonable tool for analyzing the engineering process. Unfortunately, because the theory gets its start after the preferences have been assigned, it seems to miss an important part of the process.30 The hard parts of engineering are discovering what options are available and deciding among these options. In the latter part, engineers often use a tool (the numerical evaluation matrices) that mimicks decision theory, but again, the real decision-making occurs when one chooses the objectives, “sets the weights” and evaluates each design with respect to the objectives. The remainder is mere calculation. To adapt a metaphor from [Wiggins, 2001], decision theory is well-suited for describing a game of snooker, rather than a process in which we decide how to solve a practical problem by constructing a new or improved artifact. Instead of demanding coherence for our set of preferences, let us retain focus on our instrumental analysis and ask whether there is a notion of rationality for a set of ends. To be sure, if the hardline instrumentalist is correct, there may be little sense in aiming for a coherent set of ends. After all, when it comes down to it, we want what we want. Our final ends are simply given at the start and if they are incoherent in some sense, so be it.31 But this negative response needs not deter our interest, since we are considering the instrumental ends that arise in a particular context, namely engineering. Engineers deliberate on which objectives are desirable for their constructions and there is a clear selection process involved. We may ask whether there is a set of rational constraints on the outcome of this process. A minimal constraint is that the ends selected be consistent. One should not aim to pursue two mutually inconsistent goals. Our mousetrap engineer would be irrational to insist that her product should not harm or restrict the freedom of the mouse in any way. One cannot solve a mouse problem without at least displacing some mice.32 But even this minimal requirement needs some clarification. Engineering objectives, such as low cost and high performance, are often in opposition. In general, success at lowering cost will have an adverse effect on performance and vice versa. The consistency criterion is best reserved for those situations in which 29 See,

e.g., [Luce and Raiffa, 1957]. [1969] makes a similar, if more modest, point when he writes, “When we come to the design of systems as complex as cities, or buildings, or economies, we must give up the aim of creating systems that will optimize some hypothesized utility function.” For simpler projects, on the other hand, he evidently supports decision theory as the proper model of design theory. 31 See also Section 4 for an alternative reply (specificationism) to this response. 32 Let us suppose that customers are not willing to move to a new location so that the mice continue to enjoy their current living arrangements. 30 Simon


393

the goals are stated precisely (the cost should be no more than $x and the vehicle should accelerate at a rate of no less than y). Edvardsson and Hansson [2005] have given an analysis of goal rationality that is well applicable to engineering practice. They identify four criteria — precision, evaluability, approachability and motivity — that a goal must meet in order to be rational, i.e. achievement-inducing. Let us take these in increasing order of importance for philosophy of engineering. The motivity of a goal is the degree to which it motivates those involved to work towards it. It is easier to meet a goal if everyone involved is committed to it. This feature is not much discussed in design literature, since it is more naturally an issue for management33 than engineering and we will not dwell on it here. A goal is approachable if it is feasible to either accomplish the goal or come close enough to do some good. Engineers pay explicit attention to this feature via feasibility studies, a standard tool for ensuring approachability. Precision is a measure of the detail in the goal’s specification. This feature is prominent in engineering, especially in the problem definition phase. Models of design process begin with the recognition of a need, whether requested by a client or independently discovered. This serves as the broad goal of the engineering project, but before any work can be done (even on the conceptual level), this goal must be clarified. The problem definition stage is an essential part of design processes presented in both [Dym and Little, 2004] (see Figure 3) and also [Dieter, 1983]. As Dieter says [1983], “The true problem is not always what it seems to be at first glance.” The engineering goal must be refined to include such information as design objectives, user requirements, constraints and functions, per Dym and Little’s presentation. The authors also list the particular sources of information (including literature on the state of the art, regulations, etc.) and methods (including function-means trees, requirement matrices and so on) that are particular to this important stage of development. A goal is evaluable if the agents can determine the degree to which it has been met. It is related to precision, inasmuch as a goal cannot be evaluable if it is not precise. The problem definition and conceptual design stages produce evaluable goals for the subsequent stages. Engineers verify that the design-in-progress meets these goals and that the goals are reasonable, thus forming a feedback loop (shown in Figure 3) that allows further refinement in the design. So, like precision, evaluability is a valued feature in engineering. Millgram and Thagard [1996], (see also [Thagard, 2001]) give a rather different account of coherence. They propose a model that clarifies the “instrumental relation of facilitation”. Coherence between an action A and a goal G measures the degree to which A facilitates G. Watching a rerun of “World’s Wildest Police Chases” facilitates my goal of enjoying mindless entertainment, but it impedes my goal of finishing this article. Thus, it coheres well with the former but not with the latter. Because the theory does not distinguish means from ends, the 33 Edvardsson and Hansson have come to their subject through management theory rather than engineering.

Jesse Hughes

Client statement on ml hi (Need) jk

onFinal designmlo hi jk (specs and docs)

?> / 89

Problem definition

?> 89

Conceptual design

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Preliminary design

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Detailed design

= 0; J an interval in R)

(24)

This theory has only one semantic model. The model corresponds to different spring-weight systems as we take different values for A, γ, ω0 and J. Note that the semantic model is almost the same thing as the function in (13); the main differences are the dependence on J (which we hadn’t yet discussed in Section 4), and the attachment of the function to the primitive x. This reduction to a single semantic model is available for many numerical models. In general it applies to the computer models used for modelling wind turbines, if we regard the computed outputs as descriptions of mathematical functions. Different runs of the program with different parameters correspond to different parameter values in the computed functions. A theory in first-order logic, if it has any semantic models with infinitely many individuals, has many such semantic models of different sizes. (This is a consequence of the Upward L¨ owenheim-Skolem Theorem [Hodges, 1997, p. 127].) So first-order theories are almost guaranteed to be unsuitable for this case. The theory (24) is not first-order, because it has expressions with fixed arithmetical meanings. The second case is where the theory is first-order, but of a particular form called universal Horn. A typical universal Horn sentence has the form ‘For all x1 , . . . , xn , if φ1 and . . . and φk then ψ’, where φ1 , . . . , φk , ψ are atomic formulas (for example equations). Universal Horn theories have ‘free’ semantic models whose elements satisfy the minimum possible number of atomic formulas; a free model is determined (up to isomorphism, but there is a canonical choice in the isomorphism class) by the size of its ‘basis’, i.e. its set of generators. If A is a free model of a universal Horn theory M and A has an infinite basis, then we can recover from A the set of all first-order sentences that follow from M . For further details (including the connection with ‘initial models’) see [Hodges, 1993]. At present the main practical applications of universal Horn theories are in computer science. In fact software specifications are often written entirely in universal Horn sentences. (See for example the ‘free specifications’ in CASL [Astesiano et al., 2002].) Frederick Suppe proposes we should apply the name ‘theory’ not to a theory in our sense, but to its model class (more precisely to its class of ‘theory-induced physical systems’). His reason is that

Functional Modelling and Mathematical Models

As actually employed by working scientists, theories admit of a number of alternative linguistic formulations—for example, classical particle mechanics sometimes is given a Lagrangian formulation and other times a Hamiltonian formulation—but it is the same theory regardless which formulation is employed. As such, scientific theories cannot be identified with their linguistic formulations; rather, they are extralinguistic entities which are referred to and described by their various linguistic formulations. ([Suppe, 1977, p. 82ff])

689

(25)

Suppe’s proposal is technically flawed; Lagrangian and Hamiltonian formulations use different primitives, and different primitives implies disjoint model classes. More important, one of the chief purposes of having theories is that we can write them down in journal articles or on computer slides. We can’t do that with model classes, or even with single semantic models when they have infinite domains. In order to think about a model class or a structure at all, we need to describe it in words or pictures, and then we are back with a linguistic theory.

9 ISOMORPHISM AND SIMILARITY Our diagram (1) now has two new kinds of entity in the middle: structure A

defines

interpretation model class

model (or theory) M

system S

(26)

The model M on the left was textual, so we equipped it with a type structure on its primitives and called it a theory. Then we introduced its model class (top left). To relate the theory to the system S on the right, we interpreted each primitive in terms of S. This interpretation gave us a structure A (top right). Now the following three statements all mean the same thing: A is in the model class of M . A is a (semantic) model of M . Read as a description of A, M is true.

690

Wilfrid Hodges

The modelling literature contains two other possible relationships between A and M that are alleged to be helpful. The first is that A is ‘isomorphic to a model of’ M , and the second is that A is ‘similar to a model of’ M . What if anything do these notions add to the picture we already have?

Isomorphism Suppose A and B are two structures with the same primitives and type structure. Suppose also that there is a one-to-one correspondence i between the domain of A and the domain of B. We say that i is an isomorphism from A to B if i makes B a perfect copy of A in the following sense: for each primitive p, if we take the interpretation of p in A and we apply i to it so as to change elements of the domain of A into elements of the domain of B, what we finish up with is exactly the interpretation of p in B. For example suppose the domains of both A and B are the set R of real numbers, and i is the correspondence that makes each real number x correspond to −x. Suppose also that one of the primitives of A is the symbol

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