Battle of cognition: the future information-rich warfare and the mind of the commander

Battle of Cognition

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Praeger Security International Advisory Board Board Cochairs Loch K. Johnson, Regents Professor of Public and International Affairs, School of Public and International Affairs, University of Georgia (USA) Paul Wilkinson, Professor of International Relations and Chairman of the Advisory Board, Centre for the Study of Terrorism and Political Violence, University of St. Andrews (UK) Members Anthony H. Cordesman, Arleigh A. Burke Chair in Strategy, Center for Strategic and International Studies (USA) Thérèse Delpech, Director of Strategic Affairs, Atomic Energy Commission, and Senior Research Fellow, CERI (Fondation Nationale des Sciences Politiques), Paris (France) Sir Michael Howard, former Chichele Professor of the History of War and Regis Professor of Modern History, Oxford University, and Robert A. Lovett Professor of Military and Naval History, Yale University (UK) Lt. Gen. Claudia J. Kennedy, USA (Ret.), former Deputy Chief of Staff for Intelligence, Department of the Army (USA) Paul M. Kennedy, J. Richardson Dilworth Professor of History and Director, International Security Studies, Yale University (US.A) Robert J. O’Neill, former Chichele Professor of the History of War, All Souls College, Oxford University (Australia) Shibley Telhami, Anwar Sadat Chair for Peace and Development, Department of Government and Politics, University of Maryland (USA) Fareed Zakaria, Editor, Newsweek International (USA)

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Battle of Cognition T HE F UTURE I NFORMATION -R ICH W ARFARE AND THE M IND OF THE C OMMANDER Edited by Alexander Kott

PRAEGER SECURITY INTERNATIONAL Westport, Connecticut • London

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Library of Congress Cataloging-in-Publication Data Battle of cognition : the future information-rich warfare and the mind of the commander / edited by Alexander Kott. p. cm. Includes bibliographical references and index. ISBN 978–0–313–34995–9 (alk. paper) 1. Command and control systems. 2. Situational awareness. 3. Command of troops. I. Kott, Alexander. UB212.B37 2008 355.3'3041—dc22 2007037551 British Library Cataloguing in Publication Data is available. Copyright © 2008 by Greenwood Publishing Group All rights reserved. No portion of this book may be reproduced, by any process or technique, without the express written consent of the publisher. Library of Congress Catalog Card Number: 2007037551 ISBN-13: 978–0–313–34995–9 First published in 2008 Praeger Security International, 88 Post Road West, Westport, CT 06881 An imprint of Greenwood Publishing Group, Inc. www.praeger.com Printed in the United States of America

The paper used in this book complies with the Permanent Paper Standard issued by the National Information Standards Organization (Z39.48–1984). 10

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When referring to future, all names, characters, organizations, places and incidents featured in this publication are either the product of authors’ imagination or used fictitiously. Any resemblance to actual persons (living or dead), events, institutions or locales is coincidental.

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Contents

Introduction Alexander Kott 1

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Variables and Constants: How the Battle Command of Tomorrow Will Differ (or Not) from Today’s Richard Hart Sinnreich The Timeless Conditions of Battle The Changing Context of Command Key Command Tasks Recurring Command Dilemmas New Command Challenges Enhancing Future Battle Command

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A Journey into the Mind of Command: How DARPA and the Army Experimented with Command in Future Warfare Alexander Kott, Douglas J. Peters, and Stephen Riese A Battle of 2018 Network-Enabled Warfare The History of the MDC2 Program Experimental Testbed The Blue Command The Commander Support Environment A Typical Experiment A Typical Battle History Information Processing, Situation Awareness, and Battle Command

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New Tools of Command: A Detailed Look at the Technology That Helps Manage the Fog of War Richard J. Bormann Jr. The Architecture of the BCSE Warfighter’s Command Functions and Tools within CSE An Illustrative Scenario The Decision Support Framework

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Situation Awareness: A Key Cognitive Factor in Effectiveness of Battle Command Mica R. Endsley Challenges for SA in Command and Control System Design for SA in Command and Control SA Requirements Analysis SA-Oriented Design Principles SA Design Evaluation Shared SA in Team Operations SA in Distributed and Ad Hoc Teams

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The Hunt for Clues: How to Collect and Analyze Situation Awareness Data Douglas J. Peters, Stephen Riese, Gary Sauer, and Thomas Wilk Data Collection Situation Awareness—Technical Sensor Coverage Situation Awareness—Cognitive Battle Tempo Collaborative Events

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Making Sense of the Battlefield: Even with Powerful Tools, the Task Remains Difficult Stephen Riese, Douglas J. Peters, and Stephen Kirin Information Advantage Rules SA Is Hard to Maintain Gaps and Misinterpretations Common but Not Shared The Cognitive Load Experimental Design and Analysis

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Enabling Collaboration: Realizing the Collaborative Potential of Network-Enabled Command Gary L. Klein, Leonard Adelman, and Alexander Kott The Collaboration Evaluation Framework (CEF) Three Points of Impact Task Transmissions

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Contents Hierarchical Level and Information Abstraction Collaboration and Levels of Situation Awareness Types of Coordination and Collaborative Behaviors Types of Task Processes Type of Interdependence Task Environment Concept of Operations Applying the Collaboration Evaluation Framework Impact on Mission-Oriented Thinking High Cognitive Costs of Mutual Adjustments Collaboration in a Disrupted Command

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The Time to Decide: How Awareness and Collaboration Affect the Command Decision Making Douglas J. Peters, LeRoy A. Jackson, Jennifer K. Phillips, and Karol G. Ross Collecting the Data about Decision Making The Heavy Price of Information Addiction to Information The Dark Side of Collaboration Automation of Decisions The Forest and the Trees

Concluding Thoughts Alexander Kott The Tools of Network-Enabled Command The Challenges of Network-Enabled Command

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Appendix: Terms, Acronyms, and Abbreviations

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Acknowledgments

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Notes

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Index

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About the Contributors

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Introduction Alexander Kott

The impact of the information revolution on our society has been sudden, profound, and indisputable. The last couple of decades have seen a dramatic rise of new, powerful economic sectors dedicated to machines and processes for generation, transformation, distribution, and utilization of informational products. Computers and software, wired and wireless communication networks, autonomous machines, the proliferation of highly capable sensors—all these elements have transformed both daily lives and worldwide economies to an extent that would be difficult to fathom merely a generation ago. Warfare, inevitably, is among the human endeavors that have experienced the massive impact of the information revolution. Historically, warfare has been particularly dependent on, and influenced by, technology. From iron and bronze weapons to horse breeding and riding to sails and gunpowder to motor power and so on—the history of warfare is largely the story of some people creatively adapting (and some failing to adapt) their military cultures, institutions, and tactics to new waves of technology.1 Not surprisingly, since the beginning of the information revolution, military thinkers in the United States and elsewhere have been both analyzing and implementing the changes enabled and necessitated by the rapidly advancing information technologies.2 While some of these adjustments have rapidly entered military practice, others remain elusive even after long anticipation. Examples of military transformations engendered by the information revolution include some that are relatively inexpensive and benign.3 Others are ambitious, enormously expensive, and therefore often controversial. One effort in the latter category is the Future Combat System of the U.S. Army, a colossal program intended to build a highly networked system of new battle

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vehicles, combat robots, and human warriors.4 Thriving on its ability to obtain, communicate, understand, and use huge volumes of battle-related information, this system of humans and machines is to be highly transportable, agile, survivable, and lethal to the enemy, as compared to its industrial age ancestors. Disturbingly, the history of warfare also offers numerous lessons of counterproductive military adaptation to new technologies and even notorious cases of technological dead ends.5 Can an enormously ambitious transformational undertaking like the Future Combat System work as intended? It is a $160 billion question—that’s approximately how much taxpayer money the program is currently slated to consume.6 The program’s critics and advocates argue about the readiness of the intended technologies, whether such a force can help with counterinsurgency wars bedeviling the U.S. military, and how vulnerable (or not) the future light-armored combat vehicles might be in comparison with today’s predominant battle machines like the Abrams tank. Yet while many ponder the weighty matters of armor, few seem to worry about the gray matter—the mind of the commander, the place where all the information power of the new age is supposed to converge and to yield its mighty dividends. Consider that it is the human mind, particularly the minds of military commanders and their staffs, which remains the pinnacle and the ultimate consumer of all these enormously expanded flows of information. What if the true weak link of the information age force is not the hardware of machines, but the software of the human mind? And if so, could it be that the entire conceptual structure of the information revolution, at least as it applies to military affairs, is built on sand, on the notorious fickleness of human cognition? These are the questions that this book strives to examine. Looking at the command and control of information-rich warfare, we explore its potential new processes, techniques, and organizational structures. As we do so, we find reasons for both optimism and concerns about the limitations of human cognition and supporting technologies in commanding information age battles. Naturally, much of this book is about the technology that may enable new paradigms of battle command. Without such technology, as the reader will soon see, the new methods of battle command are neither feasible nor desirable. To underpin the new technology, this book must also address the science— theoretical and empirical—of the key processes occurring in battle command. Finally, because new ideas are hard to either explain or motivate without exploring their genesis, this book is also about the history of how we developed the concepts and technologies of the new battle command. In part, the roots of this work are in two programs conducted by the Defense Advanced Research Projects Agency (DARPA), the central research organization of the U.S. Department of Defense.7 DARPA is widely regarded as the world’s most ambitious, risk-taking, and largely effective research organization dedicated to military technologies. In the words of one writer, “America’s secret weapon today is not the stealth airplane or the Predator

Introduction

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but the agency that was responsible for their development (and much else besides)—The Defense Advanced Research Projects Agency.”8 Whatever the accolades, it was DARPA that by the year 2000 became increasingly concerned about the challenges of battle command in an information-rich, network-enabled military force. Urged by the energetic and visionary Lieutenant Colonel Gary Sauer, DARPA and the U.S. Army formed a joint development program, initially called the Future Combat System Command and Control (FCS C2).9 Gary Sauer joined DARPA, became the program manager of the program, and convinced two other talented military technologists—Maureen Molz, a senior engineering manager with the U.S. Army, and Lieutenant Colonel Robert Rasch—to join him. Together, they led the program through most of its life. Around 2003, the program was renamed Multicell and Dismounted Command and Control (MDC2)10 and continued into 2007. The products of the program included an unconventional, innovative approach and technology for battle command. While not representing the position of either the U.S. Army or DARPA, this book is based partly on the ideas, experiments, and lessons of that program.11 DRIVING FORCES Major innovations do not occur without both a push and a pull. A push is a set of factors that make a change possible. Often, the push is technological—a new invention or an advance in technology or a combination of new technologies that makes possible a capability that was previously unachievable. In the world of warfare, such a push implies that the potential opposing force may also avail itself of such technologies and capabilities, and therefore some counteraction must be considered. A pull is a set of factors that make a change desirable and even necessary. Commonly, such factors result from an evolution in the environments and opponents that the military is likely to face in the near future. Today’s shifts in battle command paradigms are enabled by several push factors such as smart precision weapons, unmanned platforms and sensors, ubiquitous networking, and intelligent decision aids. There are also powerful pull factors: the continuous trend toward the dispersion of forces, the need for lighter forces that can defeat a heavier opponent without entering into his direct fire range, and the dramatic increase in the volume of battlespace information. Later in this book, we discuss these topics in detail, but let us preview them briefly here. For example, the recent emergence and the rapid progress of unmanned platforms are nothing short of revolutionary. In our generation, we are observing the entry into the battlespace of an entirely new class of warriors: unmanned automated sensors, stationary and mobile, ground based and airborne; unmanned fire platforms, for both direct and indirect fires, capable of operating in the air and on the ground, large and small. It is difficult to

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compare this development with anything else that has ever occurred in the history of human warfare. These artificial warriors possess unique strengths and weaknesses. They can obtain and process far more information than a human being and yet are generally much less intelligent in accomplishing even seemingly simple tasks. They possess inhuman endurance, precision, strength, and “courage,” thereby offering the commander a yet unexplored range of new tactics. On the other hand, the unmanned, robotic platforms impose on their human commanders a great burden: monitoring and controlling the assets that for the foreseeable future will remain remarkably unintelligent as compared to human warriors. At the same time, affordable networking and computerization have brought an unprecedented ability to exchange large volumes of information at great speeds between both human and artificial warriors, both horizontally between peers and vertically between echelons. The implications of this development for the battle command are also drastic: the information flow rates as well as distances and node-to-node connectivity have grown by many orders of magnitude as compared to any time in the history of warfare. Many traditional limitations that used to constrain and shape the nature of the battle command, such as the hierarchical flow of information and control, are now open to rethinking. In addition to greatly improving the flow of information, the technology also helps make better use of the information. The ubiquitous presence of computers in the battlespace at all levels of command became a norm in the last 10–15 years and opened the door to the emergence and acceptance of various computerized aids: visualization of the situation, exchange and integration of information, course-of-action planning, logistics, and maneuver execution control. These aids are simultaneously multiplying the need for information flows and enabling the decision maker to deal effectively with the proliferation of information. They also bring new challenges by both reducing and increasing, in different ways, the fog of war. Unlike the push factors, the pull has more to do with the changing nature of the operational environment and opposing forces, although these are also driven to a large extent by technological and economic forces. For example, as recently as the 1980s, the U.S. military operated in a bipolar world, with a clearly defined primary potential opponent. The geographic places and the modes of likely confrontations were well understood. But then the collapse of the Soviet Union—partly due to the information revolution—shattered the clarity of threats faced by the U.S. military. Instead came a bewildering array of often unpredictable conflicts scattered worldwide. Without the well-defined expectations of where the next war may occur, the cold war approach of prepositioning U.S. forces at strategically located bases becomes impractical. Besides, without a massive and highly visible enemy like the Soviet Union, the U.S. public is less willing to pay for a large number of military personnel. This creates the need for ways to shuffle the limited

Introduction

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number of U.S. forces around the globe, from one conflagration to another, rapidly and efficiently. The force has to become lighter and easier to deploy to far-flung places. Its design, platforms, and weapons, and its command and control have to adapt to the new realities. In addition to helping undermine the Soviet Union and release a plethora of other evildoers, the information revolution has produced other unexpected ramifications. Advances in communications enable millions of Americans back home to watch the wars—and their often gory outcomes—literally as they unfold. The brutal emotional impact of real-time video beamed across the world from the battlefront has no precedents in human history. Besides, the new precision weapons, also engendered in part by advances in information technologies, lead the public to expect surgical strikes without unnecessary civilian deaths. With such images and expectations, the public’s tolerance for casualties among our own troops as well as among enemy civilians has diminished dramatically. Today, the so-called “CNN effect” imposes new pressures on a military commander: images of civilian casualties caused by a single stray bomb can produce enormous, strategically significant outrage around the world and in his12 own country. Somehow, the commander has to accomplish his mission under the constraint of a public demand for low-casualty warfare. In combination, these diverse driving forces have made a trend toward a new battle command both feasible and inevitable. THE TRIANGLE OF COGNITIVE PROCESSES Like any other real-world process, the battle command is driven by objective relations and quantitative dependencies within its process. To invent a new battle command, we had to attempt to understand, measure, and analyze such dependencies. Among the multiple interconnected phenomena of battle command, particularly salient are three processes: situation awareness, collaboration, and decision making (Figure 0.1). Decision making produces the ultimate product of the battle command: decisions. Depending on the echelon and role of the battle command element, a decision can range from applying a particular weapon to a particular target to a scheme of maneuver for an entire campaign. Accuracy and timeliness of decisions are the key measures of the quality of battle command. This quality is critically dependent on the extent to which the decision makers understand the situation and on the degree of cognitive load they experience. The situation awareness here is the process by which the decision makers absorb the available information (inevitably incomplete, delayed, and often erroneous) and attempt to form a correct picture of the events and forces in the battle. Better understanding leads to making better decisions. Unfortunately, arriving at a better situation awareness is an expensive process. It consumes the attention of the decision makers and imposes a greater cognitive load on them, which can in turn decrease the quality and timeliness of their decisions.

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Figure 1. Situation awareness, collaboration, and decision making are key phenomena of battle command.

Collaboration is a process in which decision makers exchange their decision options and their understanding of the situation in order to produce a better, more complete, consistent, and correct understanding of the situation and the ultimate decisions. Collaboration usually improves the situation awareness and the decision quality. However, when the cognitive load on the decision makers is high, as is so often the case in battle command, the process of collaboration can also become a fatal distraction. Thus, the three processes are intricately interconnected. They both support and impede each other. In this book, much of the discussion is organized around these three fundamental and closely linked processes. THE ROAD MAP OF THE BOOK We begin by exploring the nature of battle command in its historical context. Whether practiced by a tribal chieftain of antiquity or a captain of tomorrow who is surrounded by technology, most elements comprising the battle command are timeless and remain substantially invariant, as argued in chapter 1. The tasks of battle command, such as making decisions and communicating them effectively, and its profound dilemmas, such as prioritizing goals or dealing with noncombatants, are stubbornly resistant to changes in the technological and political environment of warfare. And yet, one must not overlook the profound ways in which these permanent elements of battle command manifest themselves and affect commanders differently depending on the technological evolution of war. In particular, the context of battle command does change in important ways, such as the continuing drastic increases of the last two centuries in the physical scope of the battlespace as well as in the structural complexity of military forces and systems. Together, contextual and technological changes introduce new command challenges, such as

Introduction

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greater agility and simultaneity of actions, as well as the demand for more precise, surgical operations. However, we stress, the most important constant of battle command is the commander himself, and a technological advance in this field can succeed only by matching the new technology to the intricate strengths and weaknesses of the human mind. Yet, it is a very tall order to match technology and the human mind. The complexities are immense, and the only effective techniques to deal with them are experimental—essentially trial-and-error methods. In chapter 2, we describe our approach to solving these challenges: the series of experiments that comprised the core of the MDC2 program, in which we explored various arrangements of human command cells and computer-based tools. This is the place where the setting of the military scenarios and the physical arrangements of our experiments are introduced. We describe the history of the program, the typical battles portrayed in the experiments, and the tools we built to help commanders fight the battles. Having briefly introduced the battle command tools constructed and explored in the course of our experiments, in chapter 3, we offer the technically minded reader a detour to explore the nuts and bolts of the tools. The chapter starts with the overarching architecture, continues into a mapping of the command functions and corresponding tools and then shows how they work together in an illustrative scenario, and concludes by explaining the underlying technology of the tools used in the experimental battle command support environment. With the profusion of functions, tools, and ramifications of battle command, one aspect—situation awareness—stands out as uniquely pervasive and influential. That is why the next several chapters focus almost exclusively on this all-important underpinning of battle command. Chapter 4 introduces the fundamentals of situation awareness, beginning with the definitions of situation awareness at several distinct levels and its exceptional significance to battle command. To provide value to commanders, the design of a battle command tool must pay careful attention to its ability to deliver situation awareness. The chapter discusses specific recommendations on how to meet such design objectives: the approach to requirements analysis, design, and evaluation of systems that support situation awareness. It also points out the serious limitations of our current understating of situation awareness, especially as it applies to command teams. Continuing the discussion of the theoretical foundations of situation awareness, in chapter 5 we describe our experimental approach to measuring and analyzing the processes by which warfighters develop situation awareness, the role of situation awareness in effective decision making, and its ultimate impact on the battle outcome. Our experimental findings highlighted situation awareness as the linchpin of the command process, as a key factor that determined the efficacy of all its elements—from sensor and asset control to decision quality and battle outcome. We explain how we gradually developed the methods of collecting the relevant data and measuring both the so-called

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technical situation awareness (what the technical systems make available to the human mind) and the cognitive situation awareness (what the human mind actually absorbs). The actual findings resulting from such data collection and analysis are the topic of chapter 6. We illustrate them with both data and examples. Some of the findings serve as possibly the first-ever quantitative validation of longstanding intuitive expectations of military practitioners. For example, we see quantitative evidence that the difference between situation awareness levels of the opponents is among the most influential factors governing the battle outcome. Other findings are far from obvious, perhaps counterintuitive, and even somewhat disturbing, such as the surprisingly large gap between the information made available to the command cell and its actual—and very often incorrect—perception of the situation. When a single mind has difficulties understanding a situation, the timehonored solution is to bring more minds to bear on the problem; collaboration always helps. Yes and no, says chapter 7. In fact, collaboration can be a double-edged sword; it can both help and hurt. Our findings on the role and value of computer-assisted collaboration on battle command are decidedly mixed. On one hand, we find that network-enabled command tools can result in remarkably effective cooperation of far dispersed forces, when they share information and resources in ways not imaginable without such networks and tools. On the other hand, we discover that common and well-accepted approaches to computer-assisted collaboration can be quite ineffective and even counterproductive. We also find that under some conditions, collaboration can reinforce incorrect conclusions and be outright dangerous. Having formed situation awareness, with or without the benefits of collaboration, the commander must make decisions. Although in our experiments we see numerous positive contributions of battle command tools to the commanders’ decision processes, in chapter 8 we elect to focus on the shortcomings. After all, the main objective of our experiments was to seek and iteratively correct such shortcomings. We found, not unexpectedly, that the major increase in information flows available to the commander comes at a price. Too often, for example, the richness and detail of the available information led a commander to chase relatively insignificant immediate actions while abrogating his responsibility for managing the big picture of the battle. The same abundance of easily accessed information often seduced a commander into delaying a decision while collecting yet more precise and reliable information. In the final chapter, we offer the reader our conclusions. There is a wide variation in our confidence in these conclusions: some are well supported by our research while others are rather conjectural. There are also several distinct perspectives covered in the conclusions. Some are focused on technical aspects of building and testing battle command technologies; others

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offer observations on the nature and practice of network-enabled warfare. Yet others talk about the ways to command a battle in such warfare. In all cases, the reader should be mindful that this volume is a work of multiple authors, and not every author agrees with every opinion expressed in this book. And, of course, the authors’ opinions do not represent those of their employers, DARPA, U.S. Army, or any agency of the U.S. government.

CHAPTER 1

Variables and Constants: How the Battle Command of Tomorrow Will Differ (or Not) from Today’s Richard Hart Sinnreich

To be a successful soldier, you must read history . . .Weapons change, but man, who uses them, changes not at all. To win battles, you do not beat the weapons—you beat the man. —George S. Patton

In May 1916, off Denmark’s Jutland Peninsula, a naval battle took place for which Great Britain’s Royal Navy had been preparing for more than a decade and which it had sought in vain to bring about since the beginning of the World War I. For a day and a night, British admiral Sir John Jellicoe’s Grand Fleet sparred in a roar of guns and hiss of torpedoes with German admiral Reinhard Sheer’s High Seas Fleet. In risking battle against his numerically superior adversary, Sheer’s intention was to lure a detachment of British warships out of port and destroy it in detail, whittling away the Royal Navy’s tonnage advantage. Threatening British shipping navigating the straits between Denmark and Norway, he hoped, would entice Jellicoe’s fast but lightly armored battlecruisers into an ambush by the more powerful battleships of the High Seas Fleet. Instead, warned by intelligence of the intended German sortie, Jellicoe took his entire fleet to sea even before Sheer weighed anchor. Misled by a misunderstood radio intercept, however, Jellicoe, like Sheer, expected to face only his enemy’s battle cruisers. Accordingly, on the afternoon of May 31, distant from the rest of the British fleet by more than 50 miles, Jellicoe’s battlecruisers commanded by Vice Admiral David Beatty found themselves engaging their German counterparts on a course that unchanged would have taken them directly under the guns of Sheer’s battleships. Only a last-minute warning by one of Beatty’s light cruisers alerted him in time to reverse course.

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What the goddess of fortune gave with one hand, however, she took back with the other. Thanks to signaling problems and its commander’s reluctance to act without orders, Beatty’s most powerful squadron lagged behind, depriving him of its firepower for crucial minutes. That and poor gunnery cost the British two battle cruisers. Fortunately, Beatty’s detached Third Battlecruiser Squadron arrived just in time to even the odds and allow Jellicoe to deploy his battleships into fighting formation before the arrival of Sheer’s main body. What should have followed was the decisive clash of battle fleets for which both navies had been built. Instead, astonished to find himself confronting not just battle cruisers, but rather the entire Grand Fleet, Sheer turned his ships on their heels and fled, a maneuver that the British at first failed to detect, then failed to exploit. Thirty minutes later, however, Sheer unaccountably reversed course once again, in the process exposing his ships in column to the fire of Jellicoe’s battle line. Awakening to his error, he then turned back a second time, covered by his battle cruisers and torpedo attacks. Again Jellicoe failed to pursue, and with night falling, the two fleets separated. Both fleets now altered course, Jellicoe hoping to intercept the Germans at daybreak, Sheer seeking only to evade further action and return to port. In the darkness their courses converged, the Germans actually passing through the rear of the British fleet. The British warships detecting them, however, neither engaged them nor informed Jellicoe, who thus remained ignorant of their proximity. At dawn the fleets were miles apart, and the Royal Navy had lost its golden opportunity to destroy its German rival once and for all.1 Tactically, honors were nearly even. The British lost three modern battle cruisers and three older cruisers, the Germans one battle cruiser and four light cruisers, both in addition to smaller vessels. Psychologically, however, Jutland was an immense disappointment to Britain. Unchallenged at sea for more than a century, the vaunted Royal Navy had failed in a head-to-head encounter to destroy the smaller fleet of what amounted to an upstart naval power. In a penetrating examination of the evolution of the Royal Navy between Trafalgar and Jutland, British historian Andrew Gordon traced the factors that led to that embarrassing result. The most important was a pervasive change in the Royal Navy’s approach to battle command, reflecting above all the impact on the Navy’s institutional culture and leadership of revolutionary technological change during a century without major naval conflict.2 In the process, the initiative and audacity that had won Britain command of the sea surrendered to a centralized and mechanical battle-command system that proved slow to recognize opportunity and unable to exploit it. At Jutland as it has so often in the history of war, numerical superiority alone proved unable to compensate for that deficiency. A recent U.S. Army paper defines battle command succinctly as “the art and science of applying leadership and decision making to achieve mission success.”3 The elements of this definition deserve attention.

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To begin with, the definition asserts that battle command is both an art and a science. The former is a creative activity not susceptible to objective confirmation or prediction, the latter a process of systematic discovery that ultimately must satisfy both requirements. By the definition, battle command somehow must reconcile these incompatible qualities. Second, the definition implies a predetermined military objective. Battle command seeks mission success. But just what that entails must be specified elsewhere. So described, battle command differs markedly from strategic and even operational direction, in which whether and why to accept or decline battle is a preliminary and often difficult decision. Finally, battle command is asserted to comprise two separate, albeit related functions—leadership and decision making. Both are ultimately solitary activities. They differ in that respect from control, which significantly appears nowhere in the definition. Control, the application of regulation and correction, can be and typically is a corporate process, and, as with defining mission success, apparently is something distinguishable from command, a view tacitly reflected in the common pairing of the two in military terminology. Together, these elements of the definition describe an idiosyncratic but nonetheless reproducible activity. Reduced to its essentials, the definition portrays battle command as a creative process constrained to a predefined objective and conforming, in some measure at least, to confirmable principles the application of which can produce predictable results. Indeed, that is the way battle command is taught in most professional military schools. History tells a rather different story. Examining the achievements of successful battle captains, one can’t avoid concluding that much more is going on than just the application, however artful, of reliable principles and practices. Successful combat commanders display an almost uncanny ability to sense the battlespace, anticipate their enemies’ behavior, and create and exploit opportunities where none previously were visible. The late Air Force colonel John Boyd tried to capture that special talent in his now-famous “OODA Loop”—Observe, Orient, Decide, Act.4 Like the fighter pilots from whom Boyd derived his theory, successful battle commanders routinely execute that cycle more rapidly and effectively than their adversaries, gaining a progressively greater advantage with each successive engagement. As Boyd himself recognized, however, the problem confronting the battle commander differs in several crucial respects from that facing the fighter pilot. The difference affects all four elements of the OODA loop, but especially the last. For, whereas only his own reflexes and tolerances, the capabilities of his aircraft, and the laws of physics constrain the fighter pilot’s ability to act, the battle commander must act through others. The translation from decision to action thus is much less straightforward, more vulnerable to miscommunication or misperception, and above all, more sensitive to human error and plain bad luck.

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THE TIMELESS CONDITIONS OF BATTLE That is true to some degree of any collective human enterprise, but especially of war. No one understood that better than Carl von Clausewitz, who highlighted the powerful impact on commanders of what he called the “realms” of war: danger, physical exertion and suffering, uncertainty, and chance.5 As the Battle of Jutland confirmed, those conditions are by no means unique to war on land. But it is in land combat that they tend to appear in their most varied and visible forms.

Danger Danger affects battle command on several levels. At the most basic, it requires those at the sharp edge to suppress every instinct of self-preservation for purposes that rarely will be as visible to them as to their leaders. Throughout history, a focal purpose of military socialization and discipline has simply been to inculcate resistance to fear.6 Even among trained and disciplined soldiers, however, that resistance has limits. As every experienced commander knows, the well of courage isn’t bottomless. Today, when democratic societies, at least, no longer will tolerate the harsh discipline that stiffened Frederick’s lines at Leuthen or Wellington’s squares at Waterloo, fighting men and women must be convinced in other ways to expose themselves voluntarily to death or serious injury.7 As some commanders relearned painfully during the Vietnam War, nothing can more easily shatter that conviction than suspicion that their leaders don’t know what they’re doing. Battle command thus directly affects the willingness of soldiers to fight. Danger also affects human perception. Modern sensors have by no means diminished soldiers’ propensity under threat to misperceive, exaggerate, and fantasize. Clausewitz’s notorious “fog” of war is much less often the product of an outright lack of information than of misreading the information available. Like desert heat, danger tends to distort the vision and generate false images. In the confusion of battle, Clausewitz commented, “it is the exceptional man who keeps his powers of quick decision intact.”8 More information alone therefore is no guarantee of effective battle command. Instead, what matters more is the judgment through which that information is filtered and translated into knowledge. Finally, danger affects the commander directly, less often in terms of physical risk than through the dilemmas it poses. Choices that may seem obvious in hindsight rarely present themselves so clearly at the moment of decision. In battle, every choice is fraught with peril. At Jutland, as Winston Churchill justly acknowledged, Jellicoe was “the only man on either side who could lose the war in an afternoon.”9 For Jellicoe, therefore, the perceived cost of defeat more than counterbalanced the will to win. No battle-command system can relieve the commander of the moral burden such dilemmas entail.

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Physical Exertion and Suffering The psychological and moral effects of danger only are aggravated by the sheer physical demands on soldiers and leaders alike. Ground combat is arduous and exhausting. The ground itself is an unremitting obstacle, never mind weather and the enemy. Brain and muscles tire, food and sleep are erratic and often insufficient, and every successive casualty compounds the psychological pressure on the survivors. Machines ease but by no means eliminate these hardships. In return, maintaining the machines adds its own burdens. In the nature of ground combat, circumstances only rarely allow soldiers to turn their fighting platforms over to others to arm, fuel, and maintain while they themselves recuperate. The condition of weapons alone, therefore, is no guarantee of the condition of those manning them, and the commander who measures the combat effectiveness of his unit solely by the preparedness of his weapons is asking for trouble. Uncertainty In 1927, German physicist Werner Heisenberg proposed to his colleagues that perfect knowledge on a quantum scale was unattainable. Skeptics objected that this merely reflected the limits of measurement. But Heisenberg was able to demonstrate that imperfect knowledge is built into the very fabric of the subatomic universe. A century earlier, Clausewitz reached a similar conclusion about war. “War,” he wrote, “is the realm of uncertainty; three-quarters of the factors on which action in war is based are wrapped in a fog of greater or lesser uncertainty.”10 As in physics, that uncertainty is in great measure insensitive to the means by which information is acquired and transmitted. At Jutland, the same intelligence system both alerted Jellicoe and misinformed him. Modern technology certainly has vastly improved armies’ abilities to acquire and share information. Netted communications, global positioning, enhanced sensors, and overhead platforms all have significantly increased the information available to commanders. And yet, as recent conflicts reveal only too clearly, uncertainty persists. Enemy and friendly units appear where they have no business being. Targets turn out to be not what they seemed. And commanders with abundant communications still manage to misread the battlespace, the enemy, and each other.11 Such difficulties have plagued armies and navies since the dawn of organized warfare. While technology may be able to diminish them, there is no convincing evidence to date that it ever will banish them entirely. Chance Finally, chance or, more broadly, what Clausewitz called “friction,” dominates every battlefield. “Action in war,” he wrote, “is like movement in a resistant element. Just as the simplest and most natural of movements, walking,

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cannot easily be performed in water, so in war it is difficult for normal efforts to achieve even moderate results.”12 In military terms, friction denotes the unforeseeable accidents, incidents, delays, errors, misunderstandings, misjudgments, and freaks of nature that perversely intervene between plan and execution. It is famously reflected in the proverb that begins “For want of a nail, a shoe was lost” and ends with “A kingdom was lost, and all for want of a nail.” At Jutland, friction bedeviled both sides from the battle’s beginning to its end. Military and naval operations are by no means uniquely susceptible to this problem. In a recent book, surgeon and medical columnist Atul Gawande insightfully described the myriad ways in which unforeseen problems can complicate even routine surgical procedures.13 The most careful planning cannot altogether prevent such complications. Accordingly, for the battle commander as for the surgeon, coping with the unforeseen is an unwelcome but also inescapable requirement. THE CHANGING CONTEXT OF COMMAND While the preceding challenges to effective battle command are timeless, others have changed as war itself has changed. Developments in technology, military organization, and the battlefield environment all have significantly altered the exercise of command. In land warfare, three developments especially have influenced that evolution. Battlefield Enlargement The first is the progressive enlargement of the battlefield and the increasing number and dispersal of fighting formations. Until very recently in historical terms, army commanders could and did exercise tactical command directly, basing their decisions on what they could see with their own eyes, transmitting orders verbally or at worst by messenger, and exerting leadership by personal example. To an Alexander, Julius Caesar, or Gustavus Adolphus, the modern injunction to “lead from the front” would have been superfluous. Throughout much of military history, effective command could be exercised nowhere else. The annals of warfare are replete with examples of battles won through the commander’s direct personal supervision or lost through his incapacitation, capture, or flight. In the mid-nineteenth century, that began to change. At Waterloo in 1815, Wellington and Napoleon still could exercise direct tactical command, observing virtually the entire battlefield and moving their units like chess pieces. Nothing more vividly reveals the dependence of both armies on that personal involvement than the errors committed by Bonaparte’s subordinates during his brief infirmity in midbattle and Wellington’s dramatic personal intervention to mass his musketry against the Old Guard at the battle’s climax.

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Fifty years later in the wilderness, neither Ulysses S. Grant nor Robert E. Lee could even begin to exert similar personal direction. Not just the difficulty of the terrain but also the sheer scale of the battlefield and the dispersal of units made continuous observation and influence virtually impossible. Both commanders could and did intervene at a few crucial moments. In large measure, however, having brought their forces to battle, they were compelled to leave its tactical direction in the hands of their subordinates.14 Organizational Complexity During the next half-century, as weapons diversified and their lethal reach expanded, the enlargement of the battlefield was paralleled by a similar increase in organizational complexity. The late nineteenth century saw the multiplication of command echelons, emergence of battle staffs, accelerating functional specialization, and the introduction of new technologies from motorization to electronic communications. Reflecting on these developments, General Alfred von Schlieffen, chief of the German general staff from 1891 to 1906, predicted that future commanders endowed with modern communications no longer would lead from the front, but instead would direct operations by telephone and telegraph from distant headquarters where, “seated in a comfortable chair, in front of a large desk, the Modern Alexander will have the entire battlefield under his eyes, on a map.”15 His vision proved far too sanguine. When the World War I erupted in 1914, senior commanders found themselves little more able to exert direct influence on the battle than their predecessors of half a century earlier. Instead, wedded to centralized direction on battlefields the size and complexity of which far outstripped commanders’ abilities to sense, assess, and communicate, armies and their commanders collided like rudderless ships. In the end, the much criticized linearity characteristic of so many World War I battles was not simply a product of dim-witted leadership, but instead reflected as much or more the sheer difficulty of reconciling central tactical direction with decentralized execution by commanders and subordinates equally unprepared by doctrine or training for its demands. Only toward the end of the war did the Germans, acknowledging the problem, at last begin to develop tactics relying on more decentralized command arrangements.16 Meanwhile, the twentieth century also saw a steep increase in the number, types, and effects of weapons, and with it, problems in harmonizing their employment. Until the 1904 Russo-Japanese War, for example, artillery usually positioned forward and fired over open sights. Even then, coordination with supported infantry was anything but perfect, as the failure of Confederate artillery to suppress federal defenses on Gettysburg’s final day revealed. The withdrawal of artillery into defilade at the beginning of the twentieth century merely compounded the problem. Throughout World War I, telephonic communications routinely proved inadequate to coordinate fire and

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movement. Unable to communicate reliably in real time, commanders were compelled to fall back on elaborately planned and tightly scheduled bombardments that once begun could be modified only with great difficulty. Such fires sometimes proved nearly as deadly to supported infantry as to the enemy. So too the integration of cavalry with infantry, a long-standing problem that continued to plague commanders as late as the Boer War.17 The tank, when it eventually replaced horse cavalry, proved no easier to tame. Absent portable communications, tank-infantry coordination depended on visual signals. The result was to slow armor to the pace of its accompanying infantry, forfeiting even the modest increase in tempo offered by early tank models. Still more was that true of early air operations, coordination of which with ground operations remained impractical until the development and fielding of reliable air–ground radio communications. By World War II, many of these technical obstacles had been overcome, but only at the price of a quantum increase in the complexity besetting tactical commanders. Among the major belligerents, only Germany really had begun to address this problem when the war began.18 Other armies including our own learned more expensively on the battlefield. Even then, combined arms integration remained uneven throughout the war. Multiplication of Domains The final key development affecting battle command, barely glimpsed in World War I but emerging full-blown in World War II, was the transmutation of a two-dimensional battlefield into a multidimensional battlespace, in which maneuver, fires, aircraft, and electronics all found themselves competing for command attention. As the complexity of the command problem increased, the very decentralization essential to cope with battle’s increased scale and fluidity found itself in competition with the need to synchronize domains, preclude their mutual interference, and achieve economy of force. Throughout World War II, and in both major theaters, tactical integration of land, sea, and especially air capabilities prompted repeated disputes, some of which persist today. For example, the Fire Support Coordination Line, until recently the focus of bitter doctrinal debate between U.S. air and ground forces, originated as the Bomb Line, a safety measure established in mid-1944 in response to a much-publicized episode of air–ground fratricide. Conflicts since then, including our own recent engagements in Afghanistan and Iraq, reveal that this dilemma has by no means been resolved. On the contrary, the proliferation of long-range missiles, fixed and rotary wing aircraft, and unmanned aerial vehicles only has aggravated it. Combined with an enlarged irregular threat and battlefield transparency that increasingly subjects even minor tactical miscues to immediate public scrutiny, the result has been to impose unprecedented pressures on tactical commanders at every level.

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Battle of Cognition

KEY COMMAND TASKS More than a century ago, Ulysses S. Grant insisted that “The art of war is simple enough. Find out where your enemy is. Get at him as soon as you can. Strike him as hard as you can, and keep moving.”19 Much later, British Field Marshal William Slim described the battle commander’s challenge in almost identical language. Quoting a former NCO instructor, he wrote, “There’s only one principle of war, and that’s this: Hit the other fellow as quick as you can and as hard as you can, where it hurts him most, when he ain’t looking.”20 Powerful as these injunctions are, they also conceal some practical dilemmas. Finding out where the enemy is takes time and so also does deciding what will hurt him the most. Both compete with getting at him as quickly as possible “when he ain’t looking.” Today far more than in Grant’s day or even Slim’s, striking him hard may require bringing to bear capabilities distant from the fight and which often are not under the immediate direction of the commander seeking to apply them. Ditto for finishing the fight and moving on. Above all, each of these tasks increasingly must be accomplished by smaller and more widely dispersed units. Reconciling the increased complexity of battle command with that devolution of responsibility downward is perhaps today’s preeminent conceptual as well as technical military problem. As a recent U.S. Army capstone concept declared, “The conduct of simultaneous, high-tempo, non-contiguous operations executed by Future Force formations at varying levels of modernization and distributed broadly across the area of operations will place very high demands on Future Force leaders with respect to both the art and science of command.”21 It was precisely to help address that requirement that DARPA and the U.S. Army launched the Multicell and Dismounted Command and Control (MDC2) project. Before examining the project’s conduct and insights in the chapters that follow, it may be worth reviewing the recurring tasks confronting any tactical commander and their susceptibility to assistance through networked automation. Diagnosing The first and in some ways most important requirement of effective battle command is accurately reading the battlefield. Terrain appreciation, knowledge of friendly unit locations and conditions, and intelligence about the enemy all contribute. But all are essentially static, whereas battle itself is dynamic. War, Clausewitz reminds us, “is not the action of a living force upon a lifeless mass . . . but always the collision of two living forces.”22 Accordingly, successful battle command presumes the ability to infer dynamic patterns from fragmentary and inevitably incomplete information, sense them as they shift, and project them forward in time. How far forward and in what detail will vary with the situation and level of command, but not the basic requirement.

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Moreover, because such inferred patterns inherently are hypothetical, they must be tested repeatedly, to the point where the commander may have to take risk just to confirm them. The extent of that risk will vary, but it rarely will be wholly absent. Not the least difficult command dilemma is deciding how much risk to accept simply in order to learn. Networked automation by itself can’t eliminate that dilemma, but it does promise to ease it. By displaying tactical information in ways that assist commanders to infer patterns, associating reports that might otherwise seem unrelated and alerting commanders in a timely way to information tending to alter or invalidate their forecasts, automation can enhance and accelerate diagnosis, and with it the entire decision-making process. Planning Given accurate diagnosis, battle planning largely is a matter of problem solving. For the tactical commander unlike his superiors, the mission typically is prescribed, along with the constraints within which it must be pursued and the assets expected to be available to accomplish it. While that makes tactical planning simpler than strategic or operational planning in one respect, in another it is more complicated, for battles tend to be much more volatile than the strategic and operational conditions that prompt them. Prussian general Helmuth von Moltke’s much quoted warning that “no plan extends with any degree of assurance beyond the first encounter with the enemy’s main force” acknowledged this volatility.23 For the tactical commander, therefore, planning is less a matter of devising a template with which to guide the entire conduct of the battle than of arranging resources to begin it advantageously and retain that advantage as it evolves. In the end, the litmus test of battle planning is not how perfectly it anticipates events, but instead how well it promotes rapid and effective adjustment to them as they occur. Obviously, the more complete and reliable the information on which to base planning, the better. But because battle is a two-sided contest in which time plays no favorites, deferring engagement in the hope of acquiring better information easily can become self-defeating. One of the central challenges of battle command is deciding when such delay is more likely to increase than to diminish uncertainty and its associated risks. Above all, like diagnosis, planning to be useful must be continuous. At higher levels of command, additional personnel are available for that purpose. Smaller formations enjoy no such luxury and must plan with the same personnel who execute. Enabling such units to do so more rapidly and effectively is among the more important potential contributions of networked automation. Deciding If battle planning is largely problem solving, decisions are the mechanisms through which solutions are translated into intentions. In battle against a

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competent enemy, however, solutions rarely are self-evident and even less often final. Moltke exaggerated only slightly in remarking to his staff that given three possible courses of action, the enemy almost invariably could be counted on to choose the fourth.24 A great deal has been written about the decision-making process, but in the end much about it remains obscure, which is one reason efforts to date to automate it have made relatively modest progress. In the military, early attempts to apply artificial intelligence to tactical decision making have not fared well. TacFire, for example, the U.S. Army’s first true automated artillery fire direction system, became so notorious for misdirecting fires and diminishing artillery’s responsiveness that gunners eventually began turning the system off after using it to help generate their initial fire plans. If automation performed so poorly in a relatively quantifiable matter such as fire distribution, how well would it be likely to satisfy the significantly more complex decision-making requirements of battle command? Instead, automation is much more likely to be successful in assisting decision making than in replicating it. First, in addition to facilitating more rapid and effective diagnosis, automation may help the commander judge more accurately the time-space implications of choosing a particular course of action. Since the enemy has a vote, that estimate always will be imperfect. But given accurate information on the terrain and friendly capabilities, automation can help reduce the variables with which tactical decision making must deal. Second, automation can help trigger decision making by alerting the commander in a timely way to the occurrence of events likely to require a modification of his intentions. As with projecting courses of action, such triggers are likely to be imperfect and a prudent commander will avoid becoming overreliant on them. But used judiciously, they can enhance the commander’s sensitivity to changing circumstances. Finally, automation may allow some decisions to be prespecified. It thus may allow a more prompt reaction to certain events. For example, detection of an air-defense threat to a critical airborne sensor might automatically trigger counterfire or the movement of the sensor out of the threatened airspace. Or the detection of an enemy force on an open flank might automatically generate a warning to the nearest friendly unit. As TacFire’s example revealed, such a capability must be used with caution. But the potential is there. Delegating Many years ago, a respected senior U.S. Army officer became widely known for the admonition that “Only those things are done well that the boss checks.” Whatever the case for that viewpoint in preparing for battle, it invites failure once the fight begins. To command effectively in modern battle is to delegate, and delegation without discretion is meaningless. In Vietnam, the attempt of progressively senior commanders to dictate the actions of the same engaged unit from command helicopters orbiting over

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the unit and each other proved as dysfunctional as it would be infeasible in a more distributed battle.25 Unfortunately, as communications expand and battlefield transparency increases, the temptation to centralize direction can be overwhelming. Its invariable result is to submerge the commander in tactical detail while obscuring the battle’s overall pattern. History is strewn with the bones of armies whose commanders allowed themselves to become fixated on only one piece of the battle. Avoiding that and delegating effectively requires above all mutual confidence between commander and subordinate in the former’s intentions and the latter’s discretion. Some of that can be established in planning, but doctrine and training are far more important. Such mutual confidence is the more essential because commander and subordinates rarely will perceive the battle identically. Even with reliable communications and technical aids such as Blue force tracking systems, each soldier’s and leader’s view will be conditioned by his or her immediate surroundings and psychological makeup. The same information thus rarely will be processed mentally in exactly the same way. Instead, the battle commander must be able to rely on subordinates’ understanding of his or her intentions and their ability and willingness to act as the commander would in their place in the circumstances confronting them. Reflecting precisely that experience during Operation Iraqi Freedom, one rifle company commander later recalled “At one point, I had five separate elements, with four of them in contact, and I thought I was losing control . . . In slow motion, I started to realize that every single element was doing exactly what I would have told them to do if I was standing there next to them.”26 Here too, networked automation can help, provided it is used with selfdiscipline to refine and enhance communication of the commander’s intentions rather than to hamstring subordinates with detailed orders that may be utterly inappropriate to the conditions they face. In this area more than any other, networked automation easily can be a two-edged sword, and how it is used finally will determine whether it enhances or hinders its users’ tactical performance. Synchronizing Army Field Manual 3.0, Operations, defines synchronizing as the process of “arranging activities in time, space, and purpose to mass maximum relative combat power at a decisive place and time.”27 The need to synchronize goes back a very long way, ultimately to the first moment when hand and projectile weapons appeared together on the battlefield. The Roman javelin, for example, temporarily could deprive its targets of the use of their shields, but unless the unit after casting its javelins followed up quickly with hand-to-hand engagement, that momentary advantage easily could be lost. Since then, synchronizing has become more complicated with every improvement in weapons technology and, more recently, the expansion of

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Battle of Cognition

battle’s domains. In combining arms and services, the most valuable benefits accrue from complementary rather than just additive effects. As with combining javelin and sword, however, complementarity is sensitive to timing. Fires delivered too early may forfeit their utility in allowing unhindered maneuver. Sensors shifted too late may deprive a moving formation of the early warning that its deployment assumed. The retention of a mobile reserve may be pointless if the routes by which it might have to be committed weren’t earlier reconnoitered and cleared. In this area more than any other, dispersal and its accompanying devolution of tactical responsibility downward have radically increased the burden on commanders. Platoons and companies routinely must be able to synchronize activities and effects previously managed by their parent formations. While that burden occasionally may be lessened by the direct intervention of the higher commander, such intervention desirably should be infrequent. By allowing the virtual rehearsal of activities before the fight begins and the adjustment of their relative timing as it proceeds, automation can ease the synchronization burden. In the best case, by enhancing common situation awareness among the combining activities, networked automation may increase subordinates’ ability to self-synchronize, diminishing the need—and also the temptation—for the commander to intervene directly. Communicating About the importance to battle command of reliable communications, little need be said. Whether to receive and disseminate information, transmit orders and intentions, or synchronize activities, the commander must communicate. At Jutland, erratic communications contributed materially to the failure to force the German fleet to battle. But even perfect connectivity is no guarantee of effective communication. At Balaclava in the Crimea on October 25, 1854, a scribbled order to the British light cavalry brigade to “prevent the enemy carrying away the guns” produced one of history’s most celebrated blunders. To the sender on the heights, the order was perfectly clear. To its recipient in the valley below, it was utterly opaque.28 Military history is replete with such episodes. Automation is no panacea, but it can help make such disconnects less likely. Simply the ability to transmit graphics quickly and clearly can diminish if not prevent altogether the sort of perceptual divergence that sacrificed men so uselessly albeit gallantly at Balaclava. At the same time, in communicating as in delegating, automation misused can turn on its owners. As bandwidth increases, so too does the potential to communicate too much information. During the invasion of Iraq in March 2003, a senior U.S. intelligence officer complained wryly that his headquarters was awash in information it was utterly unable to process. The more comprehensively automation is networked downward, the greater the risk of similar information overload on smaller units even less capable of coping with it.

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Enhancing communications through networking thus requires not only accelerating the flow of information, but also refining what information is communicated, to whom, at what rate, and in what form. In the end, the acid test of effective communication, like that of a legal contract, is a meeting of minds, and networked automation’s contribution to battle command will be determined largely by whether it facilitates or impedes that encounter. Motivating One of Bill Mauldin’s wonderful World War II cartoons has Willie and Joe crouching behind a bush while a general standing near their outpost blithely surveys the battlefield. “Sir,” grumbles Willie, “do ya hafta draw fire while yer inspirin’ us?”29 Front line humor aside, not least of the command challenges associated with battlefield enlargement is how much harder it has become for commanders to make their personal presence felt. At Waterloo, Wellington could stand in his stirrups and wave his hat and the gesture be seen by half the men under his command. During the recent fighting in Fallujah, a battalion commander would be lucky to connect directly with more than a few of his men. Some may argue that in modern war, such direct personal influence is overrated. An incident during Operation Iraqi Freedom suggests otherwise. As the 101st Airborne attacked through An Najaf, several senior officers including the corps and division commanders assembled near a road intersection to confer. Ignoring nearby incoming mortar rounds, they continued their discussion until interrupted by small arms fire, upon which they immediately moved toward the source of the firing. No one was hurt, but word of their leaders’ coolness and audacity spread quickly among the troops, boosting morale throughout the corps.30 Moreover, motivating involves more than just inspiring. On a dispersed battlefield, the ability of any subordinate unit to gauge the impact of its behavior on the fight overall is intrinsically limited. When minutes count, one unit’s failure to act promptly by reason of wariness or weariness may mean the difference between victory and defeat. Injecting a sense of urgency when necessary is a vital command obligation, and sometimes only the commander’s personal presence will suffice. Commanders from George Washington to George Patton were renowned for appearing, unlooked for, at critical times and places. While the commander can’t be everywhere, in short, he must be able at need to go wherever his personal presence can make a difference without losing his grip on the battle overall. One of the most important prospective benefits of networked automation is to unleash the commander from his headquarters without depriving him of its information resources, thus helping to diminish the tension between the commander’s need to maintain his perspective on the battle and his ability to exert personal influence where necessary.

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RECURRING COMMAND DILEMMAS “Everything in war is very simple,” Clausewitz commented, “but the simplest thing is difficult.”31 While battle command certainly becomes more complicated with increasing responsibility, some recurring dilemmas are almost insensitive to seniority. Any command system will be judged at least partly by how well it assists commanders in resolving them. Prioritizing Requirements Like other enterprises, war is subject to economic imperatives, requiring commanders to allocate finite resources among competing requirements.32 Given battle’s uncertainties, however, doing so is especially difficult for the tactical commander. In effect, it requires him despite incomplete and transitory information to prejudge which resource commitments will prove most important in accomplishing the mission. Between friction and the enemy, that forecast rarely will be perfect. Prioritization thus requires balancing economy of force with elasticity. Ignoring the first risks wasting assets. Ignoring the second risks inability to recover from surprise. The smaller the unit, the harder it is to reconcile these competing requirements. Modern mobility systems together with more flexible tactical organizations have greatly improved commanders’ ability to shift assets around the battlefield. Even so, reallocation can’t always be counted on to correct an error in prioritization.33 In battle much more than in other activities, retasking tends to be difficult and dangerous. Every major readjustment risks confusion and delay, especially inasmuch as it is likely to be needed most urgently precisely when least convenient to execute. One solution always has been to withhold some assets for later commitment without disturbing those already committed. While appropriate for larger formations, however, retaining such a reserve is less feasible the smaller the unit. And while it is less essential when adjacent units are physically close enough to furnish each other assistance at need, the more dispersed the force, clearly, the less reliable and responsive such mutual support. Networked automation can help diminish the prioritization dilemma in two ways. First, by involving subordinate units directly and concurrently in examining the implications of prioritization alternatives, it may surface gaps in those arrangements that otherwise would be missed. At worst, by helping forecast contingencies that might require retasking, it can enable the affected units to consider in advance how they would mutually adjust. Second, by helping to track the shifting spatial relationships among subordinate units, networked automation can alert the commander to a developing situation that may obviate that contingency planning, allowing him in a timely way either to alter his intentions or seek additional support from higher echelons. The latter may be especially important, since, like internal retasking, obtaining additional support from higher may take time.

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Judging Timing “War,” Clausewitz declared, “is nothing but a duel on a larger scale.”34 It was a peculiarly apt analogy, evoking the flickering weave of blades as duelists feint, attack, parry, and riposte, each seeking to preempt the adversary’s corresponding action. For the fencer, though, timing merely is a matter of alertness and reflexes. Applying the analogy to battle, one should try to visualize the same encounter taking place on the uneven bottom of a cloudy pond, in which, moreover, each combatant is wired to a block of concrete just small enough to be dragged with difficulty. Deciding when to engage, change direction, commit a reserve, reallocate assets, call for additional support, or attempt to disengage are among the battle commander’s toughest questions. “Ask me for anything but time,” Napoleon enjoined a subordinate, and with reason. In battle, minutes may mean the difference between success and disaster. At Jutland, for example, only a few minutes’ delay in reversing course might well have cost the Beatty the rest of his battle cruisers or Sheer his entire fleet. Describing his brigade’s attack on Objective SAINTS south of Baghdad on April 3, 2003, the commander of Third Infantry Division’s Second Brigade Combat Team recalled, “At 3 in the morning, there was only one battalion ready. [I] made the decision to go without the entire brigade consolidated. The intelligence we had received said the Hammurabi [Division] was repositioning south to take SAINTS and the airport ahead of us so we didn’t have the freedom to wait. It was a classic commander’s dilemma.”35 Absent perfect intelligence and equally perfect foresight, nothing can eliminate such dilemmas, but there are ways to mitigate them. Perhaps the most important is simply to pay close attention to the locations and conditions of key assets. The more this can be managed without distracting subordinates with repetitive reporting requirements, the better. Blue force tracking already has contributed materially to this process, and more comprehensive networking can further enhance it. Tracking enemy movements and strengths is much harder, and the smaller and less readily identifiable the enemy forces of interest, the greater the challenge. Discussing his efforts to template Iraqi forces defending the Karbala Gap, one brigade intelligence officer recalled “huge disconnects between the CFLCC C2, the corps G2, and the division G2 on the enemy picture. One level had two battalions in the gap, while another level had one battalion in the west and two battalions east of the Euphrates . . . One echelon assessed a maneuver defense from Karbala with one battalion in the gap, while another had the enemy defending from its garrison and controlling bridges, and a third echelon had the enemy defending bridges from the eastern side.”36 Intelligence was even less able to detect and track the Iraqi irregulars who proved the most persistent threat to coalition forces. Networked automation won’t solve that problem, but it certainly can help narrow the intelligence disconnects among successive echelons and alert commanders in real time to developing patterns of enemy activity that otherwise

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might go unremarked or be noticed too late to take effective preemptive action. An even better way to mitigate the timing dilemma is to limit its impact. In war as in mechanics, tight tolerances increase sensitivity to friction. Plans that depend on the perfect sequencing of activities, that incorporate no fallbacks for the loss of key capabilities, or that leave no room for unexpected enemy actions are an invitation to Murphy. In the Huertgen Forest in October 1944, for example, the entire main effort of a corps attack stalled for a day when one U.S. infantry platoon became pinned down by a German machine gun.37 Networked automation can help avoid such problems by enabling multiple command levels to test in advance the sensitivity of plans to an unforeseen delay in events or the loss or temporary unavailability of key assets. In the process, it can help identify where added robustness is desirable or how the plan itself should be modified. In the worst event, it can help commanders decide when an otherwise attractive course of action should be jettisoned in favor of one less ambitious but also less sensitive to timing. At very least, it can make the risk of not doing so more visible. Managing Logistical Risk Commenting on the shifting tides of battle during 1942’s back-and-forth contest in the Libyan desert between German general Erwin Rommel and his British opponents, a student at the army’s School of Advanced Military Studies once likened the situation of both armies to being tied to rubber bands. As each attacked in turn, the rubber band would stretch until at last it snapped back, forcing the army to recoil and surrendering the initiative to the enemy.38 The rubber band in question, of course, was logistics. Commanders always have had to reconcile their ambitions with logistical risk, if only the risk of starvation. In what remains among the supreme feats in military history, Alexander the Great selected his routes, scheduled his marches, and even fought battles based heavily on logistical imperatives.39 Since that time, and especially since the mechanization of warfare, operations and logistics have become increasingly indivisible. On an enlarged and distributed battlefield, no commander however small his unit can ignore this interdependence. As British field marshal Sir Archibald Wavell rightly insisted, “A real knowledge of supply and movement factors must be the basis of every leader’s plan; only then can he know how and when to take risks with those factors, and battles are won only by taking risks.”40 Because logistics deals fundamentally with quantities changing in time and space, it understandably has been a priority focus of military automation. Nevertheless, efforts through automation to tighten the integration of operations with logistics have not been as successful to date as many had hoped, as the logistical problems encountered during the 2003 invasion of Iraq attest.

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Those problems reflected as much as anything difficulties in reconciling the logistical information available at different levels of command.41 Meanwhile, logistical inventories continue to expand and diversify. More than 20 years ago at a high-level U.S. Army war game, corps commanders uniformly declared their most intractable logistical problem to be managing the explosive increase of critical but noninterchangeable electronic components.42 Modern combat platforms built around advanced electronics can’t be jury-rigged when those components fail, nor can the latter easily be repaired in the field. Replacing a faulty component therefore may be just as important as refueling or rearming, and corps commanders aren’t the only ones who need be concerned. Networked automation obviously has a role to play in diminishing these difficulties, but assuring more comprehensive logistical tracking and better information commonality alone won’t solve tactical commanders’ logistical challenges. By itself, neither will move a single bullet or circuit board. At least as important, especially to the small unit commander, is judging the effect of the unit’s current and projected logistical condition on its ability to complete its mission. That requires translating quantitative into qualitative information, and it is in this effort that networked automation’s potential actually may be greatest. This is most visible in relation to managing supplies of fuel, water, and ammunition, the bulkiest of commodities, hence the most difficult and time consuming to replenish. By tracking time since replenishment, distances traveled, and munitions expended, networked automation can alert the commander well before supplies of these commodities become critical, enabling timely request for resupply or, at worst, modification of the commander’s intentions to avert unexpected culmination at a tactically unfavorable time and place. Automation also can help determine what assets may be fungible in relation to a given requirement, hence able to substitute for one another more rapidly than either can be replenished. For example, it can help decide whether the loss of a sensor can be covered by adjusting other sensor assets rather than suspending action to await repair or replacement. Finally, networked automation may eventually be able to forecast failure conditions in men and machines alike, and to alert commanders when those conditions approach. Work already is in progress to develop such self-alerting prognostics for the maintenance status of vehicles and weapons, and may one day extend to monitoring the physical condition of the soldiers who employ them. Such tools would significantly assist in managing logistical risk. Coping with Noncombatants “War is hell on civilians,” an anonymous wag observed in reply to complaints about World War II’s wartime rationing. His refinement of Sherman’s famous adage was no less accurate for being cynical, and no more so than

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in the irregular warfare characterizing nearly every recent conflict, in which enemy combatants unregulated by culture or convention deliberately embed themselves in civilian populations. Between that and the increased media visibility of military operations, coping with noncombatants even while fighting has become a pervasive problem for commanders at every level. Commanders always have had to deal with their own and enemy casualties, prisoners of war, and refugees. During the Crimean War, mishandling of battlefield casualties produced public outrage in Britain; and just a few years ago, claims that a U.S. unit retreating from the North Korean onslaught in the summer of 1950 deliberately fired on refugees believed to harbor enemy infiltrators prompted controversy and a formal if belated investigation.43 Today, in contrast, scarcely a combat operation of any magnitude can be mounted without considering its likely impact on civilian lives and property and vice versa. That concern will affect everything from the selection of military objectives to the use of tactics and weapons to the number and location of forward logistical and medical facilities. The major obstacle to collateral damage avoidance is target identification, particularly in operations against irregular forces that deliberately conceal themselves in and among civilian populations. In Afghanistan and Iraq as in Vietnam, distinguishing insurgents from noncombatants has been a persistent tactical problem and has affected both the conduct of combat operations and their larger political consequences. Especially in the urban warfare that has typified recent conflicts, that same problem also increasingly has restricted use of the area weapons and munitions on which ground troops have depended for more than a century. Unless offset by the use of precision weapons, such limitations thus invite an equally unpalatable increase in friendly casualties. Achieving that greater precision without loss of tactical effectiveness, however, will depend heavily on the responsiveness and reliability of the command-and-control mechanisms linking weapons to their targets and their ability to adapt to the targeting environment. The potential contribution of networked automation to managing this problem resides above all in its ability to assemble and correlate information from multiple sources in real time and display it in a useful way. That information never will be perfect, especially where the population is hostile or, if friendly or neutral, vulnerable to coercion by the enemy. Some collateral damage thus is unavoidable however disciplined the friendly force and rigorous its rules of engagement. But networked information can help diminish avoidable civil damage and casualties. Merely facilitating the rapid imposition and removal of control measures such as no-fire areas and weapons restrictions, for example, can assist materially in reducing collateral damage while minimizing the associated tactical penalties. Automation also can help manage the logistical burdens likely to be presented by noncombatants, whether enemy prisoners of war or civilians. During Operation Desert Storm in 1991, the unexpectedly rapid accumulation of Iraqi

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prisoners of war threatened to overwhelm the units to which they surrendered. The better the information on the numbers and locations of noncombatant clusters, the faster assets can be marshaled to secure and sustain them. Finally, not least of the challenges associated with noncombatants on the battlefield is insuring their condition is accurately reported, both up the chain of command and by news media. In an era when every alleged lapse in humane treatment, real or imaginary, tends to prompt a political outcry, commanders have a vested interest in disseminating truthful and rebutting false information concerning noncombatants, if only to preclude unwelcome restrictions on their tactical freedom of action. Networked automation clearly has a part to play in this effort. NEW COMMAND CHALLENGES While most challenges of battle command have varied over time in degree more than in kind, a few today are changing so profoundly that they warrant special attention. Some already have been noted in passing. Others reflect emerging requirements and technologies. Time Compression As a general proposition, tactical engagements today tend to begin more precipitately, transpire more rapidly, and terminate more abruptly than they have for centuries. To gauge the impact of this foreshortening on battle command, consider how tactical headquarters have attempted until very recently to monitor battlefield events. Reports and queries arrived in a crowded tent or command vehicle over multiple and invariably congested radio nets, often so thickly as to be audibly indistinguishable. Tired and dirty soldiers recorded those reports on whatever paper was at hand, and then, provided the reports hadn’t been misplaced, transferred them by marking pen or grease pencil to an acetate map overlay that became harder to read with every erasure and remarking. Orders were received and transmitted in the same way, occasionally accompanied by hastily drawn graphics that made their own contribution to the diminishing readability of the recipient’s map. Combine that with dated information that failed to be deleted and new information that somehow failed to be recorded, and it isn’t hard to see how quickly any correspondence between the displayed and actual tactical situation could evaporate, even assuming the original information was accurate and complete. Such a process, needless to say, is incongruous in the military of a nation whose children play routinely with devices that store, display, manipulate, and communicate information far more rapidly and reliably. More important, it increasingly has become unable to keep pace with the information flow with which today’s commanders must cope. That alone is enough to justify current efforts to apply networked automation to battle command.

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Simultaneity Not only do events on today’s battlefield happen more quickly, but they also involve more diverse activities in more places at the same time. In part, that simply reflects increased dispersal. But in recent U.S. doctrine, it also reflects a deliberate intention to confront the enemy simultaneously with more problems than his own command apparatus can handle. Its aim is to produce confusion and dislocation that deprives the enemy of the ability to respond effectively and thus accelerates his mental and psychological collapse. The presumption, of course, is that such multiple simultaneous activities won’t prove more disruptive to the perpetrator than to their intended victim, and also—and perhaps more important—that such efforts won’t result in piecemealing assets to the point where they no longer contribute effectively to a coherent overall purpose. Assuring both is a challenge even at the operational level. For the battle commander, it is much greater. To the usual obligation to synchronize maneuver and integrate combined arms, it adds the requirement to orchestrate concurrent but independent activities aimed at separate and spatially disconnected objectives. Simply keeping track of those activities to insure they don’t mutually interfere will test the commander’s information systems, never mind managing any unforeseen adjustments. In addition, simultaneity only multiplies the tactical and logistical dilemmas associated with any single operation. Effective delegation thus becomes even more critical, and with it continuous review and refinement of the commander’s estimates and intentions. There certainly is historical precedent for the success of such simultaneous operations when directed by genius, but even more for their failure in its absence. And since, as Clausewitz pointed out, genius is a scarce commodity even in the finest military, a doctrinal commitment to simultaneity implies some more uniformly accessible command resource. Apart from fostering subordinate initiative, that resource resides in improved information systems if it resides anywhere, and that too argues for enhancing battle command with networked automation, and with soldiers and leaders schooled to employ it effectively. Lethality It would be a mistake to suggest, as some have, that the lethality of ground warfare overall has increased. At Cold Harbor in 1864, attacking federal forces lost nearly 7,000 men in less than an hour, and at the Somme in 1916, 58,000 British troops were killed or wounded on the first day, roughly 3,000 per kilometer of front and more than 10 percent of the committed force. No recent conflict has seen anything like those numbers.44 What is true, however, is that the reach and precision of both surface and air weapons and their associated target acquisition means have increased

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dramatically during the past several decades. The continuing dispersal of ground combat formations discussed earlier directly reflects that change, and also helps explain why increased weapons lethality hasn’t automatically translated into heavier aggregate casualties. The net effect of these developments, however, has been to place significantly more firepower at the disposal of progressively smaller units while simultaneously making their own exposure more hazardous. Together with increased political reluctance to accept high casualty rates, that has prompted the United States and other modern military forces to seek new ways to separate tactical sensors from shooters and reduce the dependence of both on the physical proximity, hence exposure, of human operators. Efforts currently in progress range from developing means of cooperative engagement, by which one platform—a tank, say—can engage a target acquired by another that thus remains unexposed, to precision engagement from beyond line of sight of targets detected by robotic sensors, to engagement from beyond the enemy’s reach altogether by remotely controlled platforms such as armed unmanned aerial vehicles.45 Further down the road, some envision fully autonomous combat platforms, able based on automated instructions to detect, identify, maneuver to, and engage targets without additional human intervention. All these efforts today are embryonic, especially in the ground combat arena. Difficult technical obstacles in areas ranging from sensor behavior to communications reliability and robustness remain to be overcome before even semiautonomous ground combat platforms can be fielded with confidence in their battlefield performance. But the more difficult challenge concerns how the battle-command system will handle these emerging capabilities. Precision weapons are by that very quality intolerant of error. They will do exactly what they’re told. The burden on the teller increases commensurately. Similarly, while remotely controlled or autonomous systems may help diminish the impact of danger and discomfort by reducing human exposure, they also magnify the sensitivity of maneuver and engagement to uncertainty and friction. A central focus of MDC2’s experiments has been to probe these interactions at the lowest tactical level and at high resolution, against an adversary free within his capabilities to operate in whatever manner he chooses. As the chapters that follow will confirm, we still are far from resolving many of the tensions introduced by emerging automated capabilities. But the experiments also suggest that they are solvable, and indicate the directions, both technological and in terms of new soldier skills, in which those solutions may be found. Tactical Agility The greater the physical dispersal of tactical units, the less confidently they can count on timely reorganization or reinforcement to accommodate

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unexpected changes in missions or the conditions in which they must be accomplished. That reality already has prompted the U.S. Army significantly to alter its basic tactical design, pushing organic combined arms capabilities down from the division to the brigade. The implication, however, is that more of the combat power that brigades and their subordinate units will bring to bear in the future will have to be furnished by U.S. Army and joint assets at higher command levels. Until recently, that would have meant significantly expanding supported units’ organic or attached battle-command facilities and personnel, increasing tactical headquarters’ footprints and diminishing their ability to move rapidly and safely around the battlefield. Networked automation promises to diminish that requirement. Merely the ability to share threat information, friendly locations, and tactical graphics in real time inherently reduces the coordination burden, while digital connectivity similarly reduces the load on voice communications. Both should result in a need for fewer headquarters personnel, although to what extent isn’t yet clear. At any manning level, however, they promise more rapid and reliable integration of capabilities and effects. That enhancement is the more important to permit prolonged operations without loss of tactical continuity. Historically, headquarters battle rhythms have been dictated as much by the inability of command systems to operate at a uniform level of effectiveness around the clock as by the need of their personnel to rest. Networked automation won’t diminish that need. But it can make battle handover much faster and smoother, and reduce the likelihood that vital information will be lost or inadvertently neglected in the process. Finally, provided it is not abused, networked information promises improved resistance to catastrophic failure from the loss of any single command element. Merely the presence of or rapid access to the same information at multiple locations provides inherent command-and-control redundancy. Similarly, networked automation can allow more rapid assumption of command by an alternate, higher or subordinate headquarters without confusion and delay. Naval ship design long has incorporated such redundancy, with steering, fire control, and other key command functions duplicated at more than one physical location. Networked automation offers ground combat units similar robustness. Transparency On February 13, 1991, a U.S. airstrike destroyed the Al Firdos bunker in downtown Baghdad, killing scores of Iraqi civilians who had taken refuge beneath its reinforced concrete. Intelligence had confirmed the bunker’s use as a military command post, whereas no information suggested its occupation by noncombatants. Nevertheless, media reaction to the attack, ably exploited by the Iraqis, resulted in a precipitate decision to suspend any further attacks on war-supporting targets in and around Baghdad.46

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More recently, as this was written, debate swirled in the United States and international press in reaction to the use of white phosphorus against Iraqi insurgents.47 White phosphorus has been a standard artillery and mortar munition since World War II, used both to destroy inflammable materiel and attack personnel protected from high explosive fragmentation. Until now, its use for either purpose has never been challenged. Not the least of the ironies of modern war is that the same technologies promising to enhance the commander’s access to and use of information are equally available—in some cases more available—to media with interest in but no accountability for the conduct of military operations. Increasingly, the video camera hangs over the battle commander like an electronic Sword of Damocles, able almost instantaneously to dispute his appreciations, judge his decisions, and criticize their consequences. Commanders always have had to contend with history’s judgment, but never before have so many been subjected to such immediate and pervasive public scrutiny. It would be asking too much of human nature to imagine their behavior remaining unaffected by it. Its danger, of course, is the inculcation of overcaution and an aversion to the risk taking without which, as Field Marshal Wavell rightly argued, no success in battle is possible. There is little the battle-command system can or should do to diminish transparency or to immunize commanders against its effects. What it can do, given the right tools, is help avoid the internal delays and confusion that too often make the commander the last to know about an incident or decision likely to prompt unwelcome media attention. Almost invariably, the best defense against false, distorted, or incomplete reportage is rapid and accurate dissemination of the truth. And if that truth is awkward, it is even more essential that the commander be aware of it and be perceived to be aware of it. Beyond that is moral courage and the acceptance of responsibility, for which no battle-command system can substitute. ENHANCING FUTURE BATTLE COMMAND No single chapter can hope to deal with all the complexities of battle command, about which, in any case, histories, memoirs, and analyses abound. Nor has this chapter sought to do so. Rather, its purpose has been to identify some of the continuities and changes that have influenced and will continue to influence the conduct of battle command, and to suggest where in that process emerging networked automation technology may offer the most promise and present the greatest risk. When all is said and done, achieving the former and avoiding the latter will reflect how well technology designers and their military clients satisfy three requirements: reconciling the art and science of battle command, understanding the limitations as well as the virtues of networked automation technology in furthering that effort, and assuring that both the technology and the way it is used continue to reflect the inescapably human dimensions of battle.

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To complete this discussion, a few thoughts follow concerning these requirements. Reconciling Art with Science Reflecting on emerging military information systems, one thoughtful officer wrote some years ago, “As more powerful technological tools intrude into the process of command, they bring with them the risk that a generation of officers will be more inclined by instinct to turn to a computer screen than to survey the battlefield, and that the use of precise operational terms will be displaced by computertalk. If that happens, we may have lost more than we have gained.”48 Of course he was right, and events since he wrote already have surfaced indications of that problem. During early combat operations in Afghanistan, for example, some ground commanders reportedly became so mesmerized by the televised feed from unmanned aerial vehicles that other headquarters business virtually came to halt. As subsequent chapters will reveal, similar episodes occurred during the experiments described in this book. The invariable and quite correct response to such problems is that preventing them is a matter of training. But that training to be successful must begin by acknowledging the limits of technology in satisfying the creative and thus at least partly intuitive requirements of battle command. Networked information systems can assemble, filter, collate, disseminate, and display information. They can alert the commander to attend to it. But they can’t force him to attend to it, nor make sense of it for him, nor decide what elements of it deserve the most urgent attention, nor envision what form that attention should take. Just as the pilot too focused on his instruments can collide with the mountain in front of his face, so a commander too tightly wedded to his information systems may fail to sense indicators no less important for being perceptible only through his own educated instincts. Likewise, while technology can enhance the commander’s ability to judge opportunity and risk, it can neither reconcile them for him nor determine which to favor and in what way. In short, in training as in actual operations, information technology must be treated as the servant, not the master, recognizing that in battle command perhaps more than in any other human endeavor, the ultimate value of science is to facilitate the practice of art. Recognizing System Limitations A recent paper advances six principles on which to base future battlecommand system development. The first, argued to be prerequisite to the rest, proposes that “The Future Battle Command and Control System [must be] an integrated system of systems that meets the needs of commanders and staff at every level, for all BFAs [battlefield operating systems], and across the Services.”49 The Army Transformation Roadmap quoted earlier goes even

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further, envisioning that “The same system that controls wartime operations will regulate activities in garrison and in training.”50 These are heroic ambitions. They also are very likely unrealizable, perhaps fortunately, for their premise is that all command requirements can be reduced to the same ingredients. But if history is any guide, command, especially in battle, is far too idiosyncratic a process to tolerate so procrustean a solution. Instead, as one historian concluded after carefully examining several successful and less successful command systems, “Command being so intimately bound up with other factors that shape war, the pronunciation of one or more ‘master principles’ that should govern its structure and the way it operates is impossible.”51 In reality, no single system of command however robust is likely to satisfy every military requirement. Indeed, such a system even were it technically feasible to devise would tend almost inevitably to reproduce the very sort of command rigidity that contributed so heavily to the Royal Navy’s embarrassment at Jutland. Instead, a more reasonable expectation is that emerging networking technology will allow information to be shared in a timely way by different organizations without imposing undue restrictions on the way it is manipulated, displayed, and employed. As will be seen in the chapters that follow, a central objective of the MDC2 program has been to examine how commanders at different levels choose what information to attend to and how. Still more is that true of command direction, which must not only be framed by the commander’s intentions, but also adapted to the intended recipient and the conditions in which it is received. Both can be expected to vary constantly, and a command system unable to accommodate those changes will impede the commander more than assist him. Allowing for the Human Dimension No one has ever described the human dimensions of battle command more eloquently than poet Stephen Vincent Benet who, in his narrative poem about the Civil War, describes generals trying in vain to move their men around the battlefield like blocks on maps, only to discover that men linger, straggle, and die “In unstrategic defiance of martial law, Because still used to just being men, not block-parts.”52 The difficulty with commanding through icons is that it is all too easy to lose sight of what the icons represent. Soldiers—and their leaders—are human, and being human are fallible. Discipline, training, cohesion, and pride all help limit that fallibility, but battle is a crucible that can test even the strongest to failure. A battle-command system that conceals or discounts the human factors affecting every tactical evolution does its users no service. Its depiction of reality will be incomplete and its utility for decision making will be crippled. Most of all, it invites surprise and defeat the more devastating for being unnecessary. In August 1914, Von Schlieffen’s Olympian vision of command failed the test

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of battle not only for lack of adequate technology, but also because it largely ignored the human limitations of the army to which it was applied. Some of today’s command and control concepts risk making the same mistake. Networked automation can’t be made responsible for the professional competence of the commanders who employ it, and we wouldn’t want it to be. But it can be designed in a way that no flat map can to alert commanders to the creeks and gullies that may hamper their wooden squares, remind them how long it has been since those squares were rested or resupplied, warn them of dangers those squares may be unable to sense, and in the crunch, help bring to bear in a timely way the resources to insure that their block-men don’t die unnecessarily “because still used to just being men, not block-parts.” In the effort to insure that battle command remains sensitive to war’s inescapably human character, in short, the system designer shares responsibility with the commander. Only if, in addition to assisting him to apply capabilities, networked automation also assists him in protecting and preserving the soldiers on which those capabilities ultimately depend, will such a system truly deserve to be called a battle-command system.

CHAPTER 2

A Journey into the Mind of Command: How DARPA and the Army Experimented with Command in Future Warfare Alexander Kott, Douglas J. Peters, and Stephen Riese

A BATTLE OF 2018 His small robotic spy planes, tens of kilometers away, faithfully scanned the battlespace. The composite image—flat, swampy land dotted with hamlets, lakes, and untidy small forests—slowly scrolled on the computer screen. Captain Johnson1 glanced at the calendar and weather predictor tucked into a corner of the display. On this August 17, 2018, it was going to be hot and muggy all day. “Lousy visibility,” he thought, “the UAVs are going to miss a lot of enemy.” A few months ago, in early 2018, a faction within the Azerbaijan military suddenly offered its support to a long-lingering dissident movement. Traditionally, the dissidents’ influence rarely extended outside of the southeastern portion of the country, mostly south of the Kura River in the Kura Depression Region. Now, however, things were unfolding differently. By April 2018, the Azeri Islamic Brotherhood (AIB), a coalition of antigovernment factions, subverted the bulk of an Azeri Motorized Rifle Brigade, the well-trained and formerly reliable Kura Brigade that mutinied to realign with this faction. In a surprise action, a battalion from the Kura Brigade (10th MRB) seized control of a historically significant district in the capital of Baku. A desperate week-long defense by loyalist government forces against the attacks of the 10th MRB managed to secure the center of government within the capital city. Still, the AIB succeeded in halting the session of the national assembly when members fled to their home territories. The president, along with his prime minister and council of ministers, remained in Baku and continued to direct the government and remaining loyalist military forces in the city and along the Apsheron Peninsula.

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These events induced an inevitable round of desultory diplomacy by the international community, which predictably failed. At that point, Russia proposed a coalition of U.S. and Russian forces to restore order within Azerbaijan and to stabilize its government. The United States agreed to the proposal, and by July 2018, U.S. forces began staging in coalition bases along the Georgia-Azerbaijan border. Later that month, Coalition forces conducted rapid movement across the border to clear lines of communication and to establish a forward operating base to be used as a staging area by Coalition forces. Captain Johnson and his Combined Arms Unit (CAU), a total of about 30 human fighters and 20 robotic warriors, were a part of the Coalition. From this point on, we will refer to this coalition as the Blue force. Facing Captain Johnson and the rest of the Blue force was a rather formidable foe. The Red forces included the mutinous Kura Brigade joined by several other key units of the Azeri military (see Figure 2.1), a powerful uniformed motorized force—the best tanks, armored personnel carriers, and self-propelled artillery that the oil-rich Azerbaijan Ministry of Defense was able to buy in the early 2010s. This well-trained traditional military enjoyed close cooperation with insurgent forces—some of them experienced foreign guerilla fighters, and others local, untrained, but enthusiastic militiamen of AIB. The dismounted insurgent forces could disperse throughout the area of operations to provide the

Figure 2.1. Organization and equipment of Kura Brigade. The Appendix describes the equipment.

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Red commander with early warning of Blue force movement, and to serve as additional dismounted infantry to confront the Blue force. Further complicating the battlespace picture, the armed members of the Nagorno-Karabakh Internal Liberation Organization (NKILO), a militia that elected to remain neutral in this conflict, operated throughout the area. The neutral NKILO members dressed as civilians but often carried weapons like members of the insurgent AIB forces. From this point on, we will refer to these enemy units as the Red force. The raw numbers of personnel and weapon systems in Captain Johnson’s area of responsibility—Red versus Blue—were certainly stacked against him. A common rule of thumb used to say that the attacking force must be about three times larger than the defending force. Johnson’s CAU, however, had about one-third the number of platforms and many fewer troops than his Red counterpart. Yet his orders were to attack! By turn-of-the-century measures, Johnson’s force was an order of magnitude smaller than it should have been. It was hard to count on a significant difference in training and motivation: the Red force was known to be brave, motivated, and knowledgeable in their own tactics. He knew, however, that in terms of technology his force was a generation ahead of the enemy and that this fact could be worth more than a 10-fold numerical advantage. The CAU’s fighting platforms (see Figure 2.2) were light and fast. Most of them were unmanned, robotic vehicles that did not have to carry heavy armor to protect any human riders and instead carried more weapons and fuel. Less encumbered by weight and dependence on supply trains, their maneuver could be far-reaching and more agile than their opponent’s. Besides, if necessary for longer distances, most of them could be carried by helicopters.2 Johnson’s unit was also rich in aerial and ground sensors. His robotic reconnaissance assets—aerial and ground—ranged far ahead of the CAU’s main forces. With their diverse sensors and the semiautomatic ability to detect suspicious objects—potential Red vehicles or infantry—they provided the Blue force with crucial information about the locations and intent of the Red force, long before coming in contact with the enemy’s weapons. The captain usually knew much more about his enemy than the enemy knew about him. Granted, it was of limited value to know much more about the enemy without being able to impact him. Fortunately, the CAU included plenty of capable shooters. His long-range artillery and precision missiles—most of them carried by unmanned vehicles—allowed Johnson to attack the Red force at a distance once his sensors found the targets. Still, all these assets would be worthless without the network that tied them all together, a network that allowed Johnson to receive voluminous information about the battlespace and send detailed commands to his forces. United by the network, the CAU’s assets could fight in a widely distributed, dispersed fashion without losing synchronization and mutual support. Beyond the CAU’s own assets, it could rely, if necessary, on those of a sister unit: the

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Figure 2.2. Organization and equipment of CAU. The Appendix describes the equipment.

network enabled them to support each other with both information and fires even when separated by tens of kilometers. Finally, and perhaps most importantly, the CAU’s small command cell— Johnson and his three battle managers (see Figure 2.3) riding in their Command and Control Vehicle (C2V)—wielded a powerful weapon for battle command, the Commander Support Environment (CSE). A collection of computer tools, the CSE fused the massive amount of information arriving from the CAU’s manned and unmanned platforms; assisted with the recognition of enemy targets; advised on the courses of action available for maneuver, fires, and intelligence collection; translated the battle manager’s terse commands into detailed instructions to robotic warriors; and even, if necessary, autonomously planned and executed the fires and intelligence collections tasks. Johnson looked at his battle managers. On his right was Sergeant Rahim, the intelligence manager, about 15 years older than Johnson. Trying to optimize aerial sensor availability for battle damage assessment tasks, he was tasking the CSE to calculate plans to potentially use Class I sensors only; Class I and II sensors only; and Class I, II, and III sensors, with Class III sensor platforms only used after the maneuver force has reached Phase Line GOLD. Seated in front of Johnson, Specialist Chu, the maneuver manager, worked the CSE to finalize several alternative routes for relatively clumsy robotic ground vehicles. The fourth member of the CAU command cell, the effects manager, Sergeant Manzetti, sat in the front-right corner of the C2V and busied herself with entering into CSE the no-fire rules for the politically touchy villages controlled by hopefully neutral NKILO militias.

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Figure 2.3. The Blue command cell—commander and three battle managers—ride in a C2V.

“It’s weird,” the captain thought, “when Rahim joined the military, I was barely out of kindergarten, and all this stuff—robotic guns, unmanned sensors, smart computers everywhere—was considered mostly science fiction. Now it is just normal, totally normal.” NETWORK-ENABLED WARFARE In fact, the efforts to make all this totally normal stuff for Captain Johnson started well before he went to kindergarten. The style of warfare practiced by his CAU is called network centric or network enabled (we prefer to use the latter term in this book), and, like most other revolutions in warfare, it sprang from a confluence of several technological and political developments. A good place to start unraveling this chain of developments is the personal computer revolution of the 1980s. Suddenly, anyone could afford to buy a significant amount of computing power. Digital information became ubiquitous— it was easy to generate such information, to capture, to reproduce, and to distribute. One unexpected outcome of this development was its impact on the seemingly invincible Evil Empire, the Soviet Union. Long reliant on keeping information away from its citizens, the Communist power was faced with a choice: technological obsolescence or relaxation of its information control. The Soviet Union wisely elected the latter, promptly collapsed (for a number of reasons, not just the information revolution), and released in the wake of its

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collapse a tsunami wave of religious and ethnic wars around the world. These conflicts changed many of the equations for the U.S. military, forcing it to look for such things as rapid deployment, small wars, counterinsurgency, and highly distributed operations. In another branch within this network of developments, personal computers made networking both highly feasible and highly desirable. In the early 1990s, people started to notice a mysterious slogan in the marketing literature of Sun Microsystems, a then-popular maker of high-end computer workstations. “The network is the computer,” went the slogan. Sun’s leaders argued that platform-centric computing was a thing of the past, and the future was with marvels, such as the Internet, that arise from the network-centric computing. The Internet went on to have a glorious life of its own, including changing the ways that the U.S. military communicates. Meanwhile, the term network centric and its broader underlying ideas appealed to a visionary duo— an Air Force officer, John Garstka, and a Navy aviator, Admiral Arthur K. Cebrowski—who proceeded to apply it to things military.3 If all military assets—warfighters, tanks, ships, airplanes—were to be connected by powerful information networks, they could cooperate, make synchronized decisions, and fight in a more agile, effective fashion. They could be tailored to a specific mission. They could be geographically distributed. Their deployment and logistics could be faster and more flexible. They could use different ways to organize themselves, perhaps even self-organize. They could also provide a more-efficient environment for employment of a slightly older development—precision weapons. The ideas fit perfectly. Finally, here was a coherent, elegant vision of how the indisputable information revolution could revolutionize military affairs. Network-centric warfare became a popular concept within the U.S. Department of Defense. The Office of Force Transformation became the official home of the concept, and Admiral Cebrowski, John Garstka, and Dr. David Alberts issued a steady stream of influential publications.4 Naturally, every service within the U.S. military developed its own perspective on the network-centric idea. By the late 1990s, the U.S. Army was eyeing a number of challenges. The typical ponderous deployment of the army’s heavy forces was seen as a liability in the post-Soviet age: faster deployment by fixed-wing airlift seemed necessary, but the army’s equipment was too heavy. The expense of large numbers of men and women in army uniforms was becoming difficult to justify. Its main fighting platforms—the Abrams tank and the Bradley personnel carrier—were starting to approach their obsolescence horizon and called for replacements. Emerging technologies— computers, sensors, laser-guided weapons, robots, unmanned aerial vehicles (UAVs)—all seemed interesting but difficult to accommodate in the army’s current conceptual structure. Enter network-centric warfare. In one fell swoop, it offered a holistic solution, a unifying framework for all of the above-mentioned concerns. The army named this synthesis of ideas the Future Combat System (FCS).5 Computer

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networks would permeate the FCS, delivering the information from farseeing advanced sensors (such as those carried on UAVs) to shooter platforms (many of them robotic) that fire precision weapons at a faraway enemy beyond the horizon. By detecting and engaging hostile forces at a distance, the FCS force could avoid the enemy’s direct fires and allow the army platforms to carry more modest armor. This would reduce the weight of the platforms and make them suitable for rapid air delivery to multiple trouble spots around the world. These FCS platforms, elaborately rich in information but prudently frugal in armor, would be procured to replace the aging Abrams and Bradleys. All this was a perfect fit. Of course, there were critics of the idea. Some argued that certain key technologies, such as robotic vehicles, remained underdeveloped and not ready for prime time, and other technologies, such as the wireless mobile networks, remained too vulnerable to enemy attacks.6 The need for rapid deployment by air may have been overestimated; there was no pressing need to dispense with heavy armor,7 and besides, FCS would take almost as long to deploy as a conventional force.8 Others argued that the FCS system was too expensive,9 that a light-armored force was too vulnerable for direct contact with the enemy,10 and that emphasis on network-centric warfare would lead our military to neglect the need for more boots on the ground.11 Not so, responded the advocates of the program. Most critical technologies were already mature, and others were in well-managed development. The architecture and characteristics of the system were carefully optimized to balance its deployability, survivability, and lethality in a broad range of future conflicts. The overall costs would be much lower than any practical alternative approaches, including an attempt to modernize the current conventional platforms. With many tasks automated, and many platforms standardized, the need for support personnel and its associated costs would be significantly reduced.12 The new network-enabled force would provide more boots on the ground, significantly faster deployed into difficult hot spots, defeating either conventional or unconventional enemies with fewer risks and costs. We do not intend to diminish either the weightiness of all these considerations, or the contenders’ sincerity and competence. You, the reader, may find in this book grounds for support for both sides of the argument. Our findings and observations both confirm the potential of a network-enabled force and highlight risks of such systems as currently conceived. Still, these are not the arguments we wish to pursue on the pages of this book. Rather, we argue that the issues of armor and information do not need to be coupled. Most of our findings indicate that these are orthogonal issues: the cognitive challenges of information-rich, network-enabled warfare do not depend on the thickness of the armor. Network-enabled warfare will deliver its value (or will fail to deliver, if the challenges of information-rich battle command are not solved properly) even with heavily armored platforms. Conversely, heavy armor neither obviates the need for networked information, nor precludes it.

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Regardless of the decisions on the right thickness of the armor, or the right number of boots on the ground, the already-present elements of networkenabled warfare call for serious attention to how warfighters can deal with the explosion of information. The strengths (or weaknesses, as the case might be) of the collective human-machine cognition will be at least as important as the right combination of the platforms’ characteristics. While at the time of this writing the FCS program continues as a strong, innovative, ambitious, and expensive effort, network-enabled warfare does not wait. It enters the military by guerilla marketing methods, far outpacing conventional military procurement. Enterprising warfighters buy laptops and wireless devices; rig databases, blogs, and chat sites; and establish their own procedures and techniques that are clearly reminiscent of network-enabled ideas. UAVs and even ground robots find growing acceptance among the warfighters, regardless of the inevitable immaturities of the technologies. With or without the muscle of military acquisition, network-enabled warfare is entering real-world military operations. And this brings a major concern that began to emerge even in the late 1990s: with drastic proliferation of information flows impacting the warfighter, and with so many new devices requiring the warfighter’s attention, what will happen with the human cognitive mechanisms? To put it differently, networkenabled warfare will unleash a flood of information on the warfighter. Will the flood overwhelm the cognitive abilities of the warfighter? Particularly important, will the warfighter be able to manage the battle command? THE HISTORY OF THE MDC2 PROGRAM At least two organizations recognized the challenges of battle command in network-enabled warfare. One was the Defense Advanced Research Projects Agency (DARPA), a legendary and occasionally controversial powerhouse of American research and development since the late 1950s, the central R&D organization of the U.S. Department of Defense. Another was the U.S. Army Training and Doctrine Command (TRADOC), a key brain trust of the army, responsible in particular for the development of new concepts and techniques of warfare. By the year 2000, the network-enabled developments, particularly the FCS, were charging forward. But what about battle command in a networkenabled environment? How would it be performed and by what means? Recognizing the seriousness of these challenges, in October 2000, DARPA and TRADOC initiated the Future Combat Systems Command and Control (FCS C2) program. Its objective was to explore the unique command and control challenges that would face the future force envisioned at the time by the U.S. Army.13 The program was a brainchild of Gary Sauer, a visionary lieutenant colonel of the U.S. Army, whose energy and ideas impressed the army’s top leadership. Initially, the program focused on the smallest notional future unit with a significant range of weapon systems—the hypothetical CAU. The weapons,

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capabilities, and organization of a CAU were derived from the then-current Operational and Organizational Plan for FCS.14 Smaller than a conventional army company, the CAU was equipped with an impressive array of sensors and shooters, both unmanned (robotic) and manned, all connected by a data network. The unit was able to acquire and process detailed information about the faraway enemy, maneuver rapidly over distances of tens of kilometers, and defeat the enemy with precise fire, both direct and indirect.15 For its survival and effectiveness against a larger and more heavily armored enemy, the unit relied on large volumes of information collected by its numerous sensors, such as UAVs, and delivered to the unit’s decision makers via its ubiquitous network. The commander and his assistants had to process this massive volume of incoming information and to command the far-flung assets of the unit, many of them robotic and therefore reliant on precise and detailed commands. To absorb the vast quantity of incoming information, and to issue a high volume of detailed commands in a high-tempo battle, the commander required a new type of a command and control tool. Therefore, the program had to accomplish several things: (a) build a prototype command and control tool called the CSE and construct the associated command organization, techniques, and procedures; (b) devise an experimental way to measure the performance of decision makers in such an environment and identify the key factors influencing the performance; and (c) perform a series of experiments—simulated battles in which the decision makers and the new tools display their capabilities. The initial program consisted of a series of four experiments. The first three experiments focused on developing the basic CSE application and control mechanisms. The fourth and final experiment of this program was conducted in two phases and designed as a discovery experiment. It explored the implications of robotics, information superiority, and networked fires on a commander’s ability to develop situation awareness and coordinate reconnaissance and surveillance, maneuver, and fires of his organic assets. While FCS C2 was seen as a successful program, it explored a new command and control capability for one unit only. It did not answer questions about battle command in a multiunit and multiechelon environment, particularly about the challenges of collaboration between peer units and across multiple echelons. Therefore, in 2003, DARPA and the U.S. Army TRADOC decided to extend the initial effort via a successor program—the Multicell and Dismounted Command and Control (MDC2),16 which continued the FCS C2 experimental series. Both programs were created and led by Gary Sauer (who retired from the U.S. Army and became a program manager at DARPA) and Maureen Molz, a senior civilian manager in the U.S. Army’s research and development system. For the sake of brevity, in this book we refer to the combination of the two programs simply as MDC2. A key focus of the program’s continuation was on extending the CSE suite of command tools to explore the command and control required by several echelons of a future force. The extended CSE was designed to collect relevant

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Figure 2.4. The progression of experiments in the FCS C2 and MDC2 programs.

battlespace information provided by sensors across the multiechelon, multiunit force, fuse this information into a consistent representation of the battlespace, and present it in such a way that timely and effective decisions could be made at echelons ranging from the individual combatant through the battalion commander. Continuing the series, the fifth experiment expanded the CAU to include dismounted infantry soldiers and to explore the information and control requirements of these lower echelons. Inspired by the then-current suite of the U.S. Army scenarios, the so-called Caspian Sea scenarios,17 the simulated battlefield also moved to a more complex geopolitical setting. The final two experiments in the MDC2 program added a fully functional higher echelon and a sister unit to explore collaboration, assets sharing, and information flows between echelons and between peer commanders.

EXPERIMENTAL TESTBED To experiment with the ways in which the future Captain Johnson and his battle managers might execute their network-enabled battles, we constructed the MDC2 experimental laboratory. There we constructed mock-up C2 vehicles not unlike the one Captain Johnson might ride into a battle and populated them with teams of live officers and staff members. Each such team, called a command cell, commanded a force of artificial warriors and platforms, such as the CAU we described earlier, simulated by the U.S. Army’s premier simulation system called OneSAF Testbed (OTB).18 The opposing force, the Kura Brigade and their insurgent allies, were also simulated but were commanded

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by a live and very capable Red command cell. The Red and Blue command cells did not know the locations and plans of the opponent’s forces, except as they were able to determine during the battle. Both were allowed to conduct the battle as they desired, in a so-called free-play fashion, without following a prescribed script, although within prescribed rules of engagement. The battles unfolded with realistic speed, in other words in real time. The information about the battle events received by a command cell via computer monitors and radio channels was also fairly realistic, allowing the command cell to interact with the environment as if in the midst of a real battle. All of the important functions of Johnson’ CAU were represented: the ability to maneuver the forces, direct lethal and suppressive fires on the enemy, direct and collect ISR effects, and conduct a small measure of logistics. To put all this more formally, the MDC2 experimental program was conducted in a simulation-supported, interactive, real-time, free-play, human-in-the-loop laboratory environment. Although all eyes were on the live command cells, the experiments could not be performed unless the commanders had somebody to command. Thus, OTB was the critical basis of the experimental environment. OTB simulated Blue and Red forces at the entity level, meaning that each warfighter or tank or other entity in the battlespace was simulated individually. An entity received a command from the command cell and then computer programs (called behaviors) took over and controlled the detailed actions of the entity—for example, the way in which the soldier ran or fired his weapon. This simulation approach is called entity-level semiautomated or computergenerated forces. The capabilities and characteristics of such entities were strictly managed: we maintained a set of Red and Blue equipment manuals that provided the detailed characteristics of Red and Blue platforms, weapons, and sensors. It helped that the OTB software had an open architecture with source code that allowed for modifications to meet specific requirements. For example, we developed and added several dismounted infantry behaviors to the OTB software in order to support the MDC2 experiments. These unit-level (squad and fireteam) behaviors added tactically realistic functionality that reduced operator workload. For example, the React to Contact behavior provided intelligent rule-based behaviors when a dismounted infantry unit came in contact with enemy forces or indirect fire. The behavior resulted in one of several potential outcomes, including advancing on the threat, withdrawing from the threat, or pausing to survey the threat. In a typical experiment, the OTB-simulated Blue force was commanded by three Blue command cells. Two of the cells commanded a CAU each—a force of roughly company strength. The two CAUs were parts of a Combined Arms Team (CAT), a unit of about a battalion strength that was commanded by the third cell. In addition to these three cells, a notional brigade commander provided input and course correction to help ensure experimental objectives were being met. This brigade commander formed the

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necessary link between the friendly forces and the experimental control cell (described later). The Red command cell included a commander and several staff members. The commander was separated from his staff and could communicate through radio calls. Because each radio call carried the possibility of being detected by a friendly force sensor, communication was used sparingly. The Red staff members interacted with the OTB simulation and had access to all information gathered by their units. The enemy commander, however, only had access to an infrequently updated display of the battlespace. The intent of this display was to represent an approximation of the operational picture available to an enemy commander in 2018. Neutral forces, also simulated by OTB, acted independently of both the friendly and enemy forces and added much complexity to the battlespace. They included buses with predefined routes, trucks, and civilians in populated areas. Unlike the Blue and Red command cells that could see only the information that their forces would acquire in the battle, the experimental control cell had displays that showed all true locations and actions of Blue and Red forces—the full ground truth. The control cell also listened to radio conversations between the Red commander and his staff or between the Blue command-cell operators. The members of the control cell did not interact directly with the simulation unless there was a system problem but were responsible for ensuring that the experimental objectives were being met and that the systems were performing as expected. Furthermore, to maintain the integrity of the experiments, the control cell did not interface directly with the Blue command cells. Instead, required communications to Blue commanders were accomplished through the notional brigade commander using conventional military protocol. Observers and analysts were located throughout the laboratory and tracked the action directly from the experimental control cell and the enemy commander’s cell. These analyst observers were privy to all discussions within the experimental control cell and between the enemy commander and his staff. The analyst observers responsible for recording the Blue command operations were physically removed from the Blue cell operators they were observing but had access to everything the Blue commanders saw and heard. To guard against any experiment-disrupting influences, these analysts were not allowed to interact with the Blue staff. THE BLUE COMMAND The all-important command cells, the focus of our experiments, deserve a more detailed discussion. Recall that latter experiments included several Blue command cells. One cell commanded the CAT (approximately a battalionstrength unit). Subordinate to the CAT cell there were two CAU command

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cells (each CAU being about company strength). In addition to commanding the two CAUs, the CAT cell also commanded its own organic battlespace assets as illustrated in Figure 2.12. To provide greater realism in how the CAT and CAU cells would interface with higher and lower echelons, we also included one brigade-level command element, the superior of the CAT cell, and two platoon leaders, subordinate to the CAU cells. However, these echelons—brigade and platoon—were not a focus of our experiment, so we modeled them less accurately than the CAT and CAU cells. CAU and CAT command cells were similar in their internal organization. Each cell consisted of a commander and three staff members called battle managers. Although we allowed the cells to self-organize their responsibilities, the battle managers usually assumed the conventional staff roles of intelligence, maneuver, and effects. In these roles, the intelligence manager (also called information manager) was responsible for developing the picture of the battlespace by controlling sensors and examining and classifying sensor images. The battlespace manager (also called maneuver manager) was responsible for coordinating the movement and synchronization of the maneuver elements, such as combat robots and infantry carriers. The effects manager (also called fires manager) was responsible for identifying and engaging targets. In the experimental battles, the roles of commanders and battle managers were played by army officers and noncommissioned officers, some retired, most active duty. This allocation of responsibilities was far from fixed. The commander often reallocated the tasks depending on the requirements of the situation and on the skills of the battle managers. For example, the responsibility for battle damage assessment tasks often floated between the effects manager and the intelligence manager. Management of the critical collection assets, such as UAVs, often devolved to the commander. The tasks of moving Non-Line of Sight (NLOS) assets sometimes changed hands between the battlespace manager and the effects manager. Because each cell member accessed the same data set and had a unified reconfigurable command system, every member could perform every task. This allowed for a variety of creative approaches to the organization and procedures of a command cell. In addition to the four members of a command cell, a typical C2V also carried its driver and a gunner. The CAU C2V was a fully developed mock-up that was enclosed (isolated from the room in which it resided) and attempted to represent the space and lighting conditions expected in a future C2V (see Figure 2.3). The driver “drove” the vehicle while a computer simulated the appropriate location and the realistic scenery in the driver’s front view (a computer screen). The cell members in this C2V did not experience any vehicle motion, but vehicle noise did interfere with their communications. Within the vehicle, the cell members had internal audio communications using headsets. While communicating to another command cell (located naturally in another vehicle), a cell member talked to his functional counterparts over

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Figure 2.5. The relations and internal organizations of CAT and CAUs command cells. Here CMD = commander; BM = battle manager.

their own radio channel (e.g., the intelligence manager of CAU-1 would talk to the intelligence manager of CAU-2). THE COMMANDER SUPPORT ENVIRONMENT Rich in sensors and networks, the Blue force provided the command cell, such as Captain Johnson and his battle managers, with an enormous quantity of information. This flood of incoming information could be both a blessing and a curse. It was a blessing because it did help Captain Johnson to know where his forces, and the enemy forces, were located and what they were doing. He was able to avoid threats and to destroy them before the enemy even detected the presence of friendly units. On the other hand, the massive inflow of information could be a curse, an overwhelming, disorienting cognitive burden, if Johnson did not have effective tools to make sense of this information. Similarly, the high proportion of autonomous, unmanned, robotic assets within Johnson’s CAU was a double-edged sword. The autonomous sensors, such as UAVs, were able to collect, prefilter, and communicate vast amounts of

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useful information about the enemy. The robotic shooters, such as unmanned mobile cannons, fired rapidly and accurately at their designated targets. The human warriors stayed further from harm’s way and dedicated themselves to less mechanical, more creative tasks. However, robotic platforms also required extensive amounts of information—accurate, highly detailed orders—from Johnson’s battle managers. Here was another potential curse: the command cell had to feed their efficient but relatively brainless robotic warriors with an exorbitant amount of command information. Without a powerful tool, the command cell would not be able to generate such a complex, voluminous output. Fortunately, our Blue command cells had a suite of helpful tools—the CSE, a key product of the MDC2 program (Figures 2.6 and 2.7). It was the CSE that processed the flood of incoming information and reduced it to a manageable set and presented it to the cell members in an easily understandable manner. And it was also the CSE that translated high-level guidance and the commands of Captain Johnson and his battle managers into highly detailed, precise instructions to the robotic warriors. In Figure 2.3, you see two displays in front of each command-cell member. These screens, interfaces to the CSE, could be reconfigured and personalized according to the cell member’s tasks and personal preferences. Typically, the primary content of the screens was the visualization of the common operating picture, an automatically updated and integrated (fused) picture of friendly, neutral, and enemy forces. The information used to populate the common operating picture came from the unit’s organic and higher-echelon sensors. Because all displays were networked and drew the underlying data from a shared database, an update to one display immediately updated the display on all other displays. In addition to integrating and displaying the available information about the conditions of the battle, the expert system and intelligent agents within the CSE reasoned about the intelligence report assessments that correlated and fused detailed information. This included such considerations as the enemy status (e.g., fuel, ammo status, and health), alerts, planning versus execution comparisons, current tasking, and more. In particular, once Captain Johnson or Sergeant Rahim entered configurable alerts into the CSE, the system would notify them when, for example, an enemy force within an area of interest exceeded a certain size. Other types of critical events, derived from the Commander’s Critical Information Requirements, were handled similarly. To help keep track of the Blue forces, the CSE’s monitoring tools provided feedback about the status of individual assets and of the echelon as a whole. The CSE’s expert system also allowed Captain Johnson and his staff to task and control their organic assets or groups of assets and to perform maneuver, sensing, or shooting functions. A cell member communicated his intent to the CSE using a set of warfighter-configurable rules. When the configured set of conditions was met, CSE executed the predefined actions or recommended actions to the designated cell member. When the cell member approved one

Figure 2.6a. Some of the tools of the CSE.

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Figure 2.6b. Key functions of the CSE.

of the suggested actions—usually with a single click—the CSE translated the chosen action into detailed, specific instructions to a specific manned or unmanned asset—to fire a missile, to perform reconnaissance of a specific target, and so on. Sometimes, the actions were set to execute automatically, without staff interaction, when a set of predefined conditions was met. This enabled the warfighters to take advantage of fleeting opportunities and to minimize the risks of suddenly emerging threats in the battlespace. We will talk about the CSE in great detail in chapter 3, and about implications of the use of the CSE throughout this book. A TYPICAL EXPERIMENT Over the course of several years, we performed a total of eight experiments. Each experiment took multiple months to prepare and weeks to execute. An experiment involved multiple battles (we called them runs), each taking several hours to complete. Although most runs were based on a common terrain, the force structure, and general situation, the specifics of Blue and Red missions and dispositions were unique in each scenario. Each scenario was designed to address multiple experimental objectives and forced the commanders into different tactical dilemmas. At the beginning of each scenario, the commanders and their staff members would go through detailed collaborative and individual planning. Several scenarios included

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Figure 2.7. A typical CSE screen in front of a command-cell member.

fragmentary orders part way into the runs to force dynamic replanning to meet the new objectives. In general, the Red force was significantly larger and more heavily armored, although their vehicles were slower, their weapons and sensors had shorter ranges, and their C2 capabilities were less sophisticated than the Blue’s. Defeating such an enemy with a smaller, more lightly armored, but more agile and better informed force was the common dilemma of the Blue forces. In designing the experiments, we made an early decision to look at this experimental program as one of discovery instead of hypothesis testing. The most significant implication of this decision was that instead of focusing the analysis on determining if a particular hypothesis was true or false, we instead explored significant factors and their relations—for example, the information requirements of the cell members, or which CSE features were most effective, and why. The results of our experimental analysis influenced enhancements to the experimental tools and often led to generalized findings that would be pertinent to a range of future battle-command approaches and tools. To focus our data collection and guide the direction of subsequent analysis, we developed a core set of essential elements of analysis—the key questions the experiments were to answer. An analytic decomposition of these elements enabled us to carefully construct each experiment to ensure that the required

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data elements were collected. To provide a valid operational context, we based our scenario design on the U.S. Army TRADOC Caspian Sea scenario and staffed the command cells mainly with active-duty military officers and soldiers. Each scenario in an experiment was based on a common “road to war” story, which provided context for the upcoming battle and used the same basic force structures. Let us discuss these conditions in some detail. Mission The commanders and their staff planned, rehearsed, and executed one or two tactical missions each day. The scenario was derived from a collection of unclassified Caspian Sea scenarios set in the country of Azerbaijan circa 2018 and was designed to force analytically significant dilemmas and command decision making. The precise mission for each run varied, but the friendly force was consistently on the offensive and had a terrain-oriented mission (i.e., secure an area, clear a path, etc.). The enemy mission varied more substantively: in some runs the Red force was to exfiltrate across the border to Iran, in some they defended a region, and in others their priority was to destroy the Blue forces. Neither side was aware of the specific mission of its opponent. Enemy The Red forces, operating independently and unconstrained tactically, were a mixture of Azeri army regulars, special-purpose teams, and insurgents. The militants of the AIB made up the insurgent forces that attempted to overthrow the pro-Western government. The AIB subverted control of the Kura

Figure 2.8. Essential elements of analysis—the key questions the experiments were to answer.

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Brigade—a unit of the Azerbaijan army—from the Azeri government. This brigade was composed of four motorized rifle battalions (MRB), one selfpropelled artillery battalion, and a surface-to-air missile (SAM) battery. At the beginning of each scenario run, these elements were at approximately 50 percent strength. Two of the MRBs were outside the Kura River Depression and were not represented in the simulation. Special Purpose Forces (SPF) from the AIB worked with the Red commander in the region and operated in four-man dismounted teams. Additional fighters in the area belonged to the NKILO, which was not directly aligned with either the AIB or Azeri regulars. The NKILO forces carried guns but were not combatants, and they did not report to either Red or Blue forces. Allegiance of NKILO was suspect and highly dependent upon clan alliances. Figure 2.1 shows the force structure of the Kura Motorized Rifle Brigade. Terrain The area where the battles took place—the so-called terrain box—was typically located in the Kura River Depression in present-day Azerbaijan (see Figures 2.9 and 2.10). The terrain box varied in size from experiment to experiment based on the size of the force involved. The largest terrain box was approximately 100 kilometers from north to south and 100 kilometers from east to west, with the Kura River running west to east through the center of the region. The Kura River Depression region of Azerbaijan is remarkably flat with elevation variations of –5 to +15 meters from sea level. The region is mostly covered by sandy or hard-packed soil and is primarily an agricultural area for grains and native crops. Large, thickly wooded areas are dispersed throughout the area, and the region includes a large swamp in the south-central region of the depression. In order to increase the complexity of the experimental battles, the realworld terrain was modified. Enhancements included over a hundred built-up areas, dozens of mosques, 11 cemeteries, 36 national monuments, and 4 displaced persons camps. To stay within the constraints of the simulation system, the size of most of the built-up areas—very modest hamlets—was intentionally limited to 6 buildings per area. See Figure 2.10 for a graphical representation of the experiment terrain. Different colors indicate terrain features such as mountainous areas, marshy areas, farmland, lakes and rivers, water, and impassable swampland. The legend describes the cultural features, and the numbered areas are the towns and small built-up areas. Troops The organization of Blue troops used in the experiments varied according to the context of the experiment. In early experiments, a single CAU was rep-

Figure 2.9. The terrain box used in the experiment was set in a Caspian Sea region.

Figure 2.10. To increase the complexity of environment, the terrain box included a variety of additional, fictitious features.

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resented. In Experiment 5, we added a subordinate platoon with two squads of infantry. In Experiments 6 and 7, we added a second CAU and a higherheadquarters (CAT). For the sake of realism, the CAT commander reported to a Brigade Combat Team (BCT) commander and was one of three CATs represented in the mission orders that came from the BCT commander. The BCT commander controlled several sensor assets and High Mobility Artillery Rocket Systems (HIMARS), had intermittent access to joint assets such as F-117A fighter assets, and received information from a two-man Special Operations Force team operating in the BCT Area of Responsibility. Each CAU included a platoon led by a platoon leader who controlled two nine-man virtual squads. See Figures 2.2, 2.11, and 2.12.

Figure 2.11. Overall organization of the Blue force. Gray shading shows the forces represented with live personnel. Squads were computer simulated. Other forces were not represented in the experiments. The Appendix describes the equipment.

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Figure 2.12. Organization and equipment of BCT and CAT. The Appendix describes the equipment.

Civilians on the Battlespace To add complexity to the decision-making environment, and to increase the challenge of understanding the battlespace, we placed dozens of civilians on the battlefield. They drove trucks similar to the enemy vehicles and moved about in the towns alongside the insurgent forces. The Blue force had to interact with the civilians in accordance with the rules of engagement and in general had to avoid harming and antagonizing civilians. Time Mission planning was allotted two hours, mission execution was up to four hours. In most cases, the Blue force was ordered to accomplish its objectives by a specified time limit. A TYPICAL BATTLE HISTORY As an example, consider a fairly typical experimental battle, Run 6 of Experiment 6. The Red force is positioned south of the Kura river, with the bulk of its assets in the western part of the area shown in Figure 2.13a. They expect that the Blue force is about to attack from the positions north of the river. The Red commander’s plan is to delay the Blue force while exfiltrating his forces across the international boundary (southern part of Figure 2.13a) into friendly Iran.

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The Blue force plans to prevent the Red exfiltration. To this end, one CAT (not explicitly modeled in the experiment) will secure the western part of the international boundary while CAT-2 (the force modeled in our experiment) will take key objectives (MEAD and SHERMAN) in the central and eastern part of the area, thereby enveloping the Red force. CAT-2’s sensor assets will be the first to cross the river, to probe the Red positions, and to set the necessary conditions for the subordinate units to begin maneuver. CAU-2 will then initiate the main effort toward the objective SHERMAN (in southeast) while CAU-1 will execute the supporting effort and take objective MEAD (center).

Figure 2.13a. An example of an experimental battle: Blue initial plan. See Appendix for explanation of abbreviations.

Figure 2.13b. An example of an experimental battle: Blue change of plan.

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Initially, the battle unfolds approximately as expected. By H+16 (i.e., 16 minutes after the start of the operation), CAT’s aerial reconnaissance elements are crossing the river, and shortly thereafter, at H+37, CAU-2 secures the river crossing site. At H+42 CAU-1 also exits its assembly area heading toward the crossing site. At that point, at H+46, the brigade headquarters sends CAT-2 a major redirection: because CAT-1 failed to secure the southern boundary and block the Red force, the enemy is presently escaping south, and CAT-2 is ordered to attack into the western part of the area, to pursue and destroy as much of the fleeing Red force as possible (Figure 2.13b). Remarkably, using their respective CSE facilities, within the next 10 minutes, all three command cells—CAT-2, CAU-1 and CAU-2—manage to formulate and coordinate a new plan. By H+73 CAU-2 has already turned onto a new axis in the south. However, its knowledge of the enemy situation in the western part of the box is rather limited—until this time, all reconnaissance assets were focused on the central and eastern areas. Now they have to reorient their reconnaissance assets—especially UAVs—to the western area. The Red command notices the change in the UAV routes and by H+85 already notifies the Red forces— correctly—that the Blue has reoriented toward the southwest. Lacking situation awareness and sweeping with UAVs ahead of their main forces, CAU-1 and CAU-2 advance cautiously, even though in reality most of the Red force has already retreated further south. By H+162, the experiment control cell announces the end of the experiment. The bulk of the Red force successfully escapes. INFORMATION PROCESSING, SITUATION AWARENESS, AND BATTLE COMMAND Such experiments brought a rich harvest of findings. Among them, the role of one factor emerged as the most dominant and pervasive—situation awareness. In our experiments, it was situation awareness and its impact on the commander’s decisions that often determined the outcome of a battle. The fate of a unit was largely dependent upon how the commander and staff deployed sensors and forces to fight for information and exploit the unit’s advantages, if any, in situation awareness. Although the MDC2 program was probably the first systematic experimental study to obtain extensive quantitative data on the role of situation awareness in battle command, the qualitative recognition of this role has a long history and is well established in military doctrine (Figure 2.14). Information, as an element of combat power, “enhances leadership and magnifies the effects of maneuver, protection and firepower.”19 Discovering and measuring quantitatively how information influences a commander’s situation awareness and impacts his decision making has been a key objective of this program’s experiments.

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Figure 2.14. The combat power is critically dependent on leaders’ situation awareness (after U.S. Army Field Manual 3.0, Operations, p. 4-4).

The dominant role of situation awareness (i.e., the ability to obtain the necessary information about the situation in which a military force operates) should not come as a surprise. In fact, it has been long been argued that the very nature of command organizations has been historically driven by the need for situation awareness. In the world of warfare, an influential historian argued that “the history of command can be understood in terms of a race between the demand for information and the ability of command systems to meet it.”20 It also should not be surprising that the solution to the problem, in all ages, had much to do with technological innovations in information processing. Consider the Napoleonic revolution in battle command. In the large-scale operations of the Napoleonic age, the enormously enlarged and geographically dispersed armies engendered massively increased flows of information. The emperor could no longer be in person with every corps; he needed detailed reports. The task of transforming these formidable inflows of information into adequate situation awareness was too difficult even for a genius of Napoleon’s caliber. To solve the problem, he introduced a system of remarkable innovations, technological in nature even if the technology was based on humans and paper.21 He devised sophisticated databases—a system of formalized reports, specialized summaries, and custom-designed cabinets for efficient storage and retrieval of such information. He institutionalized the use of a relatively recent technological development—accurately triangulated and mass-produced maps— as a media for time-space modeling and analysis of strategic movements.22

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To process the incoming reports and outgoing orders with greater speed and accuracy, he devised a system of specialized human information processors— staff officers—responsible for formally decomposed and allocated sets of functional tasks. This suite of technological innovations—based on paper databases and human information processors—was at the core of Napoleonic battle-command revolution. In the world of industrial management, it was also long recognized that the structure and processes of an effective organization are driven by the need to transform large volumes of information into useable forms—situation awareness and decisions. The ability of a decision-making organization to produce successful performance is largely a function of avoiding informationprocessing overload,23 not unlike what we saw in the Napoleonic invention of a new battle command. Thus, in the 1990s, globalization and computerization drove massive changes in industrial and commercial management—reduction in layers of management, just-in-time operations, and networked structure of enterprises. In short, the chain of influences works as follows. New conditions of warfare (such as Napoleon’s large, distributed corps) both engender and demand more information. More information challenges the ability of the old command system to transform it into actionable situation awareness. To resolve the challenge, a capable military develops or adopts new information-processing technologies (such as Napoleon’s paper databases and specialized human processors), with suitable organizations and procedures—a new battle-command system. Captain Johnson’s command cell was a product of a similar chain. Given the unusual degree of Blue forces dispersion and their physical separation from the enemy, and with the confusing flood of information produced by multiple sensors and networks, can Captain Johnson and his battle managers maintain an effective level of situation awareness? A key hypothesis of the MDC2 program was that they could, if provided with an appropriate suite of tools, such as the CSE.

CHAPTER 3

New Tools of Command: A Detailed Look at the Technology That Helps Manage the Fog of War Richard J. Bormann Jr.

Skip this chapter if computer terminology bores you. The subsequent chapters are quite understandable without the heavy technical content of this one. On the other hand, for a technically minded reader, this is a great place to learn about the nuts and bolts of the network-enabled battle-command tools actually built and tested in the MDC2 program. Let’s begin by introducing two important abbreviations. Battle Command Support Environment (BCSE) is the overall system we build to perform the experiments with battle command within he MDC2 program. It includes many diverse components with a range of capabilities. A large part of these capabilities are the functions that actually support a commander cell like Captain Johnson and his battle managers. That set of functions is called the Commander Support Environment (CSE). In this chapter we will describe the entire BCSE. In other chapters, we focus almost exclusively on CSE only. The BCSE is an execution-centric command and control (C2) decision support system for cross-functional, collaborative mission planning and execution. It provides a common operating picture (COP) for enhanced, realtime situation awareness (SA). It supports multiple echelons, from battalion down to the individual mounted and dismounted warfighter level, including both manned and unmanned platforms. Using the BCSE, command cells control manned and robotic assets in a network-enabled, cross-functional environment in response to rapidly changing battlespace conditions and digitally share the changes across organizations in real time. In developing BCSE, we pursued a number of objectives centered around a network-enabled approach to battle teamwork. All assets within the command cell are able to collaborate and share their view of the world. The information

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is shared by every human and every robotic system within the commander’s control, so that they can each operate under the same assumptions. Even though, with inevitable periodic losses of communications, perfect instantaneous and continuous sharing of information may not be always possible, the system does its best to keep the information stores as up to date as possible. In its current form, the BCSE is based on a three-tier C2 architecture with integrated Battlefield Functional Areas (BFA). The BFAs handled directly within the BCSE include Maneuver, Intelligence, Effects, and Logistics. The three-tier architecture provides decision support (1) at the warfighter graphical user interface, (2) among multiple networked assets, and (3) at the individual asset level. The decision support capability is distributed across the command cell while providing redundancy of the information model. The latter is important because it ensures that a loss of any one asset does not result in the loss of existing information and keeps the information store from becoming a central point of failure. The integrated BFAs allow every member of the command cell, regardless of his functional specialization (e.g., military intelligence or logistics) the ability to pitch in and share the workload regardless of their assigned functional role, with the same tools and functions available to every member. If the information manager is overwhelmed while the effects manager has spare cognitive cycles, the effects manager can pitch in to help with intelligence tasks without switching computer screens or moving to other workstations. The architecture is also tailorable—it allows the users to tailor the system interface to their specific preferences and warfighting needs. In the following section, we begin by describing the architecture of BCSE in some detail. We then continue by highlighting some of the tools and features that are available to the warfighter and finish by describing the decision support system framework that at the heart of the BCSE. THE ARCHITECTURE OF THE BCSE Our main approach to the architecture design is to distribute C2 intelligence across the network as a set of intelligent agents that assist the members of the command cells. Intelligent Agents mean many things to many people. For the purposes of this chapter we use the definition by Gheorghe Tecuci: “An intelligent agent is a knowledge-based system that perceives its environment (which may be the physical world, a user via a graphical user interface, a collection of other agents, the Internet or other complex environment); reasons to interpret perceptions, draw inferences, solve problems, and determine actions; and acts upon that environment to realize a set of goal or tasks for which it was designed.”1 Unlike a conventional program, an intelligent agent should be able to do more than merely obey commands. Instead, it may be able to ask for clarifications and modify or even refuse some requests. It should employ a degree of knowledge of the user’s goals and needs.

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In the BCSE, there are three types of agents: • Commander and staff agents provide the commander and his staff with intelligent information that helps them understand the battlespace conditions and occurrences as well as giving them the ability to control the assets under their command. • Collective agents reside at nodes within the network and handle the coordination of multiple assets on behalf of the commander and his staff. Examples of a cross network agents include the Attack Guidance Matrix (AGM) and agents that maximize intelligence gathering through the coordination of multiple sensors based on the commander’s intent. • Asset agents enable each asset to understand how to carry out the commander’s intent and directives within the scope of the overall mission.

The BCSE contains automated functions that allow the commander to efficiently control his assets so that he can effectively focus on his mission. Figure 3.1 shows some of these automated functions provided by the BCSE. An example of an agent that provides employment of fires and effects is the AGM—it helps the command-cell members with a combination of threat analysis, survivability estimates, and weapon-to-target pairing. It can also automatically execute fires by issuing a command, for example, to an auto-

Figure 3.1. The main automated functional areas of the BCSE.

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mated unmanned mortar to fire at a particular target, automatically or semiautomatically, as instructed by a command-cell member. Such agents are usually distributed across the network. In Figure 3.2, for example, each of the so-called brains represents an intelligent agent that among other things contains a knowledge base and a reasoning engine. The knowledge base contains factual and heuristic knowledge about a specific domain. The knowledge base is made up of a data model representing the worldview as well as the rules that define the problem-solving paradigm. The reasoning engine, or reasoner, is responsible for applying the set of rules against the current set of knowledge to reach a result, develop a recommendation, or establish a new fact. Agents are widely distributed over the network. The most important reason for such a distribution is that the agent can benefit from the proximity to the object that it reasons about, or to the user which it assists. For example, if the agent resides directly with a robotic asset like a ducted fan unmanned air vehicle (UAV), the agent can make decisions on how to best perform a reconnaissance task taking into consideration the current battlespace conditions in order to determine how to best maneuver, what amount of risk is acceptable for the particular mission, how to position itself to capture the best imagery, how to avoid threat and react when threatened, and how to control its sensors. Knowing the current state of the battlespace and keeping the decision-making process close to the UAV enables it to make informed and educated decisions about itself without the need for a human to micromanage the UAV. Another advantage of distributed agent architecture is redundancy of the data model. While the data model resides within each agent in the network, communication disruptions and delays may cause the contents of the data model to differ. The system tries to keep the information as consistent across agents as possible. If an agent fails the data model will be recovered from another agents’ data model. Distributed agent architecture also eliminates a central point of failure because the knowledge base is not centrally located. A loss of a knowledge base instance is fully recoverable and is not catastrophic. On the other hand, distributing the agents across the network leads to a challenging question: who is in charge? If the agents are all making decisions on their own then there can be competition or a duplication of effort in achieving the mission or accomplishing a task. In the BCSE the commander and staff are always in charge and have control of every asset. They have the ability to micromanage the assets at any time; however, this is not the ideal situation to be in. As we noted when describing the types of agents, the Collective Agents handle the coordination of multiple assets on behalf of the commander and staff. Through the use of the Collective Agents, much of the coordination and micromanagement is automated, leaving the command cell to focus on the mission and not the minute details of asset control. A command-cell member can adjust the degree of autonomy given to the agents as he feels necessary. For example, if the command cell wants to give the AGM the authority to execute automatic fires against specified target

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types immediately upon detection of the target, they can instruct the AGM to do so. Conversely, the command cell can limit control, setting the AGM to provide recommendations only. In effect, a cell member has the ability to program and control the agents during the operation by modifying the rules of engagement, altering threat assessment and targetability criteria, and setting weapon-to-target preferences through the use of their user interface. This user interface is known as the CSE and will be explained in more detail in a moment. In the chapters that follow, we will use the term CSE to refer to all the functions that support the command-cell members, in order to exclude other parts of the BCSE that support command-unrelated functions, such as simulation. Now let us consider each of the three tiers of our architecture (Figure 3.2). Tier 1 of the BCSE provides decision support within the CSE. The CSE is the primary interface between the command-cell members and the rest of the system—it delivers the battle information to the cell operators, and receives the battle commands from the operators. The CSE assimilates a flood of digital battlespace information coming from intelligence reports and sensory information of assets reporting from the battlespace into a graphical picture so that the commander and his staff can quickly and easily understand what is happening. This graphical picture is known as the common operating picture (COP) (Figure 2.7 of Chapter 2). The CSE also provides information filters that a user can customize to control the type and quantity of information being received based on his information needs. The CSE includes a suite of tools that operators use to control and coordinate assets, tools like Task Synchronization Matrix, Threat Manager, AGM, Alert Tracker, and

Figure 3.2. The BCSE’s three-tier approach provides a distributed C2 decision support architecture. See Appendix for explanation of abbreviations.

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tools for reconnaissance guidance, intelligence and picture management, and Commander’s Critical Information Requirements (CCIR). In particular, the CSE includes a tool that visually presents to the cell members the COP (Figure 3.3). It uses both 2D and 3D maps and a military standard set of graphics, called MIL-STD-2525B, which includes, among other things, icons and graphical control measures. Real-time updates to the COP display friendly movement, detonations, missile fires, enemy positions, noncombatants, detections of unknown (possibly hostile) assets, battle damage assessment, and the logistical status (ammunition, fuel, and health) of friendly forces. We discuss this and other tools in detail later. One of the most intriguing features of the CSE is its integration of multiple BFAs. It allows every user to task and retask battlespace assets; monitor execution; and facilitate maneuver, reconnaissance, and effects management through a single graphical user interface. The BFA integration means that the role of the command-cell member does not have to be rigorously restricted to being only an information manager or only an effects manager, each with his own unique set of tools. Rather, each member of the command cell can configure the system to his needs, possibly fulfilling several functions (i.e., serving more than one BFA). Each member can determine threats, view intelligence information, classify and identify targets, maneuver, assign reconnaissance, perform BDA, and fire all from the same screen. This integration allows a reduced staff to operate according to need, not function. If a cell member is overwhelmed with a task or becomes incapacitated, others can step in and help out without having to change stations or computer screens. In fact, we observed in some of the MDC2 experiments how command-cell members used the BCSE to assign themselves roles that

Figure 3.3. The CSE utilizes a knowledge base to integrate the static, temporal, and spatial information into a coherent set of knowledge for the commander and staff to operate on across echelons.

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were well outside traditional doctrine. In one such an experiment, for example, the cell members divided their roles so that one maintained responsibility for maneuver, intelligence, and fires for the close fight while another handled all these functions for the deep fight. The CSE provides the following: • Visualization of Information: the real-time display and focal point for situation monitoring and understanding in forms such as 2D and 3D maps, graphics, icons, tables, and reports. • Collection management and logistical displays: the commander’s portal to the current status of each of his assets in the battlespace. • Command Center: the operators’ main control center for task creation and modification, mission planning, mission coordination, and collaboration. • Task Decomposition: the ability to break down a complex task into a set of individual tasks. • Terrain Analysis: the ability to understand the terrain, how it can be used, and how to maneuver across it. • CCIR Management and Display: the ability to specify and receive alerts, cues, and notifications in response to topics of critical interest to the commander and his staff.

Figure 3.4. The BCSE helps command cell in both planning and execution.

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Tier 2 contains agents that provide the commander and staff with specific decision support needs across the assets in their control. These agents are known as collective agents because these agents treat the assets within their control as a dedicated network of assets focusing on their combined goals. The collective agents function as the commander’s assistant by directing, coordinating, and synchronizing the assets to achieve mission goals. They also provide recommendations and assimilate disparate information into the COP. Each of these agents can be hosted on any asset equipped with the appropriate hardware anywhere within the command cell’s control and can move from one asset to another asset if required. The movement from one asset to another may occur for a number of reasons including the destruction or critical failure of its host asset. Each of the collective agents fulfills a specific need of the cell members, and therefore, there are many different types, which include those that manage schedules; provide guidance such as attack, BDA, and reconnaissance; report and manage threat information; fuse and synchronize data into information that the command-cell members can easily and quickly comprehend; provide critical alerts, such as potential fratricide or violations in rules of engagement; provide memory management, which is important in view of the large volumes of battle-relevant information in the network-enabled environment; and collect data for postbattle analysis. Tier 3 is a collection of resident agents—one at every asset controlled by the command cell. The idea is to provide a networked environment and communication mechanism such that each asset keeps the entire community aware of its current state while in turn being kept informed of the environment and its surroundings. As a result, each asset’s knowledge base is kept as synchronized as possible to the full state of the battlespace. This way, it can reason on how to maneuver, control its sensors, react to threat, and take initiative to accomplish the goals of the command cell. An important point to mention is that while each asset may know how to maneuver on its own (humans as well as sophisticated robotic platform have built-in reasoners for knowing how to move and avoid obstacles), the resident agents provide an understanding of how to maneuver in respect to the current command cell’s goals. They understand the mission, not just the specific task. The asset should be able to act for the good of the mission and not just for the good of itself; the asset must also understand the world around it. Besides merely breaking down the asset’s task into the necessary atomic actions, it must understand its purpose and goal as well as every other asset’s purpose and goal, and even the commander’s intent. This also enables the asset to carry out its mission when communications with the commander is lost and to react to threats in a way that is intelligent and meaningful, according to mission parameters. In order to react appropriately, it must also understand its role in the mission, what it can and cannot sacrifice for the sake of the mission, and how to avoid causing harm to other participants in the mission.

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The resident agents are aware of the environment that may be outside the viewpoint or perspective view of the asset’s sensors and therefore can help provide a better recommendation for which to maneuver. From that recommendation, the human and robot can use their brain or onboard navigation system to do the details. This is important because resident agents are not merely artifacts of a simulation. In fact, they provide as much to a live environment as they do to the simulated environment. Here we will attempt to clarify this with an example. Assuming a UAV is assigned to reconnoiter a potential target, it must understand the level of risk it can endure to reach the target and accomplish the task. The detection of an air-defense system might normally cause a reaction to flee, but if the mission dictates that mission success outweighs the risk of loosing the platform, then the system will decide to continue the mission despite the risk—fleeing is not an option. In other words, the UAV knows what it needs to accomplish, what risk it can take, and when to call off the task. The UAV must also ensure that when it does react, it doesn’t react in a way that may bring enemy attention to others—like fleeing back to base. The Tier 3 resident agents are currently provided in two forms, which are referred to as the Platform Support Environment (PSE) and the Soldier/ vehicle Support Environment (SSE). The PSE is used in unmanned (robotic) assets to provide system guidance and control. Using a knowledge base, with its set of rules, it translates mission related tasks into directives understood by the robotic system’s control software. For example, the commander requests a particular area to be searched for enemy. The collective agents assign asset X to perform the task due to its availability and proximity to the area. A route and set of sensor controls to accomplish the task is determined by the collective agents and passed to the resident agent on asset X. Assuming asset X only understands directives in a specific message format of segments and speeds, the agent formats the appropriate messages and sends them to the robotic system’s control software. As the asset moves out on its task, an enemy indirect fire system is detected by another asset on the network. The resident agent on asset X realizes that the enemy asset’s attack range intersects X’s route. As a result, the resident agent generates a new route around the danger area and notifies all the other agents on the network (including the command-cell members) of its course change and expected time of task completion. Again the agent formats the appropriate messages and sends them to the robotic system’s control software. The robot reroutes and completes its task without the cell member’s intervention. Without the resident agent, the asset would never have known about the danger. The SSE provides assistance to the soldier by providing recommendations on reconnaissance and fire support, alerts and cues, and situation awareness through the development and display of the COP (Figure 3.5). Generally, an agent has a way to display its output to the humans. The human warfighter has his own brain to make decisions and to break down a

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Figure 3.5a. The SSE provides C2 and decision support to the dismounted warfighter.

task into subtasks. The warfighter can only make decisions on what is actually known to him (unless he is guessing). The knowledge base knows the overall known state of the battlespace. It knows the global situation and what every asset is currently and expected to do. Decision tools, using the knowledge base, can keep track of information that is important to the warfighter, figure out when situations are occurring that are important to the warfighter, and make recommendations to the warfighter—all based on detailed information that may be difficult or tedious to keep track of when your focus is on survival. So, there needs to be a good way to interface the output of the decision tools to the warfighter so that he can use his brain to accomplish the task at hand. The soldier support environment uses a visualization display to show the COP and alert the commander to critical events. The agents interface to the display. Now, having discussed the BCSE system, the humans that use it to command the assets, and the assets themselves, let us not forget about the all-important real world within which all this operates. A system like BCSE, along with its commanders and assets, would normally exist in a real battlespace populated by real terrain features, enemies, neutrals, other friendly entities, and so forth. However, in the MDC2 program, we did not have the luxury of experimenting with BCSE in a real battle or even a field exercise. Instead, the real world was simulated by an advanced battle simulation system called OneSAF Testbed (OTB).2 It was OTB that simulated such physical events as movements of assets, their fires, and effects of fires. The overall simulation suite also included sensor effects servers (these simulated, for example, whether a

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Figure 3.5b. Another view of the SSE for the dismounted warfighter.

particular UAV would be visible to a radar system under the given conditions) and imaging severs that generated simulated imagery, for example, how an enemy tank would look to a UAV’s video camera. Because the world (including the assets) was simulated, each PSE resided not on a real asset but was instead connected to its simulated asset in OTB. Figure 3.6 depicts the architecture used in one of the MDC2 experiments. The circular part of the diagram on the right shows how multiple cells communicate—they are linked together on a shared C2 Internet. Recall that a cell consists of a commander and his staff. In this example configuration, there are 10 cells that include one higher headquarters (HHQ), one battalion (Bn), two companies (Co), two platoons (PL), and four squads (SQ). Each cell also owns the items represented in the callout on the left. Following the threetier architecture, the cell contains C2 agents that handle platform support at the robotic level (PSE), soldier support for the dismounted warfighter (SSE), vehicle support (VSE) for mounted warfighters, commander support for the commander and his staff (CSE), and collective agents providing support across

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Figure 3.6. The BCSE architecture and the decision support components used in Experiment 7.

the assets (CA). The architecture distributes the C2 elements at three levels or tiers: the commander and staff (CSE), across the networked components (CA), and at the individual assets (PSE, VSE, SSE). The reader, we are afraid, may still be uncertain of how all this works together. We will explain this later via an illustrative example. But before we do so, let us take a closer look at the functions and tools within the most important part of the system—the CSE. WARFIGHTER’S COMMAND FUNCTIONS AND TOOLS WITHIN CSE Planning Missions and Courses of Action (COAs) The command-cell member (let us call him an operator for the purposes of this section—he operates the functions of CSE) may create a new mission definition working individually or in a collaboration session simultaneously with his commander, peers, subordinates, or with higher headquarters. Each mission can contain one or more COAs, which include the military assets, in the form of units and platforms, associated with the COA, the organization of those assets, their set of tasks, military graphics, terrain overlays, and more. Having completed the analysis of a mission, the operator selects a COA to be downloaded to all the decision support entities within the command cell. Every mission can be saved and reloaded at any time. The saved mission is

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stored in an XML format. A mission wizard is provided to aid the operator in the process of mission creation. The functionality related to mission analysis resides in the Mission Workspace. From the workspace, the commander and staff can dynamically task organize and reorganize the assets during planning and execution. Its tree structure allows users to see the hierarchy of assets and ownership of Graphical Control Measures (GCMs). The Mission Workspace is directly tied to the map, so that a click on an asset, GCM, route, or asset is highlighted on the map. During execution, the workspace is automatically updated with new detections, munitions, target recognition results and asset status. Additionally, the Mission Workspace allows the users to access a suite of tools for creating and maintaining GCMs, creating tasks, and managing an array of maps and geographic overlays. Visualization The CSE shows all operators a complete and up to date view of what is known about the battlespace, during both planning and execution of the mission. Individual operators can access, view, configure, and tune their view, workspace, and processes in ways that support their thinking. Icons and markers allow the operators to quickly see the shape and status of the battlespace. • With just a single glance at the map, the operator can see new detections. A detection is a report stating that a sensor has detected a suspicious object (e.g., a possible enemy tank), with details on when, where, by what sensor, and any available imagery of the object. • With another glance, the operator can see enemy targets, the target status, including identification (e.g., is it a tank or an infantry team), engagement status (whether and who fired at the target), and battle damage assessment (is it destroyed or damaged, and to what extent). • The operator can also see the locations of every asset, its tasks, routes, and sensor coverage. • Each platform on the map also has a tooltip that shows its fuel consumption, speed, location, heading, and other pertinent information.

In general, data in the system is supplied to the operator in graphical, textual, or mixed views based on the operator’s needs and preferences. While the map of the battlespace, with its graphic representation of graphical control measures and enemy, friendly, neutral, and unknown assets, is used mostly for general situation awareness, the other views may present more detail. The system provides multiple ways to see the same data. For example, the operator may see the fuel left on a platform by moving the mouse over the platform on the map to bring up a tooltip, by bringing up the Unit Viewer and clicking on the platform, by looking in the combat power status or by looking in

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the resource allocation tool. Each icon is decorated with special adornments. These decorators indicate additional information about the object. Decorators for an enemy asset, for example, include symbology to show engagement status, BDA status, image availability, the sensor type that last detected the asset, its direction of movement, and more. Customization of User Interfaces The CSE offers the operator multiple methods of customizing his workspace. The operator can change and save his personal settings of tools, toolbars, views of information, zooms, task-organization settings, alerts, attack guidance, and BDA guidance. In addition, the system provides the operator a series of data filters. This is important because all data, with its great volume, is available to all authorized operators, and can readily overwhelm any given operator. Filters also help the operator to create an optimized view of the battlespace that meets his specific data needs. For example, the operator may show or hide assets, routes, or GCMs either individually or as a group. The operator may also elect to fade assets that are dead and munitions that have detonated. Such customized views (also called map sets) also provide the operator with geospatial references and tools for terrain measurement and visualization. For example, the Line of Sight (LOS) suite of tools helps in intervisibility analysis. With another tool, the Geographic Intelligence Overlays, the operator can specify geographic regions with associated information (such as political, military, economic, social, and infrastructure characteristics) about the region. Later, this information may be retrieved and displayed as a tooltip. Briefing Operators use the Briefing Tool to view and share mission-planning data and intent. It is a “whiteboard” shared by multiple operators during planning and execution in order to exchange information. This capability is particularly critical for allowing the commander to share his vision or view of the current or future battle with other commanders, staff, and subordinates. The operators share overlays related to mission plans, change OPORD and situation templates, and save the briefing layers. Individual function-specific plans may be merged into a single plan, changed during execution, and shared at any time with selected personnel. This feature also allows the operators to enter the mission statement, commander’s intent, and task and purpose statements. To avoid the confusion between multiple operators of the graphic space, the operators can add personalized graphics and icons and color code their pointer and graphics. There are two additional tools to aid in communication and coordination between commanders, and between a commander and his staff. The ViewSync tool allows an operator to synchronize his view of the battlespace with another

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operator’s view. The heads-up display tool allows an operator to project his screen on a shared monitor visible to all members of a cell. Situation Awareness The Threat Manager shown in Figure 3.7 provides the operator with all identified threats in a tabular display. This information is determined by the intelligence information that has been correlated and fused by the commandcell members and the fusion agents. It is their perception of the threat and does not represent ground truth. The Threat Manager includes the following information: • Threat Name—user specified name and military type of threat such as SA-13 or Draega. • Unit Status—indicates the level of knowledge about the threat (e.g., suspected, identified, and targetable), • Threat level—qualitative level of threat ranging from low to high. • Damage status—indicates the current perceived health of the threat, which includes information such as destroyed, mobility-kill (e.g., a tank cannot move but can function otherwise), firepower-kill (e.g., a tank cannot fire but otherwise functions), unknown damage, and more.

Figure 3.7. The Threat Manager identifies enemy threats and provides access to the AGM, BDAGM, and Intel Viewer.

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• Threat type—indicates type of threat such as air defense, indirect fire, direct fire, and so forth. • Friendly assets within range—lists the friendly assets that are being threatened. When an operator selects a threat by clicking on it, the map shows lines extending from the threat to the friendly assets being threatened. • Engagement status—information about engagements that have been executed against the threat. • BDA status—presents information about the last known reconnaissance against the threat since its last attack and whether the reconnaissance is scheduled, in progress, or complete. When the operator clicks on the BDA Status, the BDA Guidance Matrix recommends an asset to perform further reconnaissance on the threat. • Show Image—indicates the time when the most recent image of the threat was taken and a marker if the image has not been viewed by an operator. By clicking on this field, the Intel Viewer is displayed allowing the user to view and classify the image. • Engagement Status—indicates the last known engagement (attack) information on the target and its status indicating whether the engagement is scheduled, in progress, or complete. When the operator clicks the Engagement Status field, the AGM recommends an asset to perform another attack based on the command staff’s AGM settings.

The Resource Availability tool is a textual, tabular display that gives the operator the following information on all friendly assets: name, damage status, fuel remaining, sensor being used, percent of task completed, speed, heading, altitude, and location. Double clicking on the name of an asset centers the map on the asset and highlights it. The Collection Management tool is a textual, tabular display that gives task information for friendly assets: asset name, task, target (for a fire or reconnaissance task), start time, end time, purpose, percent complete, and task status. It is a quick way to check a platform, see all tasks assigned to that platform, and the status of each task. Double clicking on the name of an asset centers the map on the asset and highlights it. Tasking To issue a task to an asset, the operator clicks the right mouse button on an asset shown on the map, or on the Execution Synchronization Matrix (this will be described a little later in this section), or on the mission workspace. The click brings a context sensitive menu that shows the possible tasks that can be issued based on the current situation. Having selected a task, the operator is presented with a tasking window where the operator can specify the information and parameters that are specific to the execution of the task. This includes specifying intent, waypoints and schemes of maneuver, task duration, dependencies for start or completion of the task, use and operation of sensors and weapons, and terms of task completion. All platform tasks give the operator an option to be notified upon task completion. During planning,

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the operator often animates the tasks and sees how assets move on the map. During execution, operators add, delete, or modify tasks as the battle situation changes. Fortunately, many of the tasks are high level tasks for which the operator needs to input very limited information—only the intent. The system then automatically generates the rest of the tasking information. For example, to reconnoiter an area, the operator selects only the platform, the area to recon, the flight area, the sensor, and altitude. The system then determines the best route for the best coverage. A task that requires a ground maneuver invokes the terrain analysis components to automatically generate the best route to meet the operator’s intent (fastest, shortest, and most concealed) and to avoid terrain obstacles. Individual platforms usually receive tasks related to reconnaissance, maneuver, or fires. The platform task menu is context sensitive, which means that only those tasks suitable for the platform are represented in the menu. For example, the currently available movement tasks for a reconnaissance, surveillance, and target acquisition (RSTA) unmanned ground vehicle (UGV) are Move, Halt-Resume, Overwatch, Reconnoiter Route Reconnaissance, Area, Auto Reconnaissance, Locations Reconnaissance, Targets Reconnaissance, Follow and Pursue. When multiple assets are involved, an operator can create groups of assets that are to work together for tasks such as maneuver or reconnaissance. The group may also be tasked as a formation, moving the vehicles in a formation pattern established by the operator. With this technique, the operator specifies the route, a stand-off distance, and a pattern, such as column, wedge, herringbone, line, echelon left, or echelon right. The operator can change the formation later during the execution. Clicking on an asset able to fire weapons brings a menu with appropriate choices. The Quick Fire tool brings up the appropriate fire options for the selected asset, while the Prohibit Fire tool allows the operator to mark this asset as a “Do Not Fire” asset. If a fire task is assigned to this asset, the system issues a warning, which the operator may choose to override. The sensor control allows the operator to manually turn sensors on and off. However, some opportunistic reconnaissance tasks override the settings. Sensor direction can also be changed using the 360 degree reference. This works well for stationary platforms that use sensors such as GSR. The Execution Synchronization Matrix is a customizable user interface module that represents the COA tasks in a Gantt chart format. The matrix graphically shows each task’s start time, end time, and duration, the status of each task (planned, completed, in progress, off schedule), and the interdependencies between tasks. Each task can be further examined by double clicking on the graphic representation and displaying the task details. For example, a Targets Reconnaissance task may take 25 minutes to complete. Further examining the task, the operator can see that it is made up of three Target Reconnaissance tasks where the first is to reconnoiter a Garm and will take

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15 minutes to complete, followed by a nearby truck, which will take 2 minutes to complete, and finally a Draega, which will take 3 minutes to complete. The remaining time is to position itself out of harms way. Automation of Fires The AGM is a tool that automatically monitors the enemy targets that become known to the BCSE and, following the operator-specified rules, generates and issues commands (or recommendations) to fire at the targets. The AGM integrates fires and effects with intelligence, maneuver, and logistics. It does this as follows: tracks movement and location information about a potential enemy assets, reasons about the enemy assets capabilities to hurt the friendly forces, reasons about the currently available friendly assets and ammunition, determines the friendly assets that are being threatened by the enemy asset, determines if the enemy asset is a valid target using rules supplied by military intelligence experts, and pairs (allocates) the friendly weapon systems and munitions to the targets. The AGM is aware of the friendly forces in the area, No Fire Zones, and No Fire Lines when computing the firing solution. The operator uses the AGM tool in the CSE to enter the criteria, which will later be used to determine if an enemy asset is targetable. It does this by setting criteria such as how sure we know the target is the type we believe it is (identification confidence) and how sure we know where it is (CEP). The operator can also specify the priority order in which munition types should be selected to attack a given target, the number of munitions to use against a target, and much more. Figure 3.8 is shows the AGM Tool. Underlying the AGM tool in the CSE is a collective agent called the AGM agent. This agent takes the inputs made in the CSE and modifies the rule parameters in its knowledge base to provide recommendations and perform actions based on the operator’s specification. There is typically one AGM per command cell. The AGM agent is capable of coordinating its guidance with AGM agents of other cells. The extent of coordination between AGMs belonging to different cells can also be controlled through the CSE. Additionally, there are loadbalancing rules that can be set to control the types and amount of munitions that can be used by the AGM. An important aspect of the AGM is that the operator can activate a different AGM set of criteria at any time before and during a mission. The operator can also create new AGM criteria sets, change criteria sets (both active and inactive), and share criteria sets with other operators in and out of the cell. Manual Execution of Fires In addition to using the AGM, the operator can manually issue a command to fire at an enemy target using the Quick Fire tool. The Quick Fire tool allows the tasking of any armed, direct or indirect fire asset in the cell. Each platform in the cell capable of accepting the task to fire has a tab in the tool

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Figure 3.8. The CSE provides the command-cell member with an interface to the AGM for controlling the rules for automated and recommended fires.

with the current and allocated munitions count. The operator selects a target or location and clicks the button to fire. Any warfighter can make requests for fire during mission execution. For example, a Long Range Surveillance (LRS) soldier can issue a call for fire on a target. Or a CAT commander may call in joint fires from an F-117A. The Request for Fire tool displays the request and allows it to be accepted or denied by the organization that controls the fire assets. Intelligence Management The CSE offers a suite of tools to help the operator organize and act on incoming detections and intelligence. The Picture Viewer allows the operator to customize a presentation of images provided by various sensors by specifying the sensor-carrying assets he wishes to monitor. As new pictures (IR,

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DVO, SAR) taken by that asset come in, they are added to the presentation. By selecting the history for the asset, the operator can see all pictures taken by that asset. With the Intel Viewer, the operator can examine images of battlespace objects that have been detected, as well as the history of intelligence information, such as the object’s type, affiliation (e.g., enemy, neutral, unknown), damage state (e.g., destroyed, firepower-kill, unknown), how sure we know what it is (e.g., suspected, identified, targetable), and its classification (e.g., air defense, heavy tracked, wheeled) (Figure 3.9). The term battlespace object is used here to refer to a physical object of interest in the battlespace. This includes enemy, neutral, and unknown assets as well as people, bunkers, buildings, bridges, and more. The intelligence information that is displayed comes from several sources, including automated sensor fusion, information correlation, and updates based on previous operator interaction with the Intel Viewer. Previous operator interaction refers to the operator’s ability to use the Intel Viewer to update the intelligence information listed above. The Intel Viewer adheres to the principle of integrated Battlefield Functional Areas since the operator can request recommendations from the system for tasks of reconnaissance or fires and then issue the command to carry out the tasks. This integrates intelligence, operations, and fires into one tool. The Unit Viewer integrates information from several sources into one small pop-up window. The tool works for both friendly and enemy platforms and pulls all available information into one screen. The window can be docked on the screen, and whenever an asset is selected, its information is presented. For a friendly asset, it shows the asset status, such as speed, location, altitude, heading, available munitions, fuel level, its current tasks status, the current use of its sensors, and a list of enemy assets that are currently threatening it. From the Unit Viewer, there is an option to display, on the map, the friendly platform’s route and its current task’s sensor coverage. For a nonfriendly asset, the Unit Viewer shows a list of all the friendly assets threatened by that asset and the nonfriendly’s speed, direction, status (e.g., suspected, identified, targetable), and movement tracks derived from previous detections. The Unit Viewer further provides a link to the Intel Viewer to view available images of that asset. The Detection Catalog is a tabular and textual tool that lets the operator see all detections made by the system. It is organized by detection and shows who detected it, and when, and by which sensor. The intelligence estimate is a real-time enemy situation template that helps the operator template the enemy within an area of responsibility. As information is gathered within the area of responsibility, the system updates the estimate with information on what was actually identified, destroyed, immobilized, and so forth, in that area. Automation of BDA Similar to the AGM, the Battle Damage Assessment Guidance Matrix (BDAGM) monitors the friendly fires at the enemy targets and automatically

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Figure 3.9. The Intel Viewer and Picture Viewer offer methods to view images and provide information on the objects viewed.

issues optimized commands (or recommendations) to send the available sensors to perform BDA. This tool integrates information involving maneuver, intelligence, and fires. The BDAGM allows the operator the opportunity to enter his intent in conducting automatic BDA missions (referred to as his BDA plan) into the knowledge-base system. Multiple BDA plans can exist for different approaches to BDA at different times in the battle. The operator can

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modify the currently active BDA plan, create a new one, activate a saved one, and share his BDA plan information with a peer, superior, or subordinate. When setting up a BDAGM, the operator selects from a list of assets in his command that have sensors that can generate imagery or are humans who can visually assess damage. Note that no sensors modeled in our experiments can automatically assess damage. Therefore, we rely on images viewed by a human using the Intel Viewer to determine the level of damage. That is, the sensors send back images and humans determine the level of damage. For each asset under the group’s command, the operator may disable automatic tasking of the asset, allow the asset to be automatically tasked by the system, and/or dedicate the asset to the system for use as a BDA collection asset. It can further require the asset to avoid performing BDA on certain target types or limit its collection to specific geographic areas. When an enemy is fired at, the collective agents in charge of BDA will monitor the attack and recommend or automatically assign (based on the operator’s plan) the best suited asset to perform the reconnaissance (BDA) of the target based on the active BDA plan. In addition to the BDAGM, the collective agents will monitor movement, radar, fires, and communications coming from enemy units marked as damaged to determine if there are any signs of life (such as movement or communications) and then report that information back to the CSE where it is displayed in the BDA Report tab of the Intel Viewer. CCIR The system can alert an operator about a number of situations based on the development of Priority Intelligence Requirements and Friendly Force Information Requirements. Alert selections are operator specific and can be saved for the operator across multiple missions. The alert functions help a commander determine when CCIR criteria are met. The system also includes a planning audit tool that walks through a list of mission planning tasks that are either automatically validated by a collective agent as complete or posed as a yes-or-no question to the operator. The planning audit serves as an operation preparation check list for use prior to execution of a plan. Communications Even with the wealth of information available through the BCSE displays, verbal communications remains important. When the BCSE is used in a simulation environment like the MDC2 program, the ASTi simulated radio and communications system can be used. In the real world the BCSE would integrate military grade radios such as SINCGARS. The BCSE integrates the radio channel and volume controls through the use of a built in interface on the CSE that keeps the operators interface to the radio the same regardless of whether they are using it in a simulated or live environment.

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The operators also use the CSE collaboration mode in order to share the current plan with higher headquarters, peers, or subordinates. The operator may also choose to drop out of collaboration and work independently on his part of the mission plan and then rejoin the collaboration session at a later time. In order to allow the command cells the most flexibility in sending and receiving data, the bandwidth management function lets each cell set transmit and receive rates for the following: heartbeats (blue asset’s state, such as location and health), sensor measurements (as an example, moving target measurements include azimuth, azimuth variance, elevation, elevation variance, range rate, and range rate variance), and spot reports—fused information about battlespace objects, which includes an ID, type, list of all possible objects considered with each one’s estimated probability, location with its probability of error, speed, and sensor information that was used in determining the spot report. The operators can modify the customized bandwidth settings at any time during execution. A unique and important feature of the CSE is command succession. If a command asset is destroyed, like the Command and Control Vehicle (C2V), the networked components sense its loss. An alert is triggered and the remaining assets are notified that the system suspects that the particular command vehicle has been destroyed. A commander in another cell can investigate, and if the loss is confirmed, he may reassign assets to one or more cells, assign a new commander to the cell, or a mixture of the two. The CSE supports both individual chat sessions and group chat sessions. The operator is shown the active members and may select their chat partner. Operators may also set up a named group (one or more) for chat. Individuals are invited to the chat and may elect to join. Logistics The Combat Power tool tells the operator about the health, fuel, and ammunition status of assets during the battle. The operator may tailor the data to his specific interests. For example, the operator may request to show only certain assets and change such settings as the threshold when the low level of fuel is to be reported. During execution, it shows a trend analysis for fuel, munitions, and heath. The Munitions-on-Hand is a tabular, textual tool that lists all the available ammunition and the allocated and spent ammunition counts for each asset. During execution, the counts are continuously updated by the system as rounds are fired, detonated, resupplied, or allocated by a plan. Maps and Terrain Analysis Any battle-command system relies on good terrain analysis and visualization tools. The BCSE uses the Commercial Joint Mapping Toolkit (CJMTK) to provide its mapping and terrain products. The CJMTK is the standard

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geospatial exploitation tool for Department of Defense command, control, and intelligence systems.3 Using CJMTK makes it easy to load georeferenced satellite imagery and maps, and to add layers that describe features such as depression areas, contour lines, hydrology, roads, restricted areas, and buildings. Depending on the warfighter’s needs, overlays can show dynamic terrain updates; diplomatic, economic, and political reference locations; overwatch positions; terrain compartments; enemy sniper or ambush positions; and more. The 2D map helps operators understand the overall situation. The 3D map offers multiple perspectives of the terrain (third person, first person, and bird’s-eye views) along with the COP overlay. The 3D viewer also allows the user to view the terrain while “flying through” the virtual environment as well as attaching the view to an asset to see the environment from the asset’s perspective. The CSE can display location information in two formats. These are the military grid reference system (MGRS), which provides a means of locating any point on the earth with a 2 to 10 character code, and the geodetic coordinate system which uses the angular measurements of latitude and longitude to mark a location. The status bar shows the map scale (for the current zoom), position, and elevation as the mouse moves across the map. Another key technology is the route generation products that are part of the Battlespace Terrain Reasoning and Awareness (BTRA) system,4 developed by the U.S. Army Engineer Research and Development Center (ERDC). BTRA has been integrated into the BCSE to identify and show areas of observation, cover and concealment, mobility, key terrain and avenues of approach, positions of advantage, and advanced mobility analysis. For example, it can be used to answer questions like, Where are all the feasible locations that an enemy tank platoon could have moved to since its last detection 25 minutes ago? The tool for automatic route generation based on commander guidance was found to be a very useful function in the BCSE. In particular, the intelligent agents such as the PSE take advantage of the route generation tools when they decompose high level tasks like “Reconnoiter Area X” into specific routes needed to accomplish the mission. AN ILLUSTRATIVE SCENARIO To demonstrate how all the moving parts work together, and how they implement a network-enabled approach to battle command, let us walk through the following scenario with the help of Captain Johnson, the commander we met in chapter 2. In this scenario, Captain Johnson’s force is responsible for providing reconnaissance and for clearing of the enemy in an important area of the battlefield. Following Johnson’s instructions, the maneuver manager, Specialist Chu, uses GCMs—polygonal areas that he sketches on the map—to mark a specific area

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of interest. Chu names the area DOG. The assets under Johnson’s control include five unmanned (robotic) assets: • An RSTA UGV is tasked to perform surveillance of area DOG by ground surveillance radar (GSR). GSR is commonly used to detect moving targets. • A ducted-fan unmanned air vehicle Class II UAV is code named A-321. It carries a fixed camera that provides daylight and infrared imagery. A-321 also carries an onboard system that performs target detection but not target identification. In other words, it can find a suspicious-looking thing in the battlefield but cannot determine what the thing is. A-321 is tasked to automatically perform reconnaissance of area DOG. • Another Class II UAV is named A-322. It is mounted with the same camera and has the same capabilities as A-321. The commander assigned A-322 the task of obtaining imagery for the purposes of battle damage assessment. When the Blue force fires a precision weapon at a target within area DOG, A-322 will move into position to take a picture in order to evaluate the success of the attack. He does this with the help of BDAGM. • A ground robotic vehicle carries precision attack munitions for Non-Line of Sight (NLOS) fires and stands by for orders to move and fire. • A LOS vehicle carrying a Multi-Role Armament and Ammunition System (MRAAS) weapon is standing by for orders.

The RSTA detects a moving object—let’s call it ObjX—with its GSR. The PSE on the RSTA sends a stream of sensor measurement information to the Collective Intelligence Module where it is fused with other available information. The result is a spot report indicating that ObjX is a target of unknown type moving east at 40 km/h. Since its type is unknown, the system marks it with a low confidence level. The detection is broadcasted to all the components in all three tiers on the network. The visualization components, the CSE, SSE, and VSE, place an icon indicating a detection of an unknown type on the map at the last detected location. A-321’s PSE, its onboard expert system, gets the information, sees that the confidence level of the target information is low, and recognizes that the target is within the boundaries of area DOG. It immediately moves into reconnaissance action while alerting Johnson and his staff that it is beginning a new task against the unknown moving object ObjX. A-321’s PSE uses the knowledge of its own capabilities, the terrain information, and location prediction algorithms to determine a good location to snap a picture of the target. It formulates a task for itself called “Target Reconnaissance” that includes movement tasks and a picture-taking task. The PSE broadcasts this information across the three tiers so that other assets understand its intent. Specialist Chu monitors the route closely on his CSE to ensure that the right decisions are being made. The RSTA vehicle, still tracking the object, receives A-321’s information, realizes that A-321 needs a more rapid feed of information in order to accurately track the moving object and increases its transmission rate of the objects location information. Instead of broadcasting the information throughout the

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three tiers, the message is directed from the RSTA vehicle to A-321 since only A-321 needs such detailed information. This helps reduce network traffic by transmitting high-rate updates between communicating vehicles (using network relay points if appropriate) only when necessary. With the increased rate of incoming information about the location of the moving target, A-321 uses its PSE to analyze the situation, terrain, and other environmental and logistical information to track down the target and then snaps an image. The analysis of the image, however, has to be performed elsewhere. The Collective Intelligence Module receives the image, fuses it with the previous information about ObjX, and generates a new spot report, indicating an image is available. The spot report is broadcast to all three tiers. The GUI layer updates the visualization of ObjX with an image marker, adds the image to the picture viewer, and updates the status in all tables. Within the command cell, Sergeant Rahim, the intelligence manager, is alerted to the incoming image and uses his Intel Viewer on the CSE to display the image. He recognizes that ObjX is clearly an SA-13 air-defense system that does not appear to have experienced any damage. With a few mouse clicks, he designates the target as an enemy SA-13 with no damage. As soon as the identification is entered into the system, an update message is transmitted to other decision support components in the network. Each system that is displaying the unknown ObjX immediately gets an update with the appropriate symbol of an SA-13. This event, in turn, prompts several reasoning processes across the system. Using its onboard intelligence, the A-321 understands a fire mission is planned in its area and that it is in danger of friendly fire. It immediately uses its self-protection rules in the PSE and analyzes the terrain for a good place to seek cover. Fortunately, A-312 is small and went undetected by the enemy, but it still takes every precaution to survive now that its task has been completed. The CIM’s threat manager agent classifies the SA-13 as a high threat based on the criteria set up earlier within the AMF by the effects manager, Sergeant Manzetti. The updated situation awareness is broadcast to all tiers and platforms. Each user can see the friendly assets that the SA-13 is currently threatening. Captain Johnson knows what is about to happen and watches the situation extremely closely. Here is the moment when the automated tasking of vehicles and the human decision making must come together. In an instant he gets an alert that A-321 is in danger of friendly fire and is now seeking cover. Watching his CSE, Johnson confirms that A-321 is well on its way to take cover behind a nearby hill. In this case, no other friendly or neutral assets are within the vicinity of the enemy SA-13. Concurrent with the threat identification, based on preset criteria, the AGM calculates several attack recommendations, prioritizes them, and sends to the command cell. The first choice on the list is a recommendation to fire a precision attack munition from the NLOS vehicle. The second choice is to

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fire the MRAAS from the LOS vehicle. Manzetti positively acknowledges the NLOS recommendation, and a fire request is sent to the NLOS vehicle. The NLOS vehicle accepts the request and carries out the attack—fires the missile. This update is disseminated to everyone in the network Some minutes later, the missile detonates. The fact that the missile has detonated is estimated by the BDAGM agent based on the munition’s distance, trajectory, and speed. The detonation event triggers additional automatic behaviors. UAV A-322 dedicated to BDA is automatically tasked by the BDAGM agent to take a picture of the SA-13. A-322’s PSE uses the same terrain analysis tools that A-321 did. However, in this case, the PSE applies concealment criteria to the route generation because its self-protection rules indicate that the SA-13 may not have been destroyed and could shoot it down. This results in a concealed route that stays out of the line of sight of the SA-13, and other known enemy assets, for as long as possible before popping up and taking a picture. Soon, A-322 arrives at the target and takes the picture. The information is communicated across the tiers to the other components. Sergeant Rahim receives the image, views it, and then updates the damage state of the SA-13 to “Damaged.” The new state of the enemy target is broadcasted across the tiers. Johnson leans back and announces to his team “Great job, guys!” To summarize: working in partnership with the BCSE system, the operator sets criteria for automatic behaviors, responds to visual cues, and updates identifications. The system shares this information across the tiers and initiates appropriate automatic tasking of assets. The system keeps the commander well informed and enables him to focus on the high-level management of the battle rather than the control of the assets. THE DECISION SUPPORT FRAMEWORK Although fairly distinct in their functions, most of the agents comprising the BCSE—CSE, PSE, SSE, and so forth—are built from the same underlying technology framework. The Viecore Decision Support Framework (VDSF)5 has been applied to a range of C2 software applications to support army echelons from corps down to individual warfighter-borne platforms and autonomous robots. The VDSF framework is a generalized, shell-like decision support system that can be delivered as a service or enterprise java bean in a servicebased architecture. To construct this framework, we identified functions and data objects that are common to automated decision support, factored them out, tested and validated them, and combined them into the generalized framework. With a framework as a starting point, the development task is simplified—the development team can focus on those pieces that are unique to the problem being addressed. The VDSF framework employs an expert system rule-based approach that separates the communication and data exchange formats from domain specific rules, or knowledge, and the

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processing engine. Another key feature provided as part of the framework is automated code generation (Figure 3.10). The code generation tool reads the project metadata and generates well-formed source code in accordance with a specific set of design patterns. Typically, about 50 percent of the software code in the resulting decision support application can be automatically generated. The VDSF helps developers change system behavior rapidly as needs and processes evolve. By introducing a new set of rules, the decision support software can be quickly adapted to create new solutions. Rules operate on the data that represent the battlespace and reflect the decision support software’s ability to detect and respond to relevant changes in that data. Systems that have successfully used the VDSF include Future Combat Systems (FCS) C2, MDC2, Future Force Warrior (FFW), Vetronics Technology Integration Program (VTI), and Collaborative Technology Alliances (CTA). The VDSF is built around a production rules engine based on the Rete Algorithm.6 The use of a production rules engine benefits the development and knowledge acquisition process because rules can be acquired, modified, and removed without having to reexamine the entire rules base after a change is made.

Figure 3.10. The use of automated code generation minimizes development time and maximizes code reuse.

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We have been able to eliminate much of the extensive and error-prone analysis and potential problems concerning rule interaction that often reveal themselves in complex applications. The system can be adapted as the development team learns about the domain and the application through experimentation. Within the VDSF there is one framework-specific reasoning engine and two third-party products that we can currently leverage to provide the reasoning engine in the VDSF. The third-party products are HaleyRules7 and Clips/R2.8 Using the VDSF, a new system is built by adding a new set of applicationspecific rules and augmenting some of the system components rather than by creating a new architecture from scratch. By using a common architecture, we are able to exploit commonalities across applications. This approach has proven to be robust and scaleable and has resulted in significant cost savings. The key challenge from a software development perspective is to create the appropriate data models, symbolic framework, and an efficient way of detecting changes in battlespace state when the number of state change events can be very large. To mitigate the challenge, the core VDSF architecture provides means for the following: • Collecting the data needed to represent the environment (in our case, the battlespace). • Reasoning about the battlespace data. • Detecting and responding to relevant changes in that data.

In addition to the data that characterize the battlespace, the framework is used to model assets (Who), activities (What), operational graphics and terrain (Where), absolute and relative time (When), and the purpose or intent behind the activities (Why). To accomplish this, the concepts of named entities, activities, places, objects, and time are represented in the data model (i.e., the lexicon, semantics, and ontology required to symbolically reference and associate the five Ws are predefined). Figure 3.11 depicts the main architectural components of a VDSF-based Decision Support System (DSS) and their relationships. The entities at the top of the diagram are external systems that help place the DSS in context. They will not be discussed. Others include the following:: • The Device Dependent Interface (DDI)/Device Translator (DX) layer communicates with, and translates the information from, devices external to the DSS. By device we mean any software or hardware system that can exchange information with the DSS. For example a hardware device could be GPS and a software device could be another C2 system or a warfighter’s computer screen. Most often the exchange of information is performed via specific message formats. The bottom line is that there are messages that need to be received from external devices to get information into the DSS. The DDI/DX layer provides the connection to the

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Figure 3.11. The architecture of the VDSF underlies most of the agents within the BCSE.

device as well as the translation of the information from the device to the DSS. The DDI/DX provides a separation of the DSS system from a device—a failure of one device will not affect the DSS. It also ensures that the DSS proper does not have to provide a capability to communicate or provide protocol interaction with any device. • A component within DDI/DX, the DDI is a device driver built to conform and support the communication protocol of a specific supported external device or system (e.g., a GPS or Terrain Reasoner). • Another component of DDI/DX is the DX. It converts data received from the DDI into a normalized format for use by the Decision Support System Processor (DSSP). Likewise, the DX converts data received from the DSSP to device-specific formats and passes it through to the device-specific DDI layer. • The Device Independent Interface (DII) presents a single messaging interface to the DSSP, in effect hiding all device-specific data. • The Transaction Processor manages all messages between the DX and the DSS Reasoner (described next). • The DSS Reasoner contains the knowledge base containing the world state and the reasoning engine known as the Rules Engine. • The Rules Engine applies the rules against the knowledge base (called running the rules), which leads to new inferences. Ultimately the Rule Trigger Method, described below, will receive a notification of a change in world state and take the appropriate action as described (later in the Rule Trigger Method description). • Rules Helper Methods call functions registered with the rules engine during rule evaluation. For example, if an enemy asset moves, a rule may call the Terrain Reasoner to help determine if the enemy can see a given position. In this example, the Terrain Reasoner helps the rule determine if a threat exists.

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• A Rule Trigger Method is a mechanism to take an action as a result of a monitored change in the knowledge base’s world state. A trigger method can be registered to be invoked when a fact (data element) is added, modified, or deleted in the world state. When the fact is changed, the trigger method is invoked, allowing additional actions to occur such as alerting outside components or making additional changes to the world state. • The C2 Data Model is represented in the decision support application by a set of software objects instantiated in memory. These objects are typically stored in a map structure and accessed by a unique identifier. The classes of the objects are defined by the data schema which is a model of the various data elements used to represent the world state (e.g., mission, course of action, task organization). The data schema is fed into code generation utility that automatically generates the classes. Instantiation of these classes automatically associates them with the rules engine library via a framework. These instantiated objects are then represented in the working memory.

The VDSF framework can be applied to a new application by adding a new set of application specific rules, augmenting the C2 data model, and adding in additional DDI/DX components as needed. The architectural framework can be reused along with much of the tested and validated code base. All the machinery we described in this chapter assists the command-cell members in numerous ways. They use it to organize and integrate the incoming information from multiple sensors within the unit, to interpret and understand the unfolding situation, to project what may happen in the battle in the future, to plan and decide how to achieve the objectives of the mission, to formulate and issue commands to subordinate assets accurately and efficiently, and to collaborate with other command cells. Among all these important tasks and their purposes, however, one stands out as exceptionally influential—comprehension of what is happening in the battlespace. Scientists call this situation awareness.

CHAPTER 4

Situation Awareness: A Key Cognitive Factor in Effectiveness of Battle Command Mica R. Endsley

Situation awareness (SA) is critical to successful battle-command operations. Warfighters have always paid attention to determining the critical factors of the situation—the location and capabilities of enemy forces, the lay of land, and the effect of weather and terrain on their own forces and operations. This knowledge is critical to effective decision making, both during the planning of operations and during execution of the battle. Situation awareness is defined as “the perception of the elements in the environment, within a volume of time and space, the comprehension of their meaning, and the projection of their status in the near future” (Endsley 1988). Building SA therefore involves perceiving critical factors in the environment (Level-1 SA); comprehending or understanding what those factors mean, particularly when integrated together in relation to the warfighter’s goals (Level-2 SA); and at the highest level, projecting what will happen in the near future (Level-3 SA). The higher levels of SA are critical for timely, effective decision making. The three levels are depicted in Figure 4.1. Level-1 SA—Perception of the elements in the environment. The first step in building SA is to perceive the status, attributes, and dynamics of relevant elements in the environment. This includes important elements such as enemy, civilian, and friendly position and actions; terrain features; obstacles; and weather. Inherent in military operations is the difficulty associated with determining all the needed aspects of the situation due to obscured vision, noise, smoke, confusion, and the dynamics of a rapidly changing situation. In addition, the enemy works hard to deny information regarding its troops and operations, or to intentionally provide misleading information. Table 4.1 lists some of the key elements of Level-1 SA for command and control operations.

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Figure 4.1. Situation awareness levels and the decisionaction process.

Level-2 SA—Comprehension of the current situation. Comprehension of the situation is based on a synthesis of disjointed Level-1 elements. Level-2 SA goes beyond being aware of the elements that are present in a situation—it adds an understanding of the significance of those elements in light of the warfighter’s goals. This level of SA is sometimes called situation understanding. The warfighter assimilates Level-1 data to form a holistic picture of the environment, including a comprehension of the significance of objects and events. For example, an intelligence officer may need to assimilate the data from multiple sensors and reports in order to determine enemy intent or the impact of friendly operations on the degree to which an enemy asset can shoot, move, and communicate. Typically Level-1 SA (perceived data) must be interpreted (with reference to goals or plans) in order to have meaning as Level-2 SA. For example, a commander must understand the impact of discovering a new enemy asset on the conduct of his mission operation so that he can rapidly make the necessary adjustments. Table 4.2 shows examples of typical factors that are relevant to situation comprehension or understanding in command and control. Level-3 SA—Projection of future status. The third and highest level of SA is the ability to project the future actions in the environment, at least in the near term. This is achieved through knowledge of the status and dynamics of the elements and a comprehension of the situation (both Level-1 and Level-2 SA). Commanders who possess a strong Level-3 SA are able to project, for example, where and when the enemy will strike or how much time they have until reinforcements arrive. This gives them the knowledge and time necessary to decide on the most favorable course of action to meet their objectives. Typical Level-3 SA elements are shown in Table 4.3. This highest level of SA has received limited attention, yet is one of the most important. During both planning and operations, the possible actions and outcomes of relevant actors must be considered, in multiple combinations, to form a wide range of possible expectations regarding what could happen. This process of projection and planning for contingencies (also called branches and sequels in military-planning parlance) is fundamental to successful command

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Table 4.1 Examples of Level-1 SA in Command and Control Enemy

Friendly Situation

Recent enemy activity Location Time of activity Enemy composition Organization structure Leadership Unit type Equipment Transmission types Weaponry Experience level Morale/ commitment Vehicle Capabilities/skills/ training Enemy pattern of movements Location of ammo/ supplies Movement of weapons Enemy center of gravity Enemy disposition Location Dispersion Numbers Weapons Ammo/supplies Objective Enemy psychology Past behavior/ actions Religious/political beliefs Perception of friendly forces Enemy history Enemy doctrine Past COAs Past behavior/ actions

Current friendly activity Location Time of activity Friendly composition Unit type Equipment Experience level Morale/ commitment Fatigue/load Vehicle Capabilities/skills/ training Recent action Friendly disposition Operational readiness Comms types Location Dispersion Numbers Weapons Troop psychology Ammo/supplies Troop doctrine Past behavior/ actions Religious/political beliefs Civilian Situation Disposition Location Number Refugee flow Known terrorists Americans Media NGO/IGO Living condition Clans present Ethnicities

Weather Icing Projected weather Inversion Temperature Barometric pressure Precipitation Thunderstorms Hurricanes Tornadoes Monsoons/flash flooding Tides Cloud ceiling Lightning

Wind Direction Magnitude Surface winds: Aloft winds Visibility Illumination Fog Day/night Ambient noise Moon phases Sand storms

Terrain Elevation Type of terrain Flat Urban Hilly Mountainous Rocky/jagged Conditions Mud Land mines Rubble Sand Drainage Slope bank City plan Map Features Vegetation Hydrology Swamps Wetlands Rivers Obstacles Infrastructures Cellular net Telecom net Roads

(continued)

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Table 4.1 Examples of Level-1 SA in Command and Control (continued ) Previous types of occurrence Previous time of occurrence Enemy assets Location Number by location Type Coverage Mission Task Purpose Commander’s COAs

Buildings Culture Usage Languages spoken Materials Level of organization Location Mood of crowd Religious/political beliefs Agitators present Threatening actions Weapons Morales/ commitment Training/skills Intent Politics Potential terrorists

and control. Unfortunately, it is also very difficult to do well, due partly to uncertainty in possible future events, but also because of limited cognitive resources available for considering many possible permutations of future events. As shown in Figure 4.2, at anytime the warfighter may know only a subset of that which is knowable. As he seeks to make projections as to what will happen in the future, that percentage which is known may decrease further.

CHALLENGES FOR SA IN COMMAND AND CONTROL SA is rarely, if ever, perfect. It must be gathered from multiple, sometimes contradictory sources. At times, needed information is unavailable. At other times, multiple changes occur too rapidly, and too many sources compete for the warfighter’s limited attention. All of this leads to what has been termed the fog of war. SA can be derived from a variety of sources (Figure 4.3), including direct observations, communications with others (through radio or direct face-toface contact), and increasingly through computerized command and control (C2) systems and enhanced sensors that are becoming a routine part of military operations. It is important to point out that no C2 system can convey all that is needed for SA. Warfighters will still find it necessary to integrate this information with information gathered directly from the environment as well as from others. It is also important to point out that information from each of these sources will be associated with different levels of reliability. A critical part of Level-1 SA is confidence in information (based on the sensor, organization, or individual providing it), in addition to the information itself.

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Table 4.2 Examples of Level-2 SA in Command and Control Possible engagement areas Possible avenues of approach Areas of cover and concealment Choke points Friendly limitations/advantages due to terrain Friendly limitations/advantages due to weather Friendly limitations/advantages due to infrastructures Enemy cover and concealment Enemy ability to shoot Enemy ability to obscure the friendlies Enemy objectives/intent Relative costs/benefits of potential COAs to enemy Advantages of COAs Disadvantages of COAs Probability of friendly COAs success Probability of enemy COAs success Effect of terrain on enemy time to maneuver Effect of terrain on enemy’s choice of avenue of movement Enemy limitations/advantages due to terrain Enemy limitations/advantages due to weather Possible enemy locations Enemy ability to obscure identity Enemy projected time to maneuver Ability to support the plan with intel, recon, and surveillance Ability to get supplies to assets Time needed for placement Transportation needed to move the asset Level of risk to the assets in the area Ability to move the assets stealthily Security needed to protect asset Available force protection Time needed to collect the information

Advantages/disadvantages of COA Risk of mission failure/success with COA Risk of casualties/loss of assets with COA Weaknesses of COA Ability to counteract potential enemy actions Ability of friendly forces to carry out COA Number/severity of undesirable potential outcomes Ability to mitigate risk in COA Ability to make needed changes Ability of friendly forces to execute plan Flexibility of COA Freedom of maneuver of COA Ability to take advantage of opportunities Ability to respond to unexpected events Level of exposure to enemy Impact of failures on execution of plan Ahead/behind schedule for task accomplishment Deviations from expectations/plan Impact of deviations on mission Distance to supply points Mobility requirements Deviation between items needed and when we can deliver them Deviation between effectiveness level and reconstitution criteria Time and distance between location of supplies and unit Deviation between planned and actual timing of events Distance to supply routes Impact of not meeting request on mission effectiveness Impact of meeting request on future supply plans Deviation between request and availability Impact of early arrival Ability to combine shipments

(continued)

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Table 4.2 Examples of Level-2 SA in Command and Control (continued) Accessibility of the area Criticality of information needed Priority of assigned information requirements Gaps in organic assets Gaps in coverage Items where more information is needed Credibility of source Confidence level in information Difference between current and desired confidence in info

Weapon effectiveness Prioritization of targets Best points for use of smoke Capability of troops to execute actions Best areas for engagement Ability/time requirements for movement to engagement Ability of terrain to support vehicle/ troop movements

As Figure 4.3 shows, there may be a significant gap between ideal SA (perfect knowledge) and that which is currently “known” by the system from all of its available sensors and other inputs. By system knowledge, we mean not only the information residing in an individual technical system (such as a radar or command and control software), but that in the sum total of the technical systems, people, processes, and operations that together form the basis for command and control. There may also be a gap between this level of system information that the warfighter might possibly obtain and that which can be derived from the system interfaces (available information). This gap may exist because some system information may not be passed to the warfighter through the system interfaces—due to limited network bandwidth, for example, or a failure of a subordinate to pass on a needed report—or because the warfighter must take additional actions to derive the information from the system (paging through menus and windows to find information that may be obscured). An important goal for the development of command and control systems is not only to raise the level of system information, but also to minimize the gap been system information and available interface information through effective system design. Finally, a gap can occur between the amount of information available at the system interface and the SA that is finally formed in the mind of the individual. There are a number of cognitive limitations that often act to limit SA, as well as a number of external factors that can act to make situation awareness difficult to attain. Individual Limitations People have a limited amount of attention they can direct toward gathering needed information and a limited amount of working memory that can be used to combine and process perceived information to form the higher levels of SA. Unless they are experienced and dealing with learned classes of situations (which helps them develop mental models and schema that allow

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Table 4.3 Examples of Level-3 SA in Command and Control Projected enemy COAs Expected COA Most dangerous COA Projected effect of weather on enemy COA Projected effect of weather on enemy equipment Projected enemy unit size/actions Projected enemy decision points Projected effect of COAs on enemy vulnerabilities Projected impact of friendly COAs on enemy COAs Predicted reaction of population to friendly COAs Projected civilian behavior Projected effect of fires on enemy/civilians Projected effect of weather on friendly COAs Projected effect of weather on equipment Projected effect of weather on terrain Projected effect of weather on personnel Projected effect of weather on infrastructures Projected impact of weather on visibility Projected impact of weather on trafficability Projected impact of weather on visibility Projected impact of weather on ability to get air support Projected timing of weather inversions Projected impact of terrain on trafficability Projected impact of terrain on visibility Projected impact of terrain/weather on systems operations Projected impact of terrain/weather on comm capabilities Projected impact of terrain/weather on ability to get intel Projected impact of terrain/weather on ability to get air support Projected safety of deployment for assets Projected effect of infrastructures on friendly COAs

Projected availability of friendly forces Projected ease of implementation of COA Projected availability of resources Projected ability to minimize troop risk Projected impact on enemy Projected availability of resources Projected effect of COA on enemy plans/mission Projected effect of COA on enemy workload Projected effect of COA on enemy capabilities/ability to fight Projected time required to carry out COA Projected ability of plan to disrupt/ counter enemy intentions Projected risk associated with friendly COA Projected time on route Projected safety on route Projected safety of shipments Projected reliability of transportation mode Projected time required to get item to site Projected ability to get to location on time Projected ability to sustain the assets Projected ability of enemy to counterattack asset Projected ability of assets to collect needed information Projected availability of assigned assets Projected ability to support units with COA Projected usage of each item over time Projected location of unit over time Projected usage of each item over time Projected safety of units and logistics team Projected availability of resources Projected time and ability to get items to units Projected ability to achieve new supply plan

(continued )

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Table 4.3 Examples of Level-3 SA in Command and Control (continued) Projected impact of intel information on COAs Predicted effect of enemy assets on friendly COAs Predicted enemy asset deployment Projected time to carry out COAs

Projected need for more intel information Projected impact of missing information on operations

for rapid pattern matching to recognized classes of situations), the level of SA achieved in demanding real-time environments, such as C2, is necessarily limited (Endsley 1995c). In addition, many task and environmental factors can seriously challenge the ability of the warfighter to maintain a high level of SA. This includes features of the environment (e.g., noise, heat, rugged terrain) and the warfighter’s condition (e.g., fatigue and physical or mental stress). These factors can be greatly influenced by the enemy, which can alter the tempo of the battle and affect the conditions under which a battle is fought.

Figure 4.2. The extent of what is known to the warfighter decreases the further he projects into the future.

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Figure 4.3. Sources of SA—after Endsley (2006).

Perceptual Constraints In today’s practice, much of command and control occurs in a relatively stationary command post or tactical operations center (TOC). In the future, however, the military is planning on a much more mobile command and control, an on-the-move concept that distributes C2 activities and places them in conditions that are intertwined with activities in the battlespace. Under many battlespace conditions, the warfighter must traverse widely disparate terrain and deal with highly varied environmental conditions. Obstacles, noise, poor weather, visibility, and smoke may reduce the warfighter’s ability to perceive the information he needs. Due to enemy actions, even directly viewing a critical area may be impossible. Gathering the needed information across a widely dispersed operation is a challenging activity that takes considerable effort, particularly when the enemy may actively work to conceal critical information or provide misinformation. These factors work to directly limit Level-1 SA, and thus the higher levels of SA (comprehension and projection), due to incomplete or inaccurate perceptions of environmental cues. Stressors Several types of stress factors omnipresent in C2 operations may negatively affect SA. These include (a) physical stressors—noise, vibration, heat and cold, lighting, atmospheric conditions, boredom, fatigue—and (b) social

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and psychological stressors—fear or anxiety, uncertainty, importance or consequences of events, self-esteem, career advancement, mental load, and time pressure (Hockey 1986; Sharit and Salvendy 1982). Natural anxiety occurs due to the dangers inherent in military operations. In addition, the physical and mental condition of the individual can also affect SA. Fatigue (due to lack of sleep or rest, or simply prolonged mental or physical exertion—all of which often arise in combat) may negatively affect the warfighter’s individual capabilities to derive SA from the environment. The tempo and time pressures of combat operations can make maintaining SA in the face of rapid change very difficult. A certain amount of stress may actually improve performance by increasing attention to important aspects of the situation (e.g., sniper fire or booby traps). A greater amount of stress can have negative consequences, however, as accompanying increases in autonomic functioning and aspects of the stressors can demand a portion of the warfighter’s limited attentional capacity (Hockey 1986). Stressors can affect SA in various ways, including attentional narrowing, reduction of information intake, and reductions in working memory capacity. This is a critical problem for SA, leading to the neglect of certain aspects of the situation in favor of others. In many cases, especially emergency situations, it is those factors outside the person’s perceived central task that can be lethal. Under stress, the warfighter also may have fewer processing resources for combining information into a meaningful picture and making decisions. It may also be harder to retain detailed information that is essential. In tasks where achieving SA involves a high-working memory load (such as a commander managing the information flow in a fast-paced operation), a significant impact on SA Levels 2 and 3 (given the same Level-1 SA) is also expected. If, however, long-term memory stores are available to support SA, as in more practiced situations, there may be a less negative impact of stress on SA. Overload and Underload If the volume of information and number of tasks are too great, SA may suffer because only a subset of information can be considered. The warfighter may work actively to achieve SA, yet suffer from erroneous or incomplete perception and integration of information. Conversely, poor SA can also occur under low workload. In this case, the warfighter may have little idea of what is going on and not be actively working to find out due to inattentiveness or lack of vigilance. This may occur during periods of waiting, night operations, and extended duty situations. Developing and maintaining SA during C2 operations is difficult. Unfamiliar conditions (terrain, people, cultures, etc.); stress; fatigue; periods of both information underload and information overload; and the challenge of a deceptive, hidden enemy create a situation where a considerable number of the activities and cognitive resources of the warfighter must be devoted to SA. While

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modern technology cannot eliminate these fundamental constraints on SA, it can act to significantly alleviate many of them, making the development of good SA easier than it has ever been in the past. SYSTEM DESIGN FOR SA IN COMMAND AND CONTROL The capabilities of the systems provided for acquiring needed information, and the way in which the information is presented, have a significant impact on the quality of warfighter SA. While a lack of information can certainly be a problem for SA, too much information or poorly organized and presented information poses a problem as well. With improvements in the network and computerized support systems, warfighters face a dramatic increase in the sheer quantity of available data. Sorting through this data to derive the desired information and achieve a good picture of the overall situation is no small challenge (Figure 4.4). New C2 systems and technologies may inadvertently widen the information gap, even while trying to reduce it. For example, the complexity of computerized command and control systems can degrade SA because such complexity can significantly increase mental workload. System complexity may be somewhat moderated by the degree to which the warfighter has a well-developed mental model of the system to aid in directing attention, integrating data, and developing the higher levels of SA. This mechanism may be effective in coping with complexity, but developing such mastery may also require a considerable amount of training. Other technologies may inadvertently degrade SA by redirecting the warfighter’s attention inappropriately or overloading his cognitive processing. For

Figure 4.4. The information gap—after Endsley, Bolte, and Jones (2003).

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instance, the use of night vision devices has been associated with decrements in other senses (e.g., hearing) that could reduce SA (Dyer et al. 1999). More serious effects may be produced by other devices (e.g., helmet-mounted displays) that interfere with the warfighter’s vision, hearing, or attention (National Research Council 1997). High levels of automation and decision aids are also proposed and developed for C2 systems. These efforts should be conducted with great caution. Warfighter SA can be negatively affected by the automation of tasks, which puts them “out-of-the-loop” (Endsley and Kiris 1995). All of these issues lead to the need for a process that systematically identifies warfighter SA needs and develops C2 systems that specifically promote high levels of SA. Over the past two decades, a significant amount of research has been focused on this topic, developing an initial understanding of the basic mechanisms that are important for SA and of the design of systems that support those mechanisms. Based on this research, the SA-Oriented Design process has been established (Endsley, Bolte, and Jones 2003) to guide the development of systems that support SA (Figure 4.5). This structured approach incorporates SA considerations into the design process, including

Figure 4.5. SA-oriented design process, after Endsley, Bolte, and Jones (2003).

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a determination of SA requirements, design principles for SA enhancement, and measurement of SA in design evaluations. SA REQUIREMENTS ANALYSIS To determine the aspects of the situation that are important for a particular warfighter’s SA, one can use a form of cognitive task analysis called a GoalDirected Task Analysis (GDTA), illustrated in Figure 4.6. In a GDTA, the analysis identifies major goals of each warfighter position, along with the major subgoals necessary for meeting each of these goals. The analyst then determines the major decisions that need to be made in order to meet each subgoal. Then, the analyst delineates the SA needed for making these decisions and carrying out each subgoal. These SA requirements focus not only on what data the warfighter needs, but also on how that information is integrated or combined to address each decision, providing a detailed analysis of the warfighter’s SA requirements at all three levels of SA. Such an analysis is usually carried out using a combination of cognitive engineering procedures. Expert elicitations, observation of warfighter performance of tasks, verbal protocols, analysis of written materials and documentation, and formal questionnaires have formed the basis for the analyses. The analysis is conducted with a number of warfighters, who are interviewed, observed, and recorded individually. The results are pooled and then validated overall by a larger number of warfighters. An example of the output of this process (Figure 4.7) shows the goal structure for a brigade logistics coordinator and the decisions and resulting SA requirements analysis for the subgoal “project future supply needs of units.”

Figure 4.6. The form of a Goal-Directed Task Analysis for determining SA requirements.

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This analysis systematically defines the SA requirements (at all three levels of SA) for effectively making the decisions required by the warfighter’s goals. The analysis does not indicate a prioritization among the goals (which can vary over time) or that each subgoal within a goal will always be active. Rather, in practice, a warfighter juggles between subsets of goals, based on current priorities. The analysis also strives to make as few assumptions about the technology as possible. How the information is acquired is not addressed, as this can vary considerably from person to person, from system to system, and from time to time. Depending on a specific case, the information could be acquired through system displays or verbal communications with other warfighters, or it could be generated by the warfighter himself. Many of the higher-level SA requirements are generated in the minds of warfighters today, but that

Figure 4.7a. Example of Goal-Directed Task Analysis for a Brigade Logistics Coordinator position.

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may change in future as intelligent agents and other forms of automation are introduced. By focusing on ideal SA, the GDTA forms the basis for system design; it provides a delineation of the information that the system should try to provide while imposing the least workload on the warfighter. SA-ORIENTED DESIGN PRINCIPLES The development of a system design for successfully providing the multitude of SA requirements that exist in complex systems is a significant challenge. To meet the challenge, a set of design principles have been developed based on an understanding of the mechanisms and processes involved in acquiring and maintaining SA (Endsley 1995c; Endsley, Bolte, and Jones 2003). The 50 design principles include (1) general guidelines for supporting SA, (2) guidelines for coping with automation and complexity, (3) guidelines for the design of alarm systems, (4) guidelines for the presentation of information

Figure 4.7b. Analysis for the subgoal “project future supply needs of units.”

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uncertainty, and (5) guidelines for supporting SA in team operations. Some of the general principles include the following: 1. Direct presentation of higher-level SA needs (comprehension and projection) is recommended, rather than supplying only low-level data that warfighters must integrate and interpret. 2. Goal-oriented information displays should be provided and organized so that the information needed for a particular goal is colocated and directly supports the major decisions associated with the goal. 3. Support for global SA is critical, providing an overview of the situation across the warfighter’s goals at all times (with detailed information for goals of current interest) and enabling efficient and timely goal switching and projection. 4. Critical cues related to key features of schemata need to be determined and made salient in the interface design. In particular, those cues that indicate the presence of prototypical situations are of prime importance and facilitate goal switching in critical conditions. 5. Extraneous information not related to SA needs should be removed (while carefully ensuring that such information is not needed for broader SA needs). 6. Support for parallel processing, such as multimodal displays, should be provided in data-rich environments.

SA-Oriented Design is applicable to a wide variety of system designs. It has been successfully applied as a design philosophy for systems involving remote maintenance operations, medical systems, flexible manufacturing cells, and command and control for distributed teams. SA DESIGN EVALUATION Many concepts and technologies are claimed to enhance SA in command and control and military operations in general. Prototyping and simulation of new technologies, new displays, and new automation concepts is extremely important for evaluating the actual effects of proposed concepts within the context of the task domain and using domain knowledgeable subjects. If SA is to be a design objective, then it is critical that it be specifically evaluated during the design process. Without this step, it will be impossible to tell if a proposed concept actually helps SA, does not affect it, or inadvertently compromises it in some way. A primary benefit of examining system design from the perspective of warfighter SA is that the impact of design decisions on SA can be objectively assessed as a measure of quality of the integrated system design when used within the actual challenges of the operational environment. SA measurement has been approached in a number of ways (Endsley and Garland 2000). A review of the advantages and disadvantages of these methods can be found in (Endsley 1996; Endsley, Bolte, and Jones 2003). In general, direct measurement of SA can be very advantageous in providing more sensitivity and diagnostic value in the test and evaluation process. This

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provides a significant addition to performance measurement and workload measurement in determining the utility of new design concepts. While workload measures provide insight into how hard a warfighter must work to perform tasks with a new system design, SA measurement provides insight into the level of understanding gained from that work. Direct measurement of SA has been approached either through subjective ratings or by objective techniques. While subjective ratings are simple and easy to administer, research has shown that they correlate poorly with objective SA measures, indicating that they more closely capture an individual’s confidence in their SA rather than the actual level or accuracy of that SA (Endsley, Selcon, Hardiman, and Croft 1998). One of the most widely used objective measures of SA is the Situation Awareness Global Assessment Technique (SAGAT) (Endsley 1988, 1995b, 2000). SAGAT has been successfully used to directly and objectively measure warfighter SA in evaluating avionics concepts, display designs, and interface technologies (Endsley 1995b). Using SAGAT, a simulated test scenario employing the design of interest is frozen at randomly selected times, the system displays are blanked, and the simulation is suspended while warfighters quickly answer questions about their current perceptions of the situation. The questions are designed based on their SA requirements as determined by an SA requirements analysis for that domain. Warfighter perceptions are then compared to the real situation based on simulation computer databases, to provide an objective measure of SA. Multiple so-called snapshots of warfighters’ SA can be acquired in this way, giving an index of the quality of SA provided by a particular system design. The data collection approach provides an objective and unbiased assessment of SA that overcomes the problems incurred when collecting such data after the fact. It also minimizes biasing of warfighter SA due to secondary task loading or artificially cueing the warfighter’s attention, which real-time probes may do. By including queries across the full spectrum of a warfighter’s SA requirements, this approach minimizes possible biasing of attention, as subjects cannot prepare for the queries in advance since they could be queried over almost every aspect of the situation to which they would normally attend. The primary disadvantage of this technique involves the temporary halt in the simulation. As a global measure, SAGAT includes queries about all warfighter SA requirements, including Level-1 (perception of data), Level-2 (comprehension of meaning), and Level-3 (projection of the near future) components. SAGAT has also been shown to have predictive validity. For example, SAGAT scores were found indicative of pilot performance in a combat simulation (Endsley 1990). It is also sensitive to changes in task load and factors that affect warfighter attention (Endsley 2000), demonstrating construct validity. It produces high reliability levels (Collier and Folleso 1995; Endsley and Bolstad 1994; Gugerty 1997). Studies examining the intrusiveness of the freezes to collect SAGAT data have generally found no effect on warfighter performance (Endsley 1995a, 2000).

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SHARED SA IN TEAM OPERATIONS C2 is based on multiple teams that function at various echelons and levels of responsibility. Teams of military decision makers must coordinate and communicate within their immediate groups (e.g., a brigade combat team), as well as with individuals or teams across echelons that may be above (e.g., at division level), below (e.g., at battalion level), or lateral to them (e.g., other brigades). This introduces a great deal of complexity, specifically when attempting to design C2 systems for enhancing team performance and decision making. Such C2 systems must provide the SA that is needed for each team member (based on his specific SA requirements), as well as support the need for a common shared SA across the team. This is essential if warfighters are to effectively participate in making decisions with and on behalf of the team.

Figure 4.8. Example of SAGAT results: (a) experienced and inexperienced platoon leaders (Strater, Jones, and Endsley 2003); (b) brigade staff (Bolstad and Endsley 2003).

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Warfighters are not a homogenous group. Military operations are conducted by warfighters with various areas of specializations, such as operations, intelligence, logistics, engineering, fires and effects. Military operations are generally conducted by a commander supported by the smooth functioning of a highly specialized, yet integrated team. Therefore, supporting C2 involves supporting not just the SA of the commander, but also the highly specialized SA needs of each staff member and each subordinate to the commander. To begin to address these issues, it is first necessary to identify what the individuals in the team must do (i.e., what their goals are), how they must interact with one another to meet the common team goals, and what information is needed to achieve these goals using the GDTA process. Overall team SA can be conceived of as “the degree to which every team member possesses the SA required for his or her responsibilities” (Endsley 1995c). Each member of the command staff must have SA for all of his own SA requirements, or become the proverbial chain’s weakest link. In smoothly functioning teams, each team member also shares a common understanding of the situation with respect to those SA requirements that he has in common with other teammates. This is known as shared SA—“the degree to which team members possess the same SA on shared SA requirements” (Endsley and Jones 1997, 2001), as represented by the overlapping areas in Figure 4.9. For example, the intelligence manager and the effects manager both need information on enemy locations and areas of cover and concealment. They may both be aware of these data elements, though they do not make use of the information in the same way. Conversely, if one has knowledge of certain information but does not share it, or if they each have a different understanding of the same information, shared SA will be low. Complete knowledge of the other person’s SA requirements is not necessary. A team member does not need to know everything other team members know. Actually, sharing every detail of each person’s job with each team member creates a great deal of noise for people to sort through to get needed information (Bolstad and Endsley 1999) and can degrade performance. Only those portions of the overall situation that need to be shared between team members should be passed on and should to be highlighted in order to develop systems that support collaborative SA in team operations (Bolstad and Endsley 2000). A major part of teamwork involves the area where the SA requirements overlap—the shared SA requirements that reflect the essential interdependency of the team members. While two team members may be assigned different tasks in executing a mission plan, they must also operate on a common set of data. The assessments and actions of one can have a large impact on the assessments and actions of the other. In a poorly functioning team, two members may have different assessments of the shared SA requirements and thus behave in a noncoordinated fashion. For example, if a warfighter has one picture of where a target is relative to the ambush site, but this is not properly communicated to the others, suppressive fires may not be initiated at the right time or in the right direction. Bolstad and Endsley (2002) examined shared SA in brigade staff. They found that the way individual team members use and interpret the same information

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Figure 4.9. Commonalities among team member goals lead to shared SA requirements (after Endsley and Jones [1997]).

to form the higher levels of SA can vary significantly, based on the goals that are pertinent to a member’s position. For example, they found all positions require knowledge of terrain information (see Table 4.4 for terrain SA requirements); however, the required level of detail and the way in which the information is used varies considerably between staff positions. The majority of differences in SA requirements appear in how the various positions need to comprehend and make projections (Levels 2 and 3 SA) based on the same Level-1 data. For example, the intelligence and operations officers are primarily concerned with how the terrain affects friendly as well as enemy troop movements, assets, and capabilities. The logistics officer and engineer are more concerned with how terrain affects vehicle movements and the placement of obstacles and assets. By understanding not only what data each staff position needs, but also how that information will be used by each position, system displays can be designed that provide only the detail level needed for a particular position without presenting unnecessary information. The same research also shows how the shared SA requirements within the brigade combat team can be identified via the GDTA. Table 4.5 shows some of the shared information requirements for the intelligence and logistics officers. The analysis of shared SA items indicates that the two positions do not share many specific details. Instead, they share general information regarding troops, infrastructures, and courses of action. While they each have many different uses for this information, they also make a number of different future projections (Level-3 SA). Interestingly, these types of projections are rarely conveyed in display design but instead must be communicated verbally by team members for successful coordination in most systems. Unfortunately, teams are often poor at sharing high-level SA requirements. Instead, they communicate only low-level data (Level-1 SA) with the (often false) expectation that it will be interpreted the same way by other team members (Endsley and Robertson 2000). Knowledge of these shared SA requirements can be used to develop systems to increase shared SA between team members, which will be increasingly important as future operations are likely to be more distributed. One

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Table 4.4 SA Requirements Associated with Terrain Information Differ Depending on the Staff Positions (Bolstad, Riley, Jones, and Endsley 2002) SA Level 1 S2 (Intelligence)

S3 (Operations)

Areas of cover/ concealment Enemy boundaries Engagement areas Location of restrictive terrain Map of the area Restrictive Points Significant terrain characteristics Type Conditions City plan Map of area Subsurface Features Vegetation Hydrology Location Swamps Lakes Wetlands Rivers Bank slopes Water tables Obstacles

Areas of cover/ concealment Key terrain Type Conditions City plan Map of area Subsurface Features Vegetation Hydrology Location Swamps Lakes Wetlands Rivers Bank slopes Water tables Obstacles

S4 (Logistics) Areas of cover/ concealment Potential choke points due to terrain Type Conditions City plan Map of area Subsurface Features Vegetation Hydrology Location Swamps Lakes Wetlands Rivers Bank slopes Streambeds Drainage Water tables Obstacles Contour/elevation Firmness of ground Grade

Engineer Type Conditions City plan Map of area Subsurface Features Vegetation Hydrology Location Swamps Lakes Wetlands Rivers Locations Conditions Bank Slopes Condition Water tables Obstacles Type Location Quantity Rocks Houses Terrain Roads Vehicles Villages Buildings Trees People Mines Location enemy Location friendly

(continued)

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Table 4.4 SA Requirements Associated with Terrain Information Differ Depending on the Staff Positions (Bolstad, Riley, Jones, and Endsley 2002) (continued) SA Level 2 S2 (Intelligence)

S3 (Operations)

S4 (Logistics)

Enemy limitations/ advantages due to terrain Friendly limitations/advantages due to terrain Effect of terrain on enemy and friendly assets Effect of terrain on anticipated troop movement time Effect of terrain on system detection capability

Accessibility of routes Effect of terrain on movement times/time to position troops Effect of terrain on rate of enemy closure Effect of terrain on visual capabilities Effect of terrain on communication capabilities Effect of terrain on route difficulty

Suitability of land for unit Effect of terrain on ability to access location with each vehicle type Effect of terrain on type of vehicles to be supported

Engineer Potential approaches and exiting areas Potential staging areas Potential terrain suppression areas Traffic ability Visibility of the locations Critical obstacle information Past enemy usage of obstacles Effect of terrain on location of enemy counterattacks

SA Level 3 S2 (Intelligence)

S3 (Operations)

Predicted effects Predicted effects of terrain on of terrain on enemy COAs enemy COAs Projected effects of terrain on friendly COAs Projected terrain Projected effect of terrain on troop movements

S4 (Logistics) Projected effect of terrain on usage rates per item per unit Projected effect of terrain on security of resources

Engineer Estimated obstacle effectiveness Predicted most secure location for assets, soldiers, vehicles Predicted most survivable routes

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Table 4.5 Shared SA Requirements for Intelligence and Logistics Officers, after Bolstad, Riley, Jones, and Endsley (2002) Shared SA Requirements Level 1 Enemy • Number • Type • Proximity Friendly Units • Current mission status • Equipment • Experience level • Size • Type • Status • Power • Weaknesses Infrastructures • Roads • Types • Condition

Level 2 (none) Level 3 Course of Action • Predicted enemy COAs • Projected friendly COAs Enemy • Projected enemy actions • Projected enemy location • Projected enemy number • Projected enemy type Mission • Projected mission tasks

way to provide high levels of shared SA in teams is to use the identification of overlapping SA needs to create tailored shared displays or a common relevant operating picture. This method has proved effective in certain team tasks (Bolstad, Riley, Jones, and Endsley 2002). In general, it is critical that the shared displays provided in C2 systems allow for information to be tailored to each warfighter’s needs (preventing overload), but also support team SA by providing a window into the relevant SA of other team members. SA IN DISTRIBUTED AND AD HOC TEAMS With the advent of network-enabled warfare, warfighters are becoming more mobile and more distributed in time, space, and tasks. In order to support such warfighters, new tools are needed to enable collaboration among team members across the broad range and tempo of missions. The future force will be “strategically and operationally responsive, rapidly deployable, mentally and physically agile, and able to transition rapidly across the spectrum of operations—a versatile force capable of dominating any situation or adversary with minimal organizational adjustment and time” (U.S. Army 2001). Agile organizations present many challenges. With most military teams, warfighters have been trained as a unit to work together. The time spent

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together not only builds team skills, but also supports the social processes that impact team performance, such as the development of trust and understanding between the members. Network-enabled warfare calls for the ability to “leverage the intellect, experience, and tactical intuition of leaders at multiple levels in order to identify enemy centers of gravity and conceptualize solutions, thus creating a collective genius through accelerated collaborative planning” (U.S. Army 2001). The expressed intent is to bring together rapidly forming teams with the skills, background, and experience to offer multiple perspectives on a problem for the purpose of collaborative planning. These ad hoc teams would likely be selected based on the specific needs of the situation under consideration, would pull members from multiple military specialties and echelons, and often would incorporate joint forces or multination team members. Such teams would not have the benefit of combined training and background, nor would the time that is necessary to establish relationships built on mutual trust and understanding likely be afforded to these teams. Thus, the presence of ad hoc teams adds an additional level of complexity to the development of C2 systems. Experience indicates that ad hoc teams, frequently occurring phenomena, face a number of significant challenges in developing a shared understanding of the situation upon which to base their actions. • SA of ad hoc teams—First, there is an overall challenge in merely keeping up with what current ad hoc teams are in place, what they are doing, and who is a part of them. Commanders traditionally need to maintain an awareness of the current task organization, what their units are doing, and their progress on key objectives. An ad hoc team is a type of temporary unit, for which the commander needs to maintain a similar understanding and awareness of status. Yet, this is far more challenging with ad hoc teams, which may not adhere to traditional command hierarchies and whose status may be far less well defined. • Lack of team mental models—Ad hoc teams face challenges in developing a shared awareness among team members. They must rapidly develop a mutual understanding of their shared task and mission, and the state of their operational environment, while simultaneously trying to build knowledge of their new teammates’ capabilities. Unlike more permanent teams, they may have little or no mental model of their teammates, which is needed for interpreting their teammates’ inputs and contributions and for formulating joint team operations. • Temporal flows in SA—The temporal timelines on which ad hoc teams operate further challenge their functioning. They often do not form as a whole, or operate and then disband as a whole. Rather, their members come and go over time, often multitasking with other duties on other teams (permanent or ad hoc) of which they are also members. Thus playing catch up to find out what they’ve missed while out of the loop and dealing with interruptions is more the norm than the exception. A team member’s ability to develop a shared understanding of the situation in such a manner, often under time duress, may often be quite limited.

Presently, there is little information available on how to best support shared SA for ad hoc teams. This remains a challenge for systems designers.

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Admittedly, mechanisms of SA are better understood for some situations than others. In every case, however, in order to build a system that supports situation awareness, the current practice must rely on experimental analysis of alternative designs. Usually such experiments are performed using prototype or surrogate technologies, often in a simulation environment. Use of situation awareness in designing a command system—both its tools and techniques—requires a rigorous experimentation process, with effective collection and analysis of quantitative data.

CHAPTER 5

The Hunt for Clues: How to Collect and Analyze Situation Awareness Data Douglas J. Peters, Stephen Riese, Gary Sauer, and Thomas Wilk

Building on the theoretical concepts of situation awareness introduced in the previous chapter, let us now describe our experimental approach to measuring and analyzing how warfighters develop situation awareness, the role of situation awareness in effective decision making, and its ultimate impact on the battle outcome. In these experiments, the commanders and their staffs used a set of command and control tools, described in chapter 3, to assist the acquisition of situation awareness. Our experimental findings identify situation awareness as the linchpin of the command process—a key factor that determines the efficacy of all other command elements—from sensor and asset control to decision quality and battle outcome. We begin by discussing our approaches to collecting the experimental data, synthesizing the collected data, and extracting analytic insights. We also describe the tools and processes developed to facilitate our analysis of the commander situation awareness.

DATA COLLECTION Effective experimental design and setup are critical to the successful evaluation of the experimental metrics. However, the quality and depth of the resulting experimental findings are ultimately linked to the quality and depth of the collected data. We were fortunate to have extensive data collection capabilities in our experimental program. Figure 5.1 shows an overview of the data collection approach. In the top left portion of this figure, we depict the sources of data, particularly automated loggers. These loggers collect virtually every piece of information flowing

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through the network for each of the software tools. The data contained in these log files is comprehensive, as most of the files doubled as debugging tools for the software developers. Using these data, we were able to explore new avenues of analysis as we developed emerging insights and extended our analysis into areas that could not have been predicted prior to the experiment. The downside of using this information is the sheer magnitude of the data collected, combined with the lack of standardization among the data files for the different tools. Therefore, to use data from the various tools in the analysis, we developed parsers to convert each file into relational database tables. Ultimately, these data sources enable us to compare ground truth, sensor detections (including those by human eyes), fused information, and perceived truth. These automated logs are pivotal to the analysis tools described later in this and subsequent chapters. The top right of Figure 5.1 shows the data that we collected from the command-cell operators. This includes video and audio recordings of each operator during the run as well as recordings of the after-action reviews and planning sessions. In addition to these quantitative data regarding the operator interactions, we collected information on how the operators perceived the tools and the battle progress. Our approach to this evolved over time. In early experiments, we administered surveys to collect feedback from the operators. Unfortunately, the quality of responses varied dramatically between individuals, and there seemed to be a decrease in the quality of responses as each experiment wore on. In addition, the surveys were necessarily generic and could not be tailored to specific events for a given run. In later experiments, we replaced the host of surveys with a single demographic survey conducted at the start of the experiment. The majority of operator-related information collected during these later experiments was collected in focus groups. At the end of each trial run, we conducted small-group interviews to elicit the operators’ perceptions of key events in the battle. Depending on the events of interest, we arranged the focus groups by cell (i.e., company staff, battalion staff) or by staff position (i.e., intelligence managers, effects managers, etc.). By having the participants elaborate on critical situations in the recent trial run, we obtained immediate recollections that could be correlated with actual battle events. Based on the input from the group interview, we identified an individual decision maker to interview in more detail. In this oneon-one interview, we discussed a single key event in depth: what the operator knew at the time, what decisions were made, what information the decisions were based on, how a less-experienced person may have reacted under a similar situation, and what additional information may have affected the decision. All interviews were recorded, and analysts published notes from the interviews that we used during the subsequent analysis phase. The bottom portion of Figure 5.1 shows a particularly important element of the data collection process—the analytic observers. In this complex freeplay experiment, the half-life of understanding the context of key events is very short. We mitigated this by providing analytic observers with tailorable

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Figure 5.1. The MDC2 approach to data collection.

workstations, each comprising tools that enable the human observer to understand and record as much of the battle context as possible in real time. During the experiments, up to 20 analytic observers were stationed at these tailored workstations. About half of the observers focused on a command cell’s understanding of the battle situation and the collaboration within the cell. Their panels displayed a view of the active tools used by the commander and battle managers of a given cell. The other half of the analysts focused on collaboration

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and coordination between command cells. Their panels replicated the active tools of all operators who specialized in a given function. For example, one set of analyst displays considered collaboration between commanders, and another focused on effects managers. In addition to their tailored displays, each panel also contained a video view of the operators and a display of the actual ground truth status of all Red and Blue forces. Together, the displays allowed the observers to maintain awareness of ground truth, perceived truth, the commander’s situation awareness, how the cells collaborated, and how the commanders and staffs made decisions. Additionally, we created a database application that enabled each analyst to enter observations in real time. We designed the application to facilitate rapid data entry and thereby help focus the data collection. An example collection form is shown in Figure 5.2. Based on the early experiments, we realized that it was not reasonable to expect a single observer to effectively collect on all aspects of the battle. Therefore, we made a conscious effort to identify data elements that could be collected postexperiment from the automated data loggers and to not duplicate the collection of that information via human observers. Further, we staffed each functional area (e.g., intelligence, effects) and each unit (e.g., CAT, CAU) with two observers. The first observer was responsible for selected counts— recording each time certain events occurred (e.g., how often the intelligence manager collaborated with the effects manager). The second observer was

Figure 5.2. An example of a collection tool used by an observer.

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responsible for context—assessing the quality of situation awareness reports, the topics of collaboration sessions, and timing of key events. Immediately following each trial run, the observers met to identify several key events that took place during execution. This served to focus the group interviews (described above) and to enable postexperiment process tracing (described later in this chapter). SITUATION AWARENESS—TECHNICAL The rich data sets collected during the experiments gave us significant flexibility to explore emerging concepts, develop associated metrics, and relate analytic results to combat outcomes. Our quantitative data included both the information available to the commanders (perceived truth) and ground truth states of all battlespace entities. Using this information, we devised the Situation Awareness—Technical (SAt) scoring method to evaluate the quality and scope of information collected by the units over time. Our primary measure of SAt reflects the quantity and accuracy of relevant information available to a command-cell member over time. In its basic form, the SAt score is a ratio of the information available to the information required. This ratio is different for each commander at each echelon because information needs vary with the size and contents of the areas of responsibility, the lethality and range of weapon systems, and the mission at hand. While the complexities of battle command are many, we simplify the scope to include three fundamental components for each enemy entity: knowing where the enemy is (location), what the enemy is (acquisition level), and how healthy the enemy is (state). In the SAt score, we did not consider information about friendly forces or terrain because in our experiments the operators consistently had very good information in those areas. The SAt model also did not include neutral entities. Although neutrals added complexity and additional information gathering requirements to the scenarios, the command cells typically did not dedicate sensors to trying to find civilians on the battlespace. That said, the impact of civilians to situation awareness can be significant, and a more elaborate SAt model may include, for example, the awareness of civilians in the proximity of enemy entities or a decreased score when a neutral entity is incorrectly identified as an enemy. The evaluation of the SAt score was possible in our experiments because every spot report (report about a detection of an entity) included a unique identifier that allowed us to relate unambiguously a detected entity to the actual entity. This information was not available to the commander or his staff but was available for analysis. Of the three components of situation awareness considered in our model, the awareness of where the enemy is located is perhaps the most tangible. The location component of the SAt score is a measure of the accuracy of the perceived truth location of a given entity as compared to the ground truth location. For example, an inaccurate sensor reading or a target that moves after detection

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may lead to a significant error between where an enemy entity is thought to be as compared to where it actually is. Until an entity is detected, its location score is zero. Once detected, the location score is reevaluated as the entity moves and as additional spot reports arrive. If the location information becomes unusable, the overall score for that entity (including acquisition and state components) also reduces to zero. The location score is assigned using three categories— unusable, actionable, and targetable. These categories are defined by the munition accuracy and availability at a given echelon as well as the capabilities of available sensors. In other words, a target is considered targetable if the distance between the actual location of the entity and its perceived location is within the search radius of the best available munition. Likewise, the target is considered actionable if the location error is small enough that the commander could send a sensor to collect additional details on the location of the entity. The location score is defined by the information provided by the sensor network; neither the commander nor his staff can influence the value except to dedicate more sensors to refine the available information about a target. Because different sensors have different degrees of accuracy for location information, it is important that the commander understand what sensors have covered an area and how long ago the target was last detected. Although the commanders in our experiments were presented with a target location error based on the fused sensor picture, this uncertainty did not seem to be a major consideration in decision making, and the commanders usually presumed that location information on their common operating picture display was correct—immediately issuing orders to engage identified enemy platforms without first checking location uncertainty. Knowing the location of an enemy asset is not sufficient: the commander and staff also need to know what the enemy asset is. This second aspect is called the acquisition component of the SAt score and measures how completely and correctly an entity is identified. We considered the following levels of acquisition: • Detect—a sensor perceived an object of possible military interest but did not recognize it otherwise. • Classify as tracked, wheeled, or biped—the sensors (and processing systems) classified the object according to its mobility class (e.g., tracked vs. wheeled vehicle). • Classify as enemy or neutral—the entity was classified as enemy based on radio signal processing. Because neutral entities did not emit radio, and all information about the Blue force was known, the classification as friendly was not included in this score. • Recognize/identify—the entity’s specific type or model was determined (e.g., T-72 vs. M1). This provides the commander with enough information to fully understand the threat of the detected entity.

The acquisition scores represent how correctly the command cell (or the fused spot reports) acquired and classified an enemy entity. For example, if

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one of the cell members correctly identifies a tracked enemy vehicle based on a sensor picture, the score increases from “classify track” to “recognize/ identify.” However, if the command cell incorrectly identifies the same target, the score remains “classify track” because it is the most correct representation of the entity available to the commander and staff. Simply knowing the location and identification of an enemy entity is insufficient. For example, if the commander engages a target beyond line of site of his entities, he needs to know whether he effectively disabled that target before proceeding through that area. We model this need to know the state of the enemy (e.g., whether an entity is alive, dead, or damaged), as the third critical input to the SAt score. The state component of SAt is a measure of the accuracy of the perceived state knowledge compared to the actual state of the entity. For example, incorrectly marking an entity that has actually been killed as still alive may lead to expending additional scarce resources to reengage. Likewise, incorrectly marking a healthy entity as dead may have lethal consequences when the friendly force moves within range of the entity. To determine the state score, we first evaluate how much of each enemy entity’s mission is dedicated to moving, firing, and communicating (spotting and reporting). This evaluation is roughly based on the capabilities of the entity and how the enemy commander typically uses the platform. The correctness of an individual state assessment is then calculated by summing the correctly identified components of combat function. For example, a battle tank may have 35 percent of its function as moving, 55 percent of its function dedicated to firing, and 10 percent of its function as reporting or communicating. If the entity is perceived to be “total kill,” but the actual state is “firepower kill” (and therefore also communications kill), the assessed state is correct for the fire function and the communication function but incorrect for the movement function. Therefore, the state score of the entity is 55 percent + 10 percent = 65 percent. The three component scores (location, acquisition, and state) are evaluated for each entity in the opposing force and then combined to form an overall score for a particular side’s knowledge of its opponent. The formula used to support this evaluation is shown in Figure 5.3. It produces a score between 0.0 and 1.0, with 0.0 indicating a complete lack of useful information, and 1.0 indicating the possession of all required information. A score of 1.0 would imply that at a particular point in time the commander has access to full knowledge about the location, type, and state of all enemy entities within his area of interest. We introduced coefficients into the formula to enhance its utility: • The weights, W, allow the analyst to emphasize the three components of the combined score to different degrees. Setting a weight to zero eliminates the contribution of that measure from the score. We have applied the following selection of weights: location (Loc) was weighted at 0.45, acquisition (Acq) was weighted at 0.45, and state (Sta) was weighted at 0.10. This initial selection of weights reflects

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Figure 5.3. Calculation of instantaneous SAt score.

a concern that the difficulty experienced by the operators in assessing the impact of the application of effects could dominate the portrayed values of SAt. Sensitivity analyses conducted on the data available from the experimental series indicates that the general trends of the curves are not sensitive to moderate changes in the weighting values. • The criticality coefficient, c, enables the analyst to account for certain entities that might be of more value than others, regardless of location. For example, an air defense platform may be more critical to find, identify, and eliminate than a supply truck. • The decay factors, d, were used in early experiments to account for the loss in value of information over time. In the simulation, information was made available to the operators through internal reports after each sensing event. The age of the information is measured as the elapsed time since the last report of a particular target. The information is of most value immediately after a report and begins to lose value from that point forward. In later experiments, this decay component was replaced with a more accurate representation of the value of information based on constantly updated location accuracy information (discussed above). When an entity moves beyond actionable position information, its track is lost, and both the location and acquisition components of the score go to zero.

The SAt formula involves summation over a set of entities. But what is included in that set of entities? The simplest possibility is to include all enemy entities deployed in the battlespace. However, it is often more meaningful to include only particular types of targets or targets in a specific geographic area. Our SAt model allows for such specifications. For example, we used this flexibility to explore SAt scores when applied to those entities that the commander defined as most dangerous targets (MDT) or high-payoff targets (HPT). The typical analytic package produced for each experimental run included the SAt

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of all enemy entities, the SAt of the MDTs as defined by the commander, the SAt of the HPTs, and the enemy’s SAt of friendly forces. In analyzing each trial run, recomputation of the SAt score is triggered by a number of activities such as the receipt of a spot report, entity movement, fire missions, or an entity state change. Because such activities occur very frequently, the resulting graph is a nearly continuous curve that describes the evolution of the score over time. SENSOR COVERAGE A command cell obtains its information from sensor reports (including human warfighters’ reports). We found sensor coverage to be the key operational factor affecting the SAt score. Understandably, knowing the status and capability of available sensors is crucial to the commander. With the CSE, sensor detections immediately populated the COP to give the commanders a sense of the battlespace. However, this immediate display of information also often had the unintended consequence of leading the commander to mistakenly conclude that an area absent of detections was devoid of enemy entities. To reduce the risk of being surprised by a significant enemy force, the command cell had to understand how effectively an area had been covered with sensors. However, the absence of detections can also contribute positively to situation awareness. For example, suppose the commander directs certain sensors to observe an area, and the sensors do not detect anything. Knowing that the area is void of detections is very useful, assuming of course, that the lack of detections is due to the absence of enemy entities and not due to inadequate sensor coverage. To explore how effectively the commanders and staffs used their sensors to cover key areas, we developed a tool that examined the quality of sensor coverage across the battlespace. This tool enabled us to consider the commander’s level of confidence that an area void of detections on his visual display was, in fact, void of enemy entities. To compute the sensor coverage quality score, we first define one or more regions of the battlespace as critical areas of interest for the unit. Each of these areas is then given an importance score, and a regular grid is superimposed over these areas, as depicted in Figure 5.4. We then evaluate each grid cell (as described below) and compute an aggregate score based on individual cell scores and the importance of each cell. Our model for computing the sensor coverage quality accounted for a number of factors: Sensor mix—Different sensor types have different capabilities and are often much more effective in combination than they are alone. For example, some sensors can only detect moving targets, while others can only detect stationary targets. Separately, either one provides some amount of information about the area, but the combination is more effective than the sum of the parts.

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Figure 5.4. Area definition and analytic grid. Time value of information—In addition to the effectiveness of the sensors that have covered an area, it is important to account for how much time has passed since the area was covered. Time of coverage—The longer a sensor covers an area, the more effective it is at detecting entities within an area. There are several reasons for this: a stationary target may begin moving, creating possible detection opportunities, or an entity that was out of sensor range may move into range. Number of times covered—Spot-mode sensors do not cover wide areas within a single time increment but look at a localized region. Therefore, for spot-mode sensors, an important parameter to consider is the number of passes the sensor makes over a given area. In this case, the effective coverage increases the more times an area is covered. Distance from sensor—Sensors tend to provide more accurate and reliable detections at closer ranges. They are nearly ineffective at their extreme detection range.

In order to explore how the rate of change in SAt correlates with the effectiveness of sensor coverage, we plotted the quantitative measure of sensor coverage against the SAt curves. This helped reveal three primary reasons for situations in which SAt grew slowly or stayed relatively constant: • The sensors were idle. • The sensors were looking at an area that had already been covered by that sensor type. • The sensors were covering a new area, but there was nothing there to find.

Figure 5.5 shows an example of such an analysis. During this run, the SAt growth followed a fairly typical trend of rapid initial growth due to the initial intelligence feed from higher headquarters and due to sensors coming online,

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followed by a relatively flat period before the Blue unit began ground operations and then a rapid growth as the ground forces moved into enemy territory and found enemy targets at close range with sensors or human vision. Two sensor coverage curves are shown in the lower portion of Figure 5.5: the darker line represents coverage of all areas beyond the initial line of departure, and the lighter line represents the areas most critical for mission success. The commander and analysts jointly identified these critical regions. Together, these charts indicate that after an initial surge of intelligence information, there were few new detections because no new area was being covered by the sensors. As the ground forces began maneuvering, the sensor coverage quality increased, and new information became available to the commander. In addition to providing the sensor coverage measurements and graphs, the tool’s graphical interface shows analysts the positions of Red and Blue assets over time, identifies which sensors make detections, indicates differences between perceived location and actual location, and displays Red and Blue attrition over time (see Figure 5.6). SITUATION AWARENESS—COGNITIVE Although SAt is a relevant measure of the information available to a commander, situation awareness ultimately occurs in the mind of the commander: “Technology

Figure 5.5. Analysis indicates a strong relation between SAt and sensor coverage.

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Figure 5.6. Graphical display of the Sensor Coverage Tool.

can enhance human capabilities, but at the end of the day . . . we can have ‘perfect’ knowledge with very ‘imperfect’ understanding” (Brownlee and Schoomaker 2004). It is the commander who perceives, categorizes, and synthesizes the available information into a complete picture that bridges the three levels of situation awareness discussed in the previous chapter. In general, Situation Awareness—Cognitive (SAc) has a complex relation to SAt. Unfortunately, measuring how effectively the commander understands the battle situation is not as simple as developing database queries and scoring algorithms. Throughout this experimental program, we searched for ways to understand what was in the commander’s mind and how well he understood the tactical situation. At the conclusion of battles in the early experiments, we asked each commander to assess the level of situation awareness that he achieved during that battle on a scale from 1 to 10. This retrospective, subjective assessment is inherently biased and is strongly influenced by the surveyed individual’s assessment of the recent battle outcome. Because these surveys were conducted after the run was complete, and the commander knew how effective his unit had been, there was an artificially strong correlation between the commander’s self-assessed SA and unit success in the battle. This postexperiment self-assessment often contradicted what the commander said during a run. An example of this postexperiment bias is shown in Figure 5.7. In this run, the commander’s verbalizations indicated a severe lack of situation awareness regarding the critical northern avenue of advance, yet he rated his overall situation awareness very high because the unit eventually achieved a clear victory. During these early experiments, we were fortunate to have a commander who spoke freely about his current thoughts and perceptions of the battlespace. At times, the commander addressed his thoughts directly to specific cell members, while at other times, the actual target of the discourse was unclear. These verbalizations contained critical information about the commander’s current understanding of the battlespace—understanding that analysts parsed

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into components that addressed specific aspects of situation awareness: perception, comprehension, and projection. For example, consider the run depicted in Figure 5.7. In this case, the commander intended to attack through the northern corridor to avoid the expected enemy strength in the south. This turned out to be a very accurate assessment, and the northern corridor contained very few enemy units. The graph depicts the level of SAt that the command cell developed over the course of the run. Though the commander expected a weak force in the north, he was clearly surprised by how weak the force actually was. The enemy entity titles depicted on the chart (e.g., Garm, Darya) indicate the Red force that was deployed in the north, and the times at which they were destroyed. As the chart suggests, there was only one enemy entity left in this area by the end of the run. A set of quotes extracted from the audio log captured the commander’s developing mental model of the situation. It appeared that he was either convinced that the enemy had positioned more forces in the north than were actually there, or that he was extremely sensitive to the potential of a lone platform that could inflict significant damage on his force. At the start of the exercise, the commander recognized that he had “no read” (i.e., inadequate understanding of the situation) in the north. He interpreted that as a lack of knowledge about an existing enemy threat. He perceived that “there is a lot of stuff ” in the north and that perhaps there was an enemy platoon positioned there. He further projected that this enemy force would be waiting in ambush for the advancing friendly force. Consequently, he caused his unit to slow its rate of advance to 5 km/h, an exceedingly slow movement rate. The star on the chart at the 75-minute mark indicates the point when a cell member reported that he had examined all available sensor range fans and that the area appeared clear for forward movement. The commander was not influenced by that input and, as the quotes suggest, continued to perceive a significant threat in the north. In the postexperiment interview, the commander indicated that he had all the information he needed at about the 4-minute mark. However, in actuality, he did not cross the line of departure until 27 minutes into the run. When asked why, the commander suggested that the “delay was caused by continued information gathering.” This appeared to be his approach to dealing with uncertainty and to aligning his mental model with the system’s reports (or more often than not, aligning the system’s reported information to his mental model). The Red commander suggested it was a good example of the “paranoia factor”—a hesitancy of commanders of lightly armored units to move forward despite an open path and sufficient sensor coverage showing no enemy force in the region. While this approach of comparing combat events, operator dialogue, SAt scores, and battle outcome is insightful and informative, it is also time consuming and demands a very detailed understanding of the events of the battle. Therefore, we continued to search for explicit and direct measurements of cognitive situation awareness. Because postexperiment surveys proved to be unreliable measures of situation awareness, we attempted to administer surveys to operators during experiment execution, expecting

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that this would give us insight into the instantaneous state of awareness at given points in the battle. Unfortunately, because of the rapid pace of the simulated battle, command-cell operators were reluctant to take their eyes off the screen even for the limited amount of time (less than one minute) required to complete a survey, and the quality of survey responses reflected the fact that the participants viewed these surveys as a distraction from their primary duty of fighting the battle. In later experiments, we abandoned the standardized surveys altogether. Instead, we relied on three other techniques. First, we changed postrun surveys into postrun interviews to allow the exchange to be tailored to emerging trends or specific events from a completed battle. This technique also enabled us to maintain a more consistent quality of information. These interviews were based on the questioning framework of the Critical Decision Method (Klein, Calderwood, and MacGregor 1989).

Figure 5.7. An example in which the Blue commander exhibited poor cognitive situation awareness, SAc, in spite of high SAt available to him.

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This process identifies one or more critical decisions in a run and explores the commander’s thinking at the time of the decision. By limiting the focus and not asking the commander subjective questions, this technique minimizes the problems encountered with postrun surveys. Second, we made extensive use of dedicated observers to study and analyze decisions and verbalizations obtained from squad leaders, platoon leaders, and commanders in which they discussed their perception of the battle and the environment. The experimental facilities gave the observers access to all operators’ screens and communications. Using these information feeds and customized collection tools, the observers developed an analytically rich data set. Finally, we employed a more formal structure for the periodic “commander’s read”—the verbal report on the commander’s assessment of his situation and the enemy situation. Unlike our earlier attempts to encourage the commander to speak nearly continuously, we now requested that the commander give a verbal situation “read” at key points in the battle. We also provided the commander with an outline that included his assessment of friendly and enemy troops, and an indication of whether or not the mission could be completed on schedule. Both during and immediately after each experimental run, analysts recorded the commander’s reads and qualitatively assessed their correctness as compared with ground truth. This provided a subjective measure of cognitive situation awareness (SAc) as it was expressed in the commander’s reports and in dialogues with other operators (Figure 5.8). Additionally, the observers were aware of the actions and intent of the Red commander and were able to take this information into consideration when making their assessments. Each commander’s SAc was assessed by observers on a Green, Amber, and Red scale for awareness of the Red forces and Red plans; his own forces; and his own plan status. The foundation for much of the analytic effort in the latter experiments was the process trace (Woods 1993) that focused around key events (e.g., a decisive battle, a missed decision, or an effective and timely decision). After each run, analysts identified one or more key events based on their relative battle impact and the commander’s cognitive effort, compiled all available

Figure 5.8. An example of SAc as assessed by an observer.

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information for those events, and pieced together a detailed storyboard that informed other aspects of the analysis. In order to relate information availability to situation awareness, these individual analytic results were plotted over time along with the relevant SAt curves. This comparative examination led to the development of a new metric reflecting the cognitive environment in the cell, the battle tempo. BATTLE TEMPO One of the primary inhibitors to developing situation awareness is the tempo of operations in a battle. At times of peak activity, a commander is often absorbed in the details of the moment and fails to comprehend the bigger picture. We saw situations in which the commander made nearly continuous verbal observations about details he was seeing on his screen but never synthesized that information into a coherent picture. The common approach was to watch the screen for changes and then react to those changes. All commanders in our experiments exhibited this behavior to some extent, and the tendency became more pronounced as the tempo of operations increased. To better analyze this trend, we introduced a measure of battle tempo—the frequency of battle-relevant events that influence a command cell. This metric gives an indication of external cognitive factors that are likely to impact the commander’s ability to process information and act in a timely manner. The following events are available in either the log files or the observer database and are used to quantify the battle tempo score: Sensor detections—These represent the most prevalent source of information available to the commander. Of these sensor detections, more emphasis is placed on first detection of a given entity (i.e., when an icon of the entity first “pops up” on the common operating picture) than on subsequent detections because first detections prompt the greatest response by the cell members. Taskings—Giving a fire tasking, movement tasking, or sensor tasking to a unit or an entity takes effort. The frequency of issuing tasks is an indicator of the cognitive load on the command cell. Entities lost—Losing a friendly entity is a significant event that calls for a major cognitive effort: What killed it? Can we accomplish the mission without it? How are we going to reallocate resources? These events also tend to increase the pace of other events, such as taskings. Collaborations initiated—The commander typically collaborates with other operators or cells prior to making decisions. These collaborative sessions are reflective of the pace of activity within his cell.

The general form of the battle tempo metric is as follows: Tempo = W1 · FD + W2 · SD + W3 · CI + W4 · GT + W5 · FT + W6 · BA C2 C3 C4 C5 C6 C1

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Where: Wi = A weighting factor. Ci = A normalizing factor. FD = First detections of enemy entities per unit time. SD = Subsequent detections of enemy entities per unit time. CI = Collaborations initiated by the commander per unit time. GT = General taskings initiated from the cell per unit time. FT = Fire taskings initiated from the cell per unit time. BA = Blue entities lost per unit time.

In a manner similar to the SAt curves, calculating an instantaneous battle tempo score repeatedly during a run produces a curve that reflects changes in cognitive load over time. Example curves are shown for three echelons in Figure 5.9. COLLABORATIVE EVENTS By plotting multiple metrics—SAt, SAc, sensor coverage, and battle tempo as a function of time—in one chart, we are able to visually explore relationships between the metrics and underlying phenomena. We call such plots stacked charts. An example of a useful stacked chart is shown in Figure 5.10. This chart focuses on the CAU-1 SAt and battle tempo and was useful in the analysis of that unit during Run 8 of Experiment 6. This stacked chart highlights three key events and four critical decisions (denoted with large starts in the top section of the graphic). In Event 1, four of the primary information gathering units, the Unmanned Aerial Vehicles (UAVs), were lost to enemy fire early in the run.

Figure 5.9. An example of Battle Tempo score evolution over time.

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Although the commander recognized the loss of these units, he did not systematically consider the effect of this loss on his Reconnaissance and Surveillance (R&S) plan. The second key event was the asychronous maneuver of the two CAU units. The lack of effective coordination between the units allowed the enemy to strike against the attacking forces. This led to the third key event— the destruction of CAU1 and its failure to achieve the mission objectives. Even after this impace on force strength, the higher echelon commander (CAT CDR) pressed on using the current plan instrad of systematically considering the capability of the remaining force. A similar product could be generated with respect to any command cell included in our area of analytic focus. Stacked charts are particularly helpful when used in conjunction with process traces. A process trace produces a detailed chronicle of how an incident of interest came about. With the process-tracing methodology, we can map out how an incident unfolded, including available cues; what cues were noted by operators; and the operators’ interpretation of those cues in both the immediate and larger contexts. Process tracing helps to link collaboration to changes in situation awareness and to connect situation awareness to decision making with a focus on the operators and their use of the battle-command system. A stacked chart typically shows four elements: • Which operators were involved in collaborative events • The assessment of each commander’s SAc

Figure 5.10. An example of a stacked chart: CAU-1 in Experiment 6, Run 8.

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Figure 5.11. Collaborative events display.

• Select SAt curves that may include single or multiple curves for both Red and Blue commanders • Battle tempo

Figure 5.11 is a detail of the top chart in the stacked chart. This view details the observer database entries for collaborations that occurred across, and internal to, each of the command cells included in the analytic focus. The vertical axis contains the list of operators by cell. A blue diamond in the chart indicates a participant in a collaboration, while the pink square indicates an initiator of the collaboration. The second chart in the stack (Figure 5.8) reflects assessments of the commanders’ cognitive situation awareness (SAc) as expressed in the commander’s reads and in his collaborations with other operators. These subjective assessments were made by observers based on comparisons between individual commanders’ expressions and the ground truth situation available to the observer. In the chart, the assessments of the Blue commander’s awareness of the Red forces and Red plans are indicated by a square; awareness of his own forces, by a triangle; and awareness of his own plan, by a diamond.

Figure 5.12. SAt curve over time.

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Figure 5.13. Battle Tempo curve in a stacked chart.

The third chart is the SAt curve (Figure 5.12) and may depict the SAt curve for one or more command cells as indicated in the legend at the bottom of the stacked chart. The example in the figure is for CAU-1’s SAt of the enemy and the enemy’s SAt of CAU-1 in Run 4 of Experiment 6. The fourth chart (Figure 5.13) contains the battle tempo curve—an assessment of the relative rate of activities within the commander’s cell. Analysis using these stacked charts led to insights regarding the amount and nature of information available to the commander; the relationships between that information and the commander’s situation awareness; how the cells collaborated; and the linkage between situation awareness, decision making, and battle outcomes. Overall, this led to a number of interesting conclusions, some of them encouraging; and some troubling. Perhaps for the first time quantitative characteristics of battle command have been experimentally captured with special attention to situation. Such quantitative analysis begins to shed light on the relation between the science (particularly the use of technology) and the art (human cognitive processes) of command, and on the cognitive dynamics that both enable and hinder the commander in making sense of the battlespace.

CHAPTER 6

Making Sense of the Battlefield: Even with Powerful Tools, the Task Remains Difficult Stephen Riese, Douglas J. Peters, and Stephen Kirin

Some of our experimental findings, even if relatively obvious, provide possibly the first-ever quantitative validation of long-standing intuitive expectations of military practitioners. Certain findings are far from obvious; others are perhaps counterintuitive and even somewhat troubling. In this chapter, we discuss several such findings: • Information advantage, and not level of acquired information, is the stronger indicator of tactical outcome. • Human tendencies and machine-interface limitations make Situation Awareness (SA) hard to maintain. • Gaps and misinterpretations in SA are alarmingly common. • Shared information does not necessarily mean shared SA. • The cognitive load of future battle command is extremely high and tends to be disproportionately borne by the most junior leaders.

INFORMATION ADVANTAGE RULES SA is a remarkably strong determinant of battle outcome. Of all the factors examined in these experiments, SA easily surfaced as the most influential. The relationship between SA and battle outcome is worth discussing in detail. The outcome of each experimental run can be assessed in terms of whether the Blue or Red force achieved a tactical advantage. This assessment is based on mission accomplishment. For example, Blue may be required to clear a path to enable the passage of follow-on forces while Red is required to prevent Blue from penetrating its sector. While runs did not always result in a clear

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win or loss for either side, the determination of which side held the ultimate tactical advantage was usually rather clear. To help illustrate this characterization, we show a set of Situation Awareness— Technical (SAt) curves for Experiments 4a and 4b in Figures 6.1 and 6.2, grouped by assessed battle outcome. Results from later experiments tend to be similar but are more complicated due to the increased complexity that multiple echelons introduced. “Advantage Blue” or “Advantage Red”—whether it was the Blue force or the Red force that gained advantage in the battle—was a group assessment based on professional judgment of the observers, white cell, analytic team, and participating operators and was not influenced by the captured data or subsequent analysis. Some remarkable relationships between the information availability (as measured by SAt) and the tactical results emerge. In all of the charts, the initial spike in SAt reflects the intelligence feed provided to the Blue command cell. The size of that spike is relatively consistent across all runs as the amount of information initially provided was intentionally controlled. Although Experiments 4a and 4b were manned by different Blue operator teams, those command cells achieved a comparable peak of SAt across the runs (approximately 60%–63%), probably a reflection of the inevitable close fight that occurs in every run. On average, the Experiment 4b Blue command cell achieved a lower average Blue SAt score than the Experiment 4a cell (40% vs. 47%). This may reflect the second team’s focused collection management plan—a plan that painted a clearer picture of key areas of interest at the expense of not understanding the more remote areas of the battlespace. By comparison, the first team tended to cover more of the battlespace with sensors, without specifically focusing on their planned avenue of advance. The Experiment 4b team also tended to negotiate more restrictive terrain, a tactic that tended to mitigate the contribution of certain sensors. Given that the amount of information ultimately available to the Blue command cell was similar across the 12 runs presented, the understanding of the relationship between SAt and battle outcome emerges from the relative difference between Blue and Red scores. In fact, it is the difference between Red and Blue available information, and not the level of Blue SAt achieved, that is the stronger predictor of battle outcome. When the Blue command cell achieved the tactical advantage, their SAt clearly dominated that of Red over the course of the run. In the cases in which Red gained the tactical advantage, the Red SAt usually matched or exceeded that of Blue for a significant portion of the battle (significant either in terms of length of time or the criticality of the point in the operation). Typically, each graph displays periods of time within the fight when there is rapid growth of SAt, gradual and continuous growth of SAt, or no growth of SAt. Rapid growth is usually a reflection of an intense close fight where many new detections are made, as in the last 30 minutes of Run 6 in Experiment 4b. There is usually an associated rise in Red SAt during these close fight encounters. Gradual and continuous growth, as in Experiment 4a, Run 8, typically reflects deliberate movement and an Intelligence Preparation of the Battlefield (IPB) process that enabled the appropriate placement of Named Areas

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Figure 6.1. Blue and Red SAt achieved in Experiment 4a.

of Interest (NAI). Periods of no growth, as reflected in the period between 20 and 40 minutes of Run 3 in Experiment 4a, usually result from one of several conditions, including the following: sensors are inactive and not looking, sensors are searching but are looking in areas that have already been searched, or sensors are covering new ground but finding nothing new.

Figure 6.2. Blue and Red SAt achieved in Experiment 4b.

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The difference in sensing capability between Blue and Red was intentionally great and resulted in different tactics and procedures during the battles. Because of Red’s heavy reliance on humans for detection, Blue was able to limit Red’s ability to see by limiting close encounters. As a result, Red SAt routinely increased most significantly during the close fight. This observation is vital to understanding the nature of the future, information-enabled force. While the value of information has always been appreciated, it is less widely recognized that it is the information differential, and not the absolute level of information acquired, that is the stronger determinant of battle outcome. The impact on future force design, tactics, and procedure development is significant: in the fight for information, acquiring a certain level of information is less important than achieving a substantial information advantage over the enemy. SA IS HARD TO MAINTAIN In spite of the great help provided by sensors and the Commander Support Environment (CSE), commanders and staffs found it very challenging to gain and maintain adequate SA. From the large number of possible causes for this challenge, some of which have not yet been fully explored, we examine two seemingly unrelated reasons in this section: the operators’ tendency to prefer acquiring new targets over conducting Battle Damage Assessment (BDA), and the CSE’s limitations in presenting information to human operators. Human Tendencies Human biases play a significant role in battle command. For example, belief persistence and confirmation bias (Endsley 2000; Nickerson 1998) were often seen to appreciably shape the course of our experimental runs. Another tendency—to prefer acquiring new targets over assessing the state of previously acquired targets—also emerged as one of the more consistent biases. Knowing the state of enemy assets is a key component of SAt and a key contributor to battle outcome. Although the importance of conducting adequate BDA was known to all operators, it emerged and remained a key challenge through all phases of the experimental campaign. From a tactical perspective, BDA (or the lack thereof) dramatically influenced the conduct of operations as operators attended to previously engaged targets, reducing their speed of maneuver and expending redundant munitions to mitigate the risk of operating in a less certain environment. In operator surveys, the lack of BDA was reported as one of the most significant detriments to achieving SA. Although there were certainly different approaches, intents, and capabilities across the various Blue command cells, a number of similarities surfaced:

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Command cells did not fully develop a set of tactics, techniques, and procedures to address the requirement for BDA. Tactics, techniques, and procedures emerged and were modified over time as the operators conducted subsequent missions. Some cells were still experimenting with methods during their final battle. At times, the recorded dialogues revealed a lack of awareness as to who was controlling BDA assets and who was responsible for making assessments. Command cells did not establish a priority for BDA. Prior to each run, the commander established priorities, including the designation of the most-dangerous targets and high-payoff targets. Quite remarkably, staffs often did not consider these priorities in conducting BDA during the mission. Command cells struggled to satisfy the competing demands of acquiring and characterizing new targets and assessing the status of known targets. Although most sensors are optimized for one task or the other, humans are needed to pursue both tasks. Some cells developed Intelligence, Surveillance, and Reconnaissance (ISR) protocols for the use of available sensors, but these usually did not address the use of sensors for BDA. Because of this, BDA missions were usually ad hoc and seen to deviate from the ISR plan. Command cells relied heavily on sensors with lower-quality images in making their assessments. For example, in Experiment 4b, although images provided by robotic ground scouts and by UAV were a less-frequent source of imagery to support BDA, the imagery they did provide was high quality, informative, and enabled correct BDA (Figure 6.3). Command cells failed to exploit the automated BDA capabilities provided by the CSE. This may reflect a lack of training and understanding, or it may reflect operator reluctance to forfeit control of assets that were also needed to identify new targets. It should also be noted that the available automated tools did not offer the capability to prioritize targets for BDA based on Commander’s Critical Information Requirements (CCIR). Command cells often lost visibility over how many times a particular target had been attacked, how many assessments had been cataloged, and how many images were available and had been viewed—information that is available in the CSE user interface, but not easily accessed and interpreted in the heat of the close fight. Command cells repeatedly relied on the option of attacking targets multiple times in the absence of effective BDA. Some groups developed engagement heuristics to mitigate the lack of BDA (e.g., “Fire two precision munitions at a most dangerous target on our axis of advance.”)

Even when operators dedicated resources to BDA, achieving an accurate assessment was difficult. From Experiment 4b, Figure 6.4 shows the total number of assessments attempted, and the results of those evaluations (correct, inconclusive, overassessment, or underassessment). Overassessment (for example, assuming an enemy asset experienced a catastrophic kill when it was only a mobility kill) might encourage the operator to move rapidly against an enemy asset that is still capable and dangerous. An underassessment (for example, assuming an enemy asset’s state was a mobility kill when it was a catastrophic kill) may encourage the operator to move cautiously against an enemy that is ineffective or destroyed.

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Figure 6.3. Source of BDA updates in Experiment 4b.

Less than 30 percent of the attempted assessments were correct; that is, the assessment matched the actual state of the enemy asset at that point in time. Recall that in our experiments we assumed that although a sensor can detect and classify an object as a potential enemy asset, the final interpretation of the images obtained by the sensor was left to a cell member. The images were simulated with a realistic degree of uncertainty and other defects, and to make a definitive assessment was difficult at best. This resulted in the relatively low level of assessment accuracy. For example, Figure 6.5 illustrates the

Figure 6.4. Correctness of BDA.

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content provided by images; the quality of the images reflects the simulation system’s adjudication of the engagement and the quality of sensor providing the image. We describe the difficulties of BDA here at length to help the reader appreciate one of the more significant challenges faced by the human operators. Given this challenge, it is somewhat understandable that the command cells often favored using sensors to characterize unengaged targets despite the importance of BDA. However, this tendency and the resulting diminished amount of quality BDA had a direct impact on the level of SA, as measured both by SAt and SAc. Limitations of the Common Operating Picture (COP) A second significant contributor to the difficulty of achieving SA is that of the CSE interface. In particular, displays often do not convey the level of uncertainty present in the underlying data and thus present a deceiving pic-

Figure 6.5. Quality of images available for BDA in Experiment 4b: (a) 27 percent of images showed no discernable target; (b) 34 percent of images showed the presence of a target with no discernable damage; (c) 29 percent of images showed smoke rising from the target but without sufficient detail to determine the extent of damage; and (d) 10 percent of images were of sufficient detail to accurately conduct effective BDA.

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ture to the human operator. For example, the CSE’s COP displays detailed icons showing where enemies were detected with no visual indication of how old the detections were and no visual indication of the level of confidence in the information presented. Without such critical clues, human operators tend to believe what they see on the screen and ascribe near certainty to the information. A key principle for designing systems to support SA is to directly present the level of certainty associated with information on the display (Endsley et al. 2003; Miller and Shattuck 2004). The CSE’s COP presents such a believable representation of the battlespace that it is often treated by operators as ground truth. This includes instances in which particular areas have not been searched by sensors, and the human operators discount the potential presence of enemy assets in those locations. When such sensor gaps align with the expectations of the mental frame developed by Blue operators, those operators have a tendency to take what is on the map at face value—believing in this false world. In other words, if a particular area on the display is free of Red force icons, then the corresponding area in the battlespace must be unoccupied and therefore safe to move through. This was certainly true halfway through Run 8 of Experiment 6, when one Blue commander stated that if the Red unit had placed a counterattack force in the vicinity of the objective, then his unit would have already bypassed the counterattack. In fact, there was a large counterattack force just beyond the objective in an area not yet covered by sensors (see Figure 6.6). The top view shows the information available to the commander through his system. The bottom view shows the ground truth of the same area of the battlespace. At this point in the battle, the Blue unit had begun its movement toward the objective (Town 23 circled in top picture). In this example, the commander’s understanding was based on the information display showing no enemy icons in the vicinity of the objective without a corresponding view of the sensor coverage in that area. This is the same event described in Figure 5.10. The CSE battle-command system interface is platform focused—it displays individual platforms and detections of enemy, as opposed to aggregations and higher-level interpretation. Commanders and their staffs tended to focus on using the system interface to task individual sensor assets to acquire information on individual platforms. In doing so, they often lost focus on the commander’s critical information requirements (e.g., the location of the Red counterattack force). Such shortcomings in multitasking have been found to be one of the most frequent problems leading to low SA (Jones and Endsley 1996). This tendency led to gaps in sensor coverage and made it difficult to predict Red disposition from the available limited intelligence. The tendency to overtrust the COP was more commonly observed when the time to complete the mission was running low. Additionally, the level

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of trust in the COP often correlates with other available intelligence. For example, an intelligence input from higher headquarters that suggested an enemy presence in a certain area often resulted in the Blue unit conducting an exhaustive search of that area and blaming the lack of Red detections on sensor or system problems. Similarly, an intelligence report suggesting that the enemy is not defending certain terrain led the Blue unit to move quickly through that area with little or no sensor coverage. The human tendency to favor acquiring and characterizing new targets over conducting BDA and the limitations imposed by the CSE COP display are but two challenges to gaining and maintaining SA. This difficulty drives the demand for more automated and semiautomated tools (e.g., BDA and BDA management), as well as the need for human training on the use of those tools. We also see a similar demand for COP improvements to help convey

Figure 6.6. Experiment 6, Run 8, at H + 43.

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the level of uncertainty in information to the human operator, consistent with SA-oriented design principles. GAPS AND MISINTERPRETATIONS Earlier, we addressed the importance of SA in determining battle outcome. However, even with adequate levels of SAt, we often find that commanders and staffs fail to interpret that information correctly, typically resulting in lowerthan-expected levels of SAc. Gaps and misinterpretations in SA are alarmingly common. It is likewise difficult to self-assess gaps in one’s own SA, especially in time-critical stressful environments such as represented in our experiments. In the subsequent sections, we consider a number of factors that contribute directly to incorrect interpretations of information and the creation of gaps in SA. These include a human bias toward preconception, the tendency of the COP to drive human attention, the human predisposition to want more information, and the cognitive impact of the lack of sensor coverage tools in the CSE. Human Biases—How to Overcome Preconceptions As a result of the planning process, Blue operators develop a mental frame, in the form of a plan, which guides their actions and understanding of the battle as it evolves. Part of the frame is a set of expectations about how the Blue force will conduct the operation, and likewise a set of expectations about how the Red commander is likely to array his forces and what he is likely to do as the mission progresses. These expectations can be strongly held and require significant disconfirming data in order to be discarded. This phenomenon has been called confirmation bias, representational error, or belief persistence, and has been found to be particularly hard to eliminate. People tend to develop stories that explain away conflicting evidence, rather than adjust their internally held model of the world (e.g., see Jones and Endsley 2000; and Cheikes, Brown, Lehner, and Alderman 2004). In each experiment, we see a surprising number of cases where, despite data to the contrary, operators continue to expect the Red unit to act a certain way. Rather than trusting the sensor reports as represented on the CSE interface, they listen to their “gut feel” or mental frame that tells them where they should find Red forces. For example, in the fourth run in Experiment 7, one Blue unit used UAVs to repeatedly sweep the mountainside and the area around two towns, expecting to see Red assets there. The commander’s frame was developed based on the Red force’s past tendencies of hiding in the mountains. In an interview conducted after the run (excerpt below), the Blue unit commander reveals how his strongly held beliefs about the Red force’s disposition influenced his actions. In fact, as shown in Figure 6.7, the Red force had only two vehicles

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near the search area, with the majority of his forces arrayed to the west and further north. Blue Commander: Well, initially in the assembly area, obviously the first thing we wanted to do is to try to gain situation awareness through sensors. So we deployed our sensors—our Class 1s and our Class 2s and a Class 3 forward into sector to try to gain intelligence. And from the get-go they weren’t picking up a lot. They made it to the first two towns—like 97, 98—somewhere around that area, and weren’t picking up anything, which surprised us. It kind of caught us off guard a little bit because you just expect to pick up something. So now you’re thinking, “Okay, is there something wrong with the sensor? Let’s keep going back.” And we kept going back and looking and looking and not seeing anything. Interviewer: What was your expectation for what you’d be able to see? Blue Commander: At least people. Usually when we get in the towns we see a lot of human indicators come up. There wasn’t even that. At a minimum, you think you’d see a lot of human indicators come up with people in the towns . . . at the least . . . Or we would see something up in the hills—some sort of sensor or observer up there. Interviewer: So what did you do about that? Blue Commander: We just continued to go around and around that town in just the hope that we’d pick something up. Interviewer: Did you have some sense of how long you would search a town until you felt like it was clean? Blue Commander: I guess not; no, I felt like, “I’m just going to keep doing this until I find something.”

Analysis of commanders’ assessments through all of the experiments suggests that both correct and incorrect assessments were distributed among the battles, often independent of the level of SAt available at that point in time in the run. This apparent lack of a correlation between a commander’s assessment and his concurrent level of SAt is inconsistent with assertions that suggest that having more, better, and timelier information necessarily leads to greater SA. Clearly, humans often exhibit the tendency—confirmation bias or belief persistence—to stand by their beliefs in spite of evidence to the contrary. This places unique demands on the human-machine interface and suggests potential limits to the level of SA that might be achieved. Humans tend to be hardwired to view new data through the lens of prior convictions and thus often interpret correct information as reinforcement of an inaccurate assessment. Attention Shifting—Where to Expend Cognitive Effort One phenomenon observed frequently during the course of these experiments was the Blue operator’s inability to use available information to envi-

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Figure 6.7. Area in which the Blue commander expected, but did not find, Red forces.

sion the future state of the battle (Level-3 SA). One possible explanation is that the command cell was inclined to rely heavily on the COP and often preferred to watch events unfold rather than projecting two or three steps ahead. Thus, in some respects, the CSE COP may inadvertantly encourage a more reactive cognitive posture. Endsley suggests that the “single most frequent causal factor associated with SA errors involved situations where all the needed information was present, but not attended to by the operators. This was most often associated with distraction due to other tasks” (Jones and Endsley 1996). Indeed, in a number of our experiments, we noticed that the CSE display drove a commander toward very rapid and apparently unproductive shifting of his attention focus. Constantly scanning the display for any new information, the commander would rapidly move the cursor from one enemy icon to another, hunting for additional details. This led him, apparently, to focus very narrowly on the most recent information, often in a very small area of the battlespace, and to allocate little attention to the broader appreciation of the battle. Furthermore, he would often induce other cell members to shift their attention focus to the subject of his immediate, narrow interest. Rather than directing the attention of the specific operator who needed to be cognizant of the event, he would often make a general announcement that forced the other operators to interrupt their activities. In chapter 8, we will return to this observation and examine it in more detail.

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We frequently see that attention is drawn to areas where the most current activity occurs, regardless of the importance of the information represented by the activity. Thus, commanders can easily lose overall situation awareness when they become too focused on specific areas of the battlespace, specific events, or specific distracting conversations. By focusing primarily on new information that populates the display, and therefore narrowing their attention, command cells can lose overall situation awareness. Information Overload—How to Recognize What Is Important The manner and amount of information presented through the CSE interface drove the SA process. An objective of interface design, therefore, should be to maximize the amount of available critical information while minimizing the amount of unimportant data presented to the operator. Overall, the CSE interface did enable the operators to execute their key tasks. In some ways, however, the robust set of available CSE options actually hindered operators’ tactical actions. For example, the amount of information that a cell member could cause to display easily exceeded the available screen space. Most conspicuously, the screen clutter produced by numerous adjacent icons made individual assets difficult to distinguish (see Figure 6.8). In another example, the operators’ tendency to set low thresholds for the automated alert function often caused multiple noncritical alerts. As a result, the system frequently overloaded the cell members with alerts (averaging as many as 10–12 per minute for a single operator) and thus impaired their ability to develop SA. Also, many of the images captured by sensors contained no usable information (e.g., they did not allow the viewer to discern the type of asset or the extent of damage following an engagement; see Figure 6.5). With no prefiltering capability, and with limited cataloging capability provided by the CSE system, the operators often seemed inundated with images: they had to review all that were provided and had to remember which images were new and which had been previously viewed, at least until they were comfortable with their current assessment. While each operator chose the alerts he felt were required to execute his assigned tasks, there seemed to be little general appreciation of the number of alert messages (hundreds per operator each mission) and the amount of noncritical information that various interface selections would generate. As a result, operators were often inundated with information, particularly alerts and images, and the value of each data element was lost in the sea of messages generated (see also Endsley et al. 2003). For example, in Experiment 4a, less than 10 percent of the alerts received could be classified as critical information; the vast majority of alerts were general in nature or provided redundant information. Interestingly, less than 5 percent of the decisions made by operators resulted from the alerts defined by those operators.

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Figure 6.8. CSE interface showing clutter that can result from too much information.

These observations suggest the need for both training and interface improvements. The system could provide fewer, yet more helpful, alerts that are a combination of specific and fused data. One author distinguishes between acute incidents in which the situation presents itself all at once and the alert must be immediate, and going sour incidents in which there is a slow degradation of the monitored process (Woods, Johannesen, Cook, and Sarter 1994). For example, it is easy to imagine an immediate alert, “out of ammunition,” but this type of alert could also be given in regular intervals—25 percent, 50 percent, and 75 percent ammunition exhausted— that allow the command cell to better assess the status and make decisions before the situation becomes immediate. Furthermore, a fused alert might only warn the operator when the amount of ammunition is being expended at a rate faster than the percent of the mission accomplished. An alert fused to the CCIR might indicate when a dangerous enemy asset is detected in the planned axis of advance. By reducing false alarms and providing such salient and distinct cues, the system would allow the operators to devote more attention to understanding trends and other more important decisionmaking tasks. Sensor Coverage—How to Know What Is Already Known Earlier, we suggested that a Blue command cell may experience periods of little or no growth in SAt because sensors are inactive, looking in areas that have already been searched, or are simply finding nothing new. In our experiments, all command cells were challenged to maintain awareness of locations and activities of their sensors. At times, valuable aerial assets awaited instructions while simply orbiting unproductively. Command cells often did

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not know where their sensors had already looked and employed sensors in areas that were already well covered. To further complicate the problem, the Red force avoided establishing any recognizable operational patterns and specifically avoided assuming similar force dispositions from one experimental run to another. For example, the Red commander put decoys where he thought the Blue unit expected actual forces and often interspersed decoys with actual forces to portray a larger force. Red also did not attempt to defend the entire battlespace but instead massed combat power in one area and took risk in another area. Further, the Red force organized itself into irregular groups composed of platforms of different types that precluded the Blue command cell from making consistent conclusions about the enemy force disposition. Recognition of these conditions motivated the development of a sensor coverage analysis tool (illustrated in Figure 6.9) that could portray, over time, the amount of terrain being searched by the Blue sensors. Among its functions, the tool measures the quality of sensor coverage by comparing the amount of terrain searched to the Blue unit’s area of interest. A sample resultant curve is depicted in Figure 6.10. This afforded the analysts a visual comparison of the SAt curve to the amount of terrain covered by the available sensors. Additionally, because operators were extremely sensitive to the appearance of new icons on their displays, and because they are the primary causes of the distinct jumps observed in the SAt curves, the display shows the number of enemy assets detected in each 10-minute period. In all battles examined with this

Figure 6.9. Sensor coverage analysis tool.

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approach, there was a clear positive relationship between the amount of terrain searched and the corresponding level of SAt. The sharpest increases in coverage tended to occur when Blue ground elements or more capable UAVs moved forward, covering new terrain with high-quality direct vision optics (DVO) sensors or human vision. However, the contribution of ground elements came with a cost: since Blue ground platforms’ sensing capability was comparable to that of the Red platforms, the ground advance usually contributed to an increase in Red SAt. And because the Red force was usually dispersed throughout the battlespace, there were few instances of significant coverage growth without some number of detections (Figure 6.10). The commander often based maneuver decisions in large part on whether or not an area had been searched by a sensor. Seldom was he able to quantify or assess the quality or currency of that coverage. In addition, if an area had been searched and no enemy entities detected, there was no mechanism to remind the commander that the area was potentially vacant. An investigation of the factors that contribute to the commander’s decision making indicates that the knowledge of what is not there often may be as important as the

Figure 6.10. Relationship between sensor coverage and SAt.

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knowledge of what is there. For example, recognizing gaps in the enemy’s defense can have as many tactical implications as knowing where his strength is located. To this end, we explored the possibility of developing a CSE version of the sensor coverage tool that captures how effectively critical battlespace areas have been covered by sensors (a prototype display, based on the analytic tool, is shown in Figure 6.11). Such a sensor coverage device would assist the command cell in understanding how effectively the plan is being executed and when an area has been sufficiently covered and movement should begin. Not having such a tool, one Blue commander described his mental model of the sensor coverage provided during the battles as discrete, rectilinear, and homogeneous—similar in design to the crisp and distinct NAIs that were drawn during the planning phase. In reality, as illustrated in Figure 6.11, sensor coverage is continuous, nonlinear, and heterogeneous. The four impediments to SA presented above all have a significant influence on decision making and, as we have seen, are not overcome with more information. When adequate information is available, human decision makers may often interpret it incorrectly and at the same time demand even more information. Automated tools that quickly provide large amounts of information to humans do not yet inherently help overcome human tendencies and biases. In fact, they often exacerbate the biases as commanders look for the specific data elements they expect to see while discounting the perceived noise of the other information. The impact of these findings for the future force clearly includes specialized training (largely not discussed here), the direct presentation of interpreted information (Level-2 and Level-3 SA), and the need for the interface to help recognize when humans are developing or maintaining incorrect views of the world. To account for human limitations in perception and attention span, future systems should present information to the human operator with an indication

Figure 6.11. User interface of the Sensor Coverage Tool.

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of certainty and degree of urgency. Absent such a mechanism, humans tend to treat the most recent information as the most urgent, and all information as equally credible. The system should help curb the operator’s predisposition to demand more information, regardless of his current cognitive load. Also needed are sensor management tools that indicate the amount and quality of previous sensor coverage. The lack of such a mechanism often results in deficient decisions even while the operator is unaware of the underlying information gap. COMMON BUT NOT SHARED Shared information does not necessarily lead to shared SA. Our original expectation was that the collaborative nature of the CSE would lead to a shared perception of the environment (shared Level-1 SA). One could argue that sharing this information across the command cell would lead to a shared comprehension (SA Level-2) and a shared projection (SA Level-3). Evidence from our experiments suggests that although a shared display of information does lead to a degree of shared awareness, its interpretation and projection differs among individuals, and shared understanding can only be developed through effective collaboration. Several observations from Experiment 7 serve to illustrate this finding. Earlier in this chapter, we presented a number of SAt curves that indicate information availability and its correlation with battle outcome. While the information was available equally to all members of the cell, it was often interpreted differently at each station. Just as in a class in which all students have the same text and hear the same lectures but do not achieve the same level of understanding of course material, operators in our experiments often drew different conclusions from the same data. In particular, the following three commonly held assumptions proved to be faulty: Assumption 1: If we can show operators more Red and Blue information, then they will have a more accurate interpretation of both the Red and Blue situations. Assumption 2: If we can show operators more Red and Blue information, then they will make better predictions about how the Red and Blue actions will play out. Assumption 3: If we can show operators more Red and Blue information, then they will have a shared Level-2 and Level-3 SA across the command cells.

In the second run of Experiment 7, we see a compelling example in which assumptions 1 and 3 do not hold true. In this battle, the Combined Arms Unit (CAU)-2 commander inaccurately assessed that the loss of two reconnaissance vehicles was due to a Red minefield. In fact, the units had been destroyed by an undetected enemy scout vehicle. This seemingly minor sense-making failure had a profound impact on the manner in which the battle was fought by the entire Blue team. In fact, that failure was arguably the momentum killer that negated any possibility of Blue mission accomplishment, as the Blue unit

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struggled to neutralize the perceived minefield. Through these events, two different Blue commanders looked at the same information and drew two different conclusions about the enemy force—the failure to achieve shared SA across the team. Early in the mission, the commander saw data in the form of three Automatic Target Detections (ATDs) in the vicinity of Town 97 and immediately suggested that this could indicate a minefield and sent a slow-moving mine-detecting sensor, a MULE, to investigate (see Figure 6.12). Although the ATDs actually represented Red infantry, the Blue unit had developed an expectation of Red minefields near their route of advance. Based on the movement corridors provided by the terrain, and on their expectation of what the Red force would anticipate that the Blue force would do, the Blue operators came out of the mission planning session with a mental frame that produced a prediction of Red mines. The ATDs that arrived in close proximity to each other supported the cell’s expectation of a minefield, thus serving as a mental anchor for the invalid frame that continued to develop. As the run continued, the Blue operators’ attention was diverted to Town 101 (also in Figure 6.12), where multiple sensor hits produced a picture of several Red combat vehicles. The Blue team spent considerable time engaging and collecting BDA on those Red assets. About 10 minutes later, the Blue team lost two of its unmanned reconnaissance vehicles, both at the same location near the ATDs mentioned previously. The Blue operators’ mental frame for Red minefields in that vicinity was so strong that the commander immediately concluded that the reconnaissance vehicles’ loss was due to a minefield. In fact, the vehicles were lost to fires from Red armored infantry vehicles near Town 101, where the Blue unit had earlier destroyed several Red assets. The Blue commander did not even consider that Red indirect fires could have accounted for his losses. He lacked a key piece of information: an indication of what killed the unmanned reconnaissance vehicles. This data alone would likely have dramatically changed the outcome of the battle. The data provided by CSE interface concerning this incident was accurate. It was the human comprehension of the data that was flawed. The Blue operators were correct to assume that the Red team would lay mines near the Blue avenues of approach—Red mines were in fact further west of their suspected location. However, the ATDs provided such strong anchors for their mental frame regarding the specific minefield location that this mind-set could not be broken. This may be an example of confirmation bias—the tendency to search for or interpret information in a way that confirms one’s preconceptions. For the Blue commander, the new information fit his working hypothesis so well that he did not perceive a need to generate alternative explanations. While individual sense-making mistakes are to be expected even under the best circumstances, this scenario is illustrative of a number of cases that begin to challenge the validity of the assumptions presented earlier. To further examine assumption 1, we compare Level-1 and Level-2 SA using the SAt curves (Figure 6.13). Here we see that the Red and Blue units

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Figure 6.12. Phantom minefield versus actual location of Red mines.

had comparable SAt scores up until about 50 minutes into the run. However, we know from the Blue commander’s actions just described that his explanation of the data available around Towns 96 and 97—his Level-2 SA—was not strong. At 70 minutes into the run, the Blue unit lost two unmanned reconnaissance vehicles to Red indirect fire. Level-1 SAt at this time is higher for CAU-2 than it is for Red regarding CAU-2. At 80 minutes into the run, the Blue unit commander finalizes his assessment of the Red minefield by drawing it on the map, and from that point on, the entire Blue team holds onto the assessment that a minefield is located east of Town 96 and subsequently makes movement decisions according to that interpretation. The SAt curve indicates that the Blue unit’s Level-1 SAt was much better than the Red unit’s for the last 90 minutes of the run—a clear example of how accurate data provided by the system does not necessarily lead to an accurate interpretation of the situation. Furthermore, despite the fact that all Blue operators saw the same elements of data via the COP, a shared interpretation (Level-2 SA) was lacking. Both unit commanders (CAU-1 and CAU-2) saw the same three ATD reports. The CAU-1 commander correctly interpreted them as Red infantry in Town 97. He stated this assessment during a formal commander’s report and again during informal information exchanges with the CAU-2 commander. The CAU-2 commander noted the difference of opinion but maintained his suspicion of a minefield. Working from their individual frames, the two CAU commanders maintained different explanations (Level-2 SA) for the same data. Interestingly, the CAU-1 commander later accepted the assessment of a minefield in that location after the two unmanned reconnaissance vehicles

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Figure 6.13. SAt over time for Blue and Red in Run 2.

had been killed and the minefield was drawn on the map. Thus, when they finally did gain a shared understanding, it was a wrong one. As a result of the misinterpretation, they allocated resources to clear the phantom minefield. The Blue unit had enjoyed good forward momentum to this point, but movement stopped for the mine-clearing work. The tactical impact of the flawed assessment was also felt by CAU-1, as they postponed initiating their movement until the phantom minefield was cleared. The nonexistent minefield contributed to movement decisions for the remainder of the run (e.g., whether to go around the minefield or to navigate the path through the minefield). Had the CSE prompted the commander to consider other explanations for the data, it might have helped both CAUs and the entire Blue force’s mission. Additionally, because the discrepant interpretations were articulated but not examined, an improved CSE system could provide a better means of supporting collaborative comprehension of data (e.g., tools to compare and contrast probabilities associated with different interpretations). In a separate situation, during the mission planning for Run 3, the combined arms team (CAT—higher echelon for the two CAUs) set forth criteria to be met prior to initiating the attack. They included certain preparatory fires to disable Red ADA, sensors sweeps and surveillance at river crossing sites, and a sweep of the sector 20 km forward of the Line of Departure (LD). The CAT commander’s intent was to be as certain as possible that the subordinate CAUs would be safe to move forward. As it turns out, the CAT commander did not feel comfortable allowing the forces to cross the LD until more than an hour past the start of the mission. During that hour, both subordinate

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units were confused as to why they had to wait so long. In one instance, at 25 minutes into the battle, the CAU-2 commander asked for permission to cross the LD with unmanned mortar and reconnaissance vehicles, in order to both range the enemy infantry and to increase the viewing area of the UAVs that were tethered to the reconnaissance vehicles. The higher commander denied this request, and as a result, the subordinate commander decided to use longer-range and more-powerful-than-necessary weapons on the exposed infantry. This in turn confused the higher-echelon command cell—why would they use such weapons, usually reserved for more dangerous targets, on dismounted infantry? The CAT commander further realized that by using these weapons, CAU-2 may have telegraphed his location to the Red force. As a result, there was sudden pressure to cross the LD more quickly, despite the CAT commander’s low level of confidence that the situation was right. Two SA issues are brought to light from this example. First, there was not a common frame among the various Blue elements to describe how the plan should unfold—lack of shared SA. The CAT commander developed a certain vision during planning for how the battlespace should look before crossing the LD and verbally communicated the specific criteria to the CAUs prior to the run. But his mental vision that operationalized those criteria and essentially described what his comfort level had to be before acting was not communicated. Both subordinate units were ready to move out long before the CAT commander’s unknown vision had been achieved. Second, the two echelons (CAT and CAU-2) comprehended the evolving battle differently as a result of different cognitive frames. The CAT commander’s goals were to target enemy ADA and to have sensor coverage of specific areas. The CAU-2 commander’s goals were to target all known enemy entities and to continually extend his sensor coverage forward. The CAU-2 commander’s interpretation of the data in the context of his mission objectives led him to believe that his best action was to move certain assets across the LD, in contrast with the CAT commander’s comprehension of the same data and his perceived best action. This difference in Level-2 SA resulted in the CAT’s refusal to let CAU-2 cross the LD, the subsequent use of more potent weapons to take care of enemy infantry, and confusion at both echelons concerning the other’s actions. The difference led to Level-3 SA (projection) problems as well. The CAT commander did not anticipate that a delayed movement would lead to a suboptimal engagement decision on the part of one of his subordinates. And while the CAU-2 commander may have anticipated that his fires could provide his location to the Red unit, he did not anticipate that higher headquarters would wait so long to approve his crossing the LD. These few examples, and there are many more, help illustrate that having access to the same information does not necessarily ensure common understanding. In the next chapter, we further discuss how collaboration suffered when operators believed that sharing the same display content reduced the requirement for human communication. Situation awareness is built upon information;

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it does not equal information. Turning the available information into SA is a demanding cognitive task, and one that different individuals do differently. Future battle-command tools must help overcome these challenges, perhaps through development of methods for gauging the common understanding, or alerting when a unit’s actions conflict with the commander’s intent. One can envision tailorable and flexible displays that allow operators to view information from perspectives of different goals. Additionally, there are significant training implications as future leaders will have to assimilate and comprehend information more rapidly, while at the same time overcoming the natural tendency to assume that others see things as they do. THE COGNITIVE LOAD The increased availability of information, along with a shift in capabilities and responsibilities of lower echelons, presented our commanders and their staffs with two distinct problems. First, the increased demands of information gathering and processing led, overall, to a high level of cognitive load. Second, this increased cognitive burden is shared very unequally among the functions of cell members. We see four aspects that combine to create a heavy cognitive load on the operators of future battle command. First, there is a major increase in the influence of SA on determining battle outcome, as we discussed earlier. Second, the individual tasks associated with gaining and maintaining SA are among the most difficult and time demanding of all staff duties. Third, the CSE tools that support acquiring and sustaining SA are among the least developed of the command cell’s automated aids. And fourth, the depth of experience, education, and expertise necessary to overcome these challenges is typically lacking at the lower echelons. In this environment, the resulting fight for information is truly a difficult struggle and imposes a heavy cognitive load on the commander and his staff. In the future force environment, we anticipate that the amount of information passed from sensors to commanders and staff will be orders of magnitude greater than we see today. One of the largely unanticipated consequences of this increased load is the disproportionate burden shouldered by leaders at lower echelons. As brigade-like capabilities are given to lower echelons, at the company and battalion levels, commanders at these lower echelons require an advanced skill set and a greater experience level to enable effective decision making. With the least experienced commanders and staffs facing the greater cognitive load, a significant rethinking of manning and training practices may be needed. In particular, future force warfighters will likely need to begin early training to operate effectively in an information-saturated environment. Using the SAt metric during postrun data analysis, we were able to measure and quantify the information available for Level-1 SA. This information was used by a commander to develop and maintain his SAc. The analysis of how different commanders interpret this information to formulate a mental model

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upon which their decisions were based was a critical area of focus for Experiment 7. Here we examine the relationship between the cognitive loads at the two combined arms units (CAU-1 and CAU-2—approximately companylevel units), the CAT (roughly comparable to a battalion), and their higher headquarters (brigade). Figure 6.14 depicts the SAt curves obtained from two selected runs (4 and 7). The curves indicate that the Blue commander won the critical fight for information and was able to obtain a higher level of SAt than his adversary. While this leads one to expect that the Blue team would win the overall battle, we saw situations where this did not occur. In fact, the ability of one side to gain an early information advantage and maintain that advantage throughout the battle is critical. While we have already discussed the relationship between SA and battle outcome, we highlight this result here because it is essential that our future commanders be trained to properly use their future force assets to gain an early information advantage. Additionally, it is critical these commanders understand how they can use future force assets to perform counterreconnaissance against a future enemy in order to limit the information obtained by the enemy early in the battle. The CSE provides a significantly greater quantity of information to lower echelons at a faster pace than occurs with today’s force. To handle the increased amount of information volume, the commander requires an advanced skill set and experience level in order to effectively visualize the battlespace, identify and understand decision points, and create effective CCIRs to support his decisions. A Blue commander described the challenge of being capable of quickly sorting through all of the available information and determining what information is most relevant to his mission at that time: I don’t look at all the imagery, I look at what I see as affecting my plan because there is a lot of superfluous imagery that floats around and you know I don’t have to be aware of every shot nor would I ever want to be aware of every shot. I can’t process that much. I want to focus on 2–3 bits of information at any given time and that helps me maintain a confidence level vis-à-vis my mission success.

Experience and training on how to handle this new cognitive load are essential for a commander to excel in the future environment of networkenabled warfare. The critical combat enabler is not simply the presence of the information, but rather the ability of the commander to understand and process the relevant information and act upon that information quickly. This ability to understand, process, and act quickly must be built into the experiential base of future junior leaders. Often this means overcoming the natural human tendency to want more and more information. Future company-level commanders and staffs need to effectively integrate advanced ISR assets into tactical operations, classify and identify future enemy targets through different types of imagery, prioritize and engage these targets with the correct munitions, and perform effective BDA. More important, and

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Figure 6.14. CAT’s SAt for Runs 4 and 7.

likely more difficult, is the requirement for these future leaders to be proficient in processing vast amounts of information, determining what is relevant and what is not relevant, and making key decisions based on partial information. The solution to helping future tactical commanders is likely to be a complex one that includes training, assignment policies (to better manage experience),

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and technical developments in C2 systems. Increasingly complex simulation environments, such as those seen in this experimental campaign, begin to address some of these needs. EXPERIMENTAL DESIGN AND ANALYSIS Finally, we highlight several lessons concerning the requirements for creating an effective experimental and analytical environment. Our campaign of experiments, with its supporting simulations, scenarios, data-collection system, and investigative mechanisms provided analysts with an uncommon opportunity to explore the relationships between man, machine, and commander’s situation awareness. While the following list is necessarily a partial one, it conveys the demand placed on experimental organizers, simulation programmers, and analysts in order to gain insight into such relations. Experimental Environment • Having multiple, free-play runs of the same mission, with different operator teams across experimental phases, allowed analysts to make observations and draw conclusions that would not be possible from single-run experiments or from more-varied runs. • The dynamic, stressful environment in which decisions could not be exhaustively examined a priori allowed a thorough exploration of the more intuitive decision modes. • Having humans both in the loop and able to freely exercise battle command allowed situation awareness to influence battle outcome. • The representation of multiple potential future capabilities in a structured environment enabled examination of a relatively large number of concerns with a relatively small number of operators. C2 Tools and Simulation • The clear technical centerpiece of these experiments is the CSE. And while certain technical aspects of the CSE system are limited to capabilities of current technologies, the system introduced a significant number of anticipated C4ISR (Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance) enhancements, particularly regarding future sensors and unmanned platforms. These allowed us to study man-machine interactions and assess where cognitive demand might outpace automation support. • The tailorable COP required each operator to set up and to adjust his C2 interface and thus allowed analysts to observe operators’ tendencies concerning the acquisition and management of information. • The high-resolution sensor effects module in the simulation architecture compelled the human staff to develop ISR plans, control the execution of sensor platforms, and interpret sensor images and communications. This allowed analysts to thor-

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oughly explore the cognitive demand of sensor management and its relationship with gaining and maintaining SA. Analysis • The SAt metric to measure information availability, combined with metrics for mission success, permitted analysts to objectively determine who held the tactical advantage (Blue or Red) and to compare those outcomes to information availability. • Availability of dedicated analysts and data collectors during the experiments allowed us to capture the context of command decisions. • Tailorable analyst observation stations, automated logging of all simulation activities, recordings of all communications, and complete transcriptions of a number of runs enabled the detailed analysis that yielded substantive, quantitative insights.

The findings outlined in this chapter derive from relatively early experimentation on network-enabled battle command. Continued and more sophisticated experiments and analysis are required to fully explore the impact of network-enabled warfare on the future commander’s cognition. Still, even these tentative findings are significant in their implication for the design and training of our future force. The importance of situation awareness in network-enabled warfare drives the demands on the human operator who, despite remarkable advances in automation, faces an increased cognitive burden in the future. This in turn calls for further improvements in battle-command support systems and in corresponding training. In addition to training and tools, another age-honored approach to dealing with cognitive challenges is to bring more minds to the task. Two heads are better the one, says the common wisdom, and collaboration helps solve hard problems. It should be particularly true, one could argue, with the modern tools designed to make collaboration more efficient. Our experiments, however, offer a far more nuanced story of collaboration’s role in battle command.

CHAPTER 7

Enabling Collaboration: Realizing the Collaborative Potential of Network-Enabled Command Gary L. Klein, Leonard Adelman, and Alexander Kott

The battle command’s tasks that we discussed in chapter 1—diagnosing, planning, deciding, delegating, synchronizing—are enabled by collaboration between the members of the battle command: decision makers, staffs, and implementers. For our purposes, collaboration is the behaviors people use to participate jointly in a task, especially information-related behaviors such as locating a collaborator, transmitting information, establishing a shared meaning for the information, and orchestrating joint actions. Doctrinal publications (e.g., Field Manual 6.0 2003) highlight the importance of collaboration for fast and effective command and control. Collaboration occurs in multiple forms and across multiple types of command relations. It occurs between members of a staff of an organization; between superiors and subordinates; between peer commanders; between peer staff members of different organizations; between organizations that are explicitly intended to collaborate and those that found a need or an opportunity to collaborate by happenstance. Although the content and means of collaboration have changed throughout the history of warfare, it is nonetheless a timeless component of battle command: Alexander the Great collaborated with his advisors, subordinates, and allies; future warfighters will collaborate too. However, the importance of collaboration in future warfare will be greater than ever before. There are several reasons why theorists of network-enabled warfare stress the growing role of collaboration in future battle command (e.g., Garstka and Alberts 2004). One reason is that wide distribution of information envisioned in the future network-enabled warfare enables a qualitatively greater extent of collaboration between units, even at the lowest tactical echelons and even when

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geographically dispersed. In MDC2 experiments, for example, we observed multiple examples when Combined Arms Units (CAUs) were able to support each other by fires, sensors, and information, and to plan and execute rapidly complex synchronized tasks when separated by up to 50 km. The broad technological support for readily available shared situation awareness (including, for example, the awareness of other units’ resource availability and current tasks) made such collaboration possible. In addition, the growing expectations of the future force’s effectiveness and the great amount of information available to the battle-command practitioners require greater collaboration. MDC2 experiments have demonstrated that a CAU was rarely able to accomplish its demanding missions without an agile sharing of tasks and capabilities with its peer units and higher echelons. Furthermore, with the large volume of information available (albeit often ambiguous in meaning), the commander and staff found it necessary to elicit opinions and interpretations from others within their own command cell and from peers in other units. Collaboration, then, is not merely a convenient option available to the future warfighter; instead, collaboration is a necessity. To survive and to win, the battle command of the future must collaborate, and collaborate effectively. And yet, in our experiments we find that collaboration in a network-enabled environment can also be difficult and even harmful. It can be a cognitively expensive process that takes time and attention away from other tasks, such as monitoring and interpreting the evolving situation. Although multiple programs—the Army Battle Command System, the Command Post of the Future, and the Future Combat System to name a few—have been developing collaboration-oriented technologies, much remains unknown about the complex and nonobvious effects of collaboration. To support collaboration, these programs are developing capabilities like common operational pictures and common information environments that provide facilities for chatting, drawing, and file sharing among participants in collaborative command and control. These developments hold great promise, but realizing their full potential will depend on systems engineering and new skills development that address a number of organizational, social, and cognitive factors in conjunction with technological capabilities. In this chapter, we present a conceptual framework that shows how such systems engineering can be done and how collaborative systems can be evaluated. We use examples from the MDC2 experiments to illustrate the conceptual framework; we then use the framework to explain the experimental findings. THE COLLABORATION EVALUATION FRAMEWORK (CEF) Effective C2 collaboration requires consideration of a number of organizational, cognitive, and social factors. The authors have therefore developed a framework that integrates applicable theories from organizational design,

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social psychology, and cognitive psychology. The following are some of the considerations addressed in the framework: • The different ways that technology can affect a collaborative task process • The different granularity of information that is required at different levels in an organizational hierarchy, which requires collaborators to transform and reinterpret the information that they share • The different informational requirements of each level of situation awareness • The different approaches used for collaboration and the different behaviors they require • The interaction between concepts of operation, the task environment, and technology on performance efficiency • The means to measure the effect of using technology on collaborative performance

The following sections present a detailed description of the CEF in order to provide the reader with the background needed to understand its application to new technological systems being developed for collaborative C2. To that end, the framework will be introduced with explanatory examples from the MDC2 experiment. THREE POINTS OF IMPACT A collaborative system includes people, organizations, processes, and technology. Sets of collaborative behaviors and task transmissions must be supported by a collaborative system in order to achieve either effective sharing of the task load or sharing of information needed to complete the task. The following sections explain the basis for determining what subsets of behaviors and transmissions will require support in a given collaborative environment. Based on that determination, the sufficiency of the elements of a collaborative system can be assessed. Key to designing or evaluating the impact of technology in a collaborative system is understanding that there are actually three points of impact, which are illustrated in Figure 7.1. In any task, technology can facilitate the task process itself, such as automating the detection of targets. However, in a collaborative task, technology can also support collaborative behaviors that facilitate doing the task process jointly, such as notification of others when one operator has a significant piece of information to share. Also in a collaborative task, technology can support task transmissions among the collaborative task participants, such as rationalizations or justifications for a commander’s intent, which are needed to facilitate complete understanding. Considerably more-detailed discussions of collaborative tasks, collaborative behaviors, and task transmissions will be presented in subsequent sections. That will allow us to demonstrate how the combination of all three points of impact ultimately determines task performance and task cost in a collaborative system.

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Figure 7.1. The three impacts of technology on a collaborative task.

Figure 7.1 illustrates the interaction between tasks and technology. The downward arrows represent the constraints of one element on another; the nature of the tasks should define the behaviors and transmissions required in a specific context, and together they should define the nature of the technology. The upward arrows represent that one element is a resource to the other; the nature of the available technology will influence the conduct of the collaborative behaviors and transmissions, and together they will influence the conduct of the collaborative task. Taken together, these influences suggest the mutual entanglement between task, technology, and concept of operations that always exists in a system. So for example, various characteristics of the MDC2 collaborative task require certain types of collaborative behaviors and task transmissions among and between collaborators in their vehicles for costeffective performance. On one hand, the need for these behaviors and transmissions represents requirements for the Command Support Environment (CSE) technology, which if not met, constrain its effectiveness as a collaborative tool. On the other hand, the prototype and future CSE technology can provide resources that when combined with an appropriate concept of operations, permit behaviors and transmissions that were not previously available and thus provide the potential for dramatically improving collaborative task performance. TASK TRANSMISSIONS All collaborative tasks require the transmission of information among participants. This requires the development of external representations of mental constructs, which in an individual task might otherwise remain intuitive and imprecise. A number of typical generic classes of transmissions can be defined: • Situation awareness confirmation—determining that we share situation awareness • Rationalization—establishing internal consistency from data to decision or among levels of situation awareness

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• Justification—establishing links to external values (why should one take an action) • Differentiation—identifying differences • Alternative generation—developing multiple options (e.g., courses of action) from which to choose • Alternative evaluation or selection—ordering options based on some set of criteria or objectives

In addition to these generic classes, there is a great deal of task-specific information that must be transmitted among participants. In a battlespace, this may include the location of entities in the battlespace, the commander’s evaluation of his unit’s state, or the commander’s evaluation of the enemy’s intent. Collaborative systems can facilitate developing these external representations. For example, they can provide structured constructs like data-entry forms or graphical representations like maps and overlays. The CSE provides a number of such constructs, such as on-screen forms that are filled in by the intelligence manager for identifying target entities in the battlespace, and a common operational picture of the battlespace.

HIERARCHICAL LEVEL AND INFORMATION ABSTRACTION In C2, a major consideration that should shape the nature of task transmissions is hierarchical organization. Understanding the need for hierarchy will help clarify the informational requirements of an organization and consequently the role that technology can play. One way that collaboration serves to improve task performance is by reducing the individual cognitive burdens of a task by distributing the load among a group of individuals. There can be both a hierarchical decomposition of the task and a horizontal task allocation. The need for hierarchical organization emerges from the nature of the battlespace. Brehmer (1991) shows that this hierarchy results from three primary informational drivers: • The need for a way of understanding a complex situation • The need for a way of structuring an organization for management • The need for a principle for the control of a complex situation

Organizational success comes from designing a system that brings these three drivers into alignment. Moreover, achieving understanding, management, and control of a complex system requires a hierarchical organization because of two irreducible informational constraints. First, the inescapable cognitive limits of people require a hierarchical decomposition for understanding complex situations and managing complex organizations. Brehmer (1991) states, “To impose a hierarchical system is to introduce

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a series of descriptions of the system, which differ with respect to their level of abstraction. This makes it possible to control the level of complexity in the sense that only a limited number of units have to be considered at each level of the hierarchy.” For example, even a proficient Combined Arms Team (CAT) commander will be at some point cognitively incapable by himself of understanding what is happening in a large battlespace at the level of each individual entity. Yet through a hierarchical decomposition, the CAT commander’s understanding of the battlespace can be simplified to regarding the overall status of his CAUs and the likelihood of their accomplishing their missions, rather than regarding the status of whether specific, individual entities of the CAU have succeeded in crossing the river. Similarly, applying the same hierarchical principle to managing the organizational structure, a CAT commander needs to manage only three staff members and two CAU commanders rather than dozens of individual people and entities. Second, limits on control of any system result from the impossibility of developing models of complex situations that provide sufficient prediction information at every level of abstraction for a given time frame (Flake 1998). For example, we can perfectly predict in September that it will be cold in the winter and warm in the summer; however, due to the complex interactions and nonlinearity in atmospheric phenomena, we can never have enough information, in September, to predict the day (or perhaps even the month) of the last frost for the coming year. This is an informational limit not a cognitive limit: the principles of chaos theory assert that this constraint is inescapable, regardless of measurement accuracy, computer power, or software sophistication. Therefore, although CAU commanders may be able to estimate the likelihood of mission completion for the CAU, they cannot predict the precise future state of every vehicle in the unit in that same time frame. However, to achieve meeting the commander’s mission objectives, individual vehicles and other assets at the more detailed level of abstraction must be controlled in real time compensating for the unpredictable real-time conditions on the ground. The commander’s mission-completion estimate and the control of the assets must happen at different levels of abstraction and in different time frames. Both levels of abstraction are critical to mission success. Because of the difference in conception and information structure at each level, hierarchical organization requires that information, transmitted up from one level to another, needs to be not just aggregated but transformed and reinterpreted. Commanders do not need merely the sum of the casualties taken; they need to know how the distribution of these casualties is going to affect the campaign to secure their mission objective. This systematic transformation of information should be an essential element of designing organization-system integration (Katz and Kahn 1978). The significance of these hierarchical organization considerations is that in a complex battlespace situation, collaboration also needs to be designed hierarchically. When so designed, the scope of function at one level will informationally encompass more than one function at the level below, but at a higher level of abstraction. With information

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appropriate to its level of abstraction, a function at one level is therefore able to facilitate aspects of coordination that are beyond the scope of the individual functions below it (Thompson 1967). Figure 7.2 sketches a hypothetical hierarchical CAU and CAT organization that addresses these considerations. The paramount importance of designing for transformation and reinterpretation of information for such a hypothetical CAU and CAT organization is apparent from the sheer number of transformation and reinterpretation transmission links illustrated in the figure. This kind of organization, when facilitated by technology like the CSE, can support dynamic self-organization at each level of the hierarchy while maintaining a structured relationship between levels of the hierarchy. For example, via the CSE planning tools, the commander provides the intelligence manager with the current mission objectives and timelines. Within that framework, the information manager can manage and control their Intelligence, Surveillance, Reconnaissance (ISR) entities in real-time via the CSE interfaces and can coordinate via voice and chat with other intelligence managers to develop the Battle Damage Assessment (BDA) for use by the maneuver manager and effects manager. To operate effectively at each level, cell members must be provided with information that is at the appropriate level of aggregation and abstraction for the scope of the decisions being made at that level. In addition, the time frame for these decisions must match their scope. Therefore,

Figure 7.2. A hypothetical MDC2 hierarchical organization.

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by facilitating this transformation of information from one level to another, a system can facilitate better synchronization between the levels. In this hypothetical organization, the CAU commander needs to deal with the higher-level abstraction of the CAU itself. For example, the CAU commander should set mission objectives, evaluate enemy intent, evaluate the CAU’s tactical standoff status, and collaborate with other CAU commanders about these issues. Bridging levels of abstraction when collaborating with their own staff (maneuver manager, intelligence manager, effects manager), there is a constant transformation and reinterpretation of information as it is transmitted across hierarchy levels in both directions—commanders explain their intent, while the staff provides information and evaluations in support of the commander’s goal setting. Therefore, the CAU commanders need to have a different conception of the system than their staff, and the commanders need to have information for their decisions visualized in a different way from that of the staff (Brehmer 1991). Ideally, there should be correspondingly different visual displays for describing the system at each level in the hierarchy, and there should be a set of command interfaces appropriate to each level. Some detailed information will be lost at each level of transformation, but new information is gained from the higher-order patterns that become apparent from the more abstract visualizations. In addition, each level of transformation achieves longer-term information stability than the levels below, such that information stability in turn supports longer planning horizons. So, even though detail is lost at higher levels of transformation, new information is also gained as a larger strategic situation awareness is established. When the hierarchical management and control structures of an organization are aligned with the hierarchical structure needed to understand the environment, and technology supports the informational needs at each level of the organization, then localized functions (whether a commander’s or a battle manager’s) can self-organize and adapt to dynamic conditions through monitoring the environment at their level of abstraction. They then can plan responses at the corresponding temporal, physical, and organizational level to any changes that arise (Rasmussen, Pejtersen, and Goodstein 1994; Simon 1996; Thompson 1967). Such an alignment can be seen between military organizations and the battlespace environment (Brehmer 1991). The challenge is reengineering that alignment for new concepts of operation. Figure 7.2 in fact illustrates a very conventional organization for echelons at brigade and above today. However, the MDC2 experiments suggest how technology such as the CSE could allow visiting this structure at lower echelons such as the CAT and CAU. COLLABORATION AND LEVELS OF SITUATION AWARENESS In addition to facilitating sharing the task load as described above, collaboration can improve task performance by facilitating information sharing to

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enable a level of situation awareness for the group of participants, which may not be possible for any single member. Therefore, a second major consideration for task transmissions is enabling such awareness. As was discussed in chapter 4, Endsley (1995) has identified three levels of situation awareness (SA). • Level-1 SA is the perception of information. For example, it is having the awareness of where different battlespace objects (enemy and friendly) are located in the battlespace at different times. • Level-2 SA is the comprehension of meaning. It addresses what the Level-1 situation awareness means currently; for example, what actions the enemy is currently capable of performing. • Level-3 SA is the projection of the situation over time. It is the awareness of what could happen in the future under various contingencies.

There are different collaboration implications for these three levels of situation awareness. Level-1 is the basic awareness of available information. It answers the question, “What information do we have about the enemy?” or “Where are the friendly forces?” It is information that can be placed in a database (or pooled) for use by others because it has a global frame of reference that is not tied to the information recipient’s situation. In Figure 7.2, a hypothetical example is the development by the intelligence manager of a common BDA picture that can be drawn upon by both the maneuver manager and effects manager to support developing their own situation-specific Level-2 and Level-3 SA. In fact, Level-2 and Level-3 SA require that Level-1 information be interpreted to meet the information recipient’s needs. Moreover, when information is shared across organizational levels, up the chain of command, the lower echelon’s Level-2/3 SA must be transformed and reinterpreted into the higher echelon’s Level-1 SA. For example, the CAU-1 commander’s assessment of CAU-1’s combat effectiveness, and the status of their plan execution, is Level-1 SA for the CAT commander’s assessment of the CAT’s mission status. Therefore, providing the same common operational picture at the same level of abstraction, across levels of an organizational hierarchy, can be problematic because the same information is not equally useful at each level—the form of the information and its level of abstraction should be dictated by the information needs of the recipient. Sharing the same entity-level information through the CSE can enable the operators at the same level of abstraction (e.g., intelligence manager) to efficiently backstop and compensate for each other regarding entity-level actions (Level-1 SA) like identifying targets and directing fires. However, employing that same view across different levels of abstraction (CAT or CAU commander) could result in a lack of missionoriented (Level-2/3 SA) command and control. These information-sharing and load-sharing aspects of collaboration can interact with each other to impact performance. For example, the effectiveness of information sharing can interact with the hierarchical decomposition:

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as was seen in one MDC2 case where none of the operators developed a more mission-oriented view of the situation, and the larger picture (e.g., likelihood of mission success) may not have been evaluated even though all of the needed information existed within the group. In addition, the information requirements for the levels of situation awareness interact with characteristics of the task process. The nature of this interaction can be understood after a description of those characteristics is presented in the following sections. TYPES OF COORDINATION AND COLLABORATIVE BEHAVIORS Within the collaboration evaluation framework, the key defining dimension of the collaborative task process is the type of coordination employed. Whereas collaboration refers to the general situation where people work together to achieve mutual goals, coordination refers to the specific approaches that people use to work together. As will be subsequently described, the type of coordination employed defines the collaborative behaviors that will be required in a given task context and therefore the ultimate cost of coordination. In the CEF, all of the other dimensions defined by Thompson (1967) serve to determine the constraints on the type of coordination that is possible. Thompson (1967) defines three types of coordination: standardized, planned, and mutual adjustment. Under standardization, there are established rules or routines for how people should coordinate their activity. As with traffic rules, standardization improves performance per unit cost by reducing coordination costs in both financial and cognitive terms because rules remove many uncertainties about how people should coordinate their behaviors. Standardization functions best in stable task environments. For example, in the MDC2 experiment, when an intelligence manager identifies a target, they enter its description into the target database, which in turn is used by the effects manager for targets to fire upon. This routine relieves the need for the intelligence manager to communicate overtly with the effects manager. In fact, this allows them to reserve such overt communication for critical targets that are an immediate threat to the CAU, which makes such nonroutine communications more alarming and effective. In some task environments, preestablished global rules and routines are not feasible. However, team members can still plan their coordination processes based on more immediate circumstances. For example, in the battlespace, each mission can be planned: waypoints set, areas of interest defined, and surveillance strategies determined. Through planning, different units have been provided with critical information for how to coordinate with each other, thereby reducing the requirements (and costs) of subsequent coordination through discussion as long as the plan remains in effect. When the task environment does not lend itself to standardization or even planning, team members must coordinate through continuous mutual

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adjustment to each other’s activities. This requires constant communication to make sure that coordination requirements (and expectations) are clear and that activities are performed with minimal confusion and maximum benefit. As a result, mutual adjustment is the most costly form of coordination. This costly coordination is required when the task environment is very dynamic and unpredictable. For example, once a mission is under way, an intelligent adversary will present situations that could not be anticipated. Then, operators will need to mutually adjust to coordinate their responses. The types of coordination defined by Thompson (1967) delimit the types of behaviors people need to carry out a collaborative task. Clark (1996) describes the behaviors that people engage in to carry out joint actions, such as a conversation. Extrapolating from Clark, at least eight collaborative behaviors can be identified: • Connection—locating with whom to collaborate and how to contact them • Transmission—sending a message • Notification—alerting the intended party of an incoming transmission • Identification—designating the sender, receiver, and subject of a transmission • Common ground preservation—establishing and maintaining a shared context and meanings in transmissions • Confirmation—notifying the sender of a transmission that it has been received • Synchronization—orchestrating actions to facilitate joint action • Election—group process of selecting among alternatives

Each type of coordination requires a different subset of collaborative behaviors as shown in Table 7.1. Mutual adjustment generally requires all of the collaborative behaviors because of its ad hoc nature. However, the predefined rules and routines of standardized coordination take care of all of the behaviors but identification and transmission. This difference highlights why mutual adjustment is so relatively costly—because all of these behaviors involve communication and time. Consider a situation, when CAU-2 needs CAU-1 to fire upon a target that is in CAU-1’s range, but on the border of CAU-2’s targeting area, and is attacking CAU-2’s assets. If the CAUs coordinate through mutual adjustment, CAU-2 will need to notify CAU-1, transmit and identify the nature of the request, establish common ground regarding the location of the target and the action to be taken, confirm that the information has been received, and synchronize their own actions with CAU-1’s destruction of the target. In contrast, they could standardize their coordination using a so-called cursor-on-target approach. Cursor-on-target could allow all the necessary information and targeting orders to flow digitally to the effects manager as needed when the requesting staff put their computer cursor over the target and click to approve. Once a target is approved, transmission, identification, and

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Battle of Cognition Table 7.1 Behaviors Required for Mutual Adjustment (M), Planned (P) and Standardized (S) Coordination Collaborative

M

P

S

Connection

□

□

□

Notification

□

□

□

Identification

□

□

□

Transmission

□

□

□

Common ground preservation

□

□

□

Confirmation

□

□

□

Synchronization

□

□

□

Election

□

□

□

all other collaborative behaviors are handled by the automation. Therefore, technology (in this case a shared database) actually can facilitate moving from the more numerous (and therefore more costly) mutual adjustment behaviors to less expensive (and faster) standardization. The drive for such collaborative efficiency often results in spontaneous, innovative user adaptations of technology. This in fact occurred in the midst of the MDC2 experiment when the participants spontaneously devised their own variation of cursor-on-target: they standardized on using a circle-drawing function (normally used in planning) to indicate targets by circling them during mission execution. Although the linkage may not be as strong, because of the nature of task processes and level of interdependence, the type of coordination among collaborators affects the type of requisite task transmissions as well as collaborative behaviors. This is noticeable when replanning occurs during execution. For example, when the CAT and CAU commanders are executing an operation that is not meeting the intent of higher headquarters, and the commanders have previously identified branches and sequels, then SA confirmation may be the only task transmission required; there is no need for alternative generation and evaluation because of the standardized alternatives they developed during prior planning. However, if branches and sequels were not developed, then the collaborators must perform all of the generic task transmissions to reach a new course of action, unless the new action is obvious (e.g., via recognition-primed decision making; Klein [1999]). They first should confirm their SA with respect to the current situation and possible futures, exchanging rationalizations connecting current observables (data) to predictions about future states. Then, they should generate alternative courses of action, evaluate them through differentiation and justification, and then select one for implementation. Performing these

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task transmissions typically requires coordination via a scheduled planning session at the mission-oriented level of abstraction. MDC2 C2Vs and CSE provide the potential of engaging in such a session, even with mutual adjustment, while on the move on the battlefield. Such mobile, agile C2 decision making could save time and keep pressure on the enemy, but the collaborative technology, and the concept of operations for its use, must support all of the task transmissions required by the collaborative decision-making process. Based on the relationship between the type of coordination and the consequently required collaboration behaviors, the framework clarifies that the effectiveness of different tools to support coordination depends upon (1) the type of coordination used to perform a task and (2) how many of that type’s required collaboration behaviors are supported by the tools. In addition to how many are supported, how well that support is implemented in terms of human factors and cognitive usability must be considered. Finally, the framework clarifies how a new entanglement of task, processes, organization, and technology can facilitate moving to a new task concept of operation with less expensive coordination. However, the task characteristics of a collaborative environment can constrain such movement. These characteristics will be considered next. TYPES OF TASK PROCESSES Thompson identified three general types of task processes (which he called technologies): long linked, mediating, and intensive (Thompson 1967). Long-linked processes require the completion of various task activities over time, like an assembly line. The intelligence process can be considered long linked: collection tasking, surveillance, analysis, and dissemination of a finished product. Mediating processes link together individuals or groups that want to be interdependent for their mutual benefit, as a broker mediates between those who buy and sell stock. Effects management is a mediation process: connecting targets, given their characteristics, with appropriate weapons for attack (albeit not for mutual benefit in this case). In Figure 7.2, the BDA (a pooled resource) facilitates mediation: connecting the intelligence products with the maneuver manager and effects manager that use them. Intensive task processes are directed toward changing an object, where the specific actions taken depend on feedback from the object. Military operations in general are intensive processes, where the next operation against a target is dependent on the effects of earlier operations. A relationship between the types of task processes and types of coordination (long linked and standardization, intensive and mutual adjustment) is apparent. However, this relationship is not deterministic. The other organizational and environmental dimensions must be considered as well. In particular, technology can be used to change this relationship too. The cursor-on-target example above showed how at least one phase of an intensive targeting task could be moved from mutual adjustment to standardized coordination.

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TYPE OF INTERDEPENDENCE Thompson (1967) identified three general types of interdependence among unit personnel and organizational units: pooled, sequential, and reciprocal. In pooled interdependence, each team member or unit provides a discrete contribution to the whole by collating (or pooling) their obtained information and knowledge. In the MDC2 task, individual intelligence managers contributing to the shared CSE database is an example of pooled interdependence. Although the final product depends on the activities of each intelligence manager, the individual analysts’ work is not necessarily dependent on each other’s activities. However, their organization as a group is critical to ensure that each intelligence manager’s surveillance of part of the battlefield contributes to a complete picture of the whole battlespace. In sequential interdependence, the product of one unit (or person) is dependent upon the output of another. In MDC2, the intelligence managers and effects managers exhibit a sequential interdependence: the intelligence manager identifies targets, the effects manager directs fires upon the targets, the intelligence manager does BDA, and the sequence repeats. Finally, in reciprocal interdependence, units pose critical contingencies for each other that have to be resolved before taking action. Operations and logistics often have a reciprocal interdependence. Whether or not different operations can be undertaken depends on the availability of certain resources, and, in turn, the availability of those resources depends on previous and planned operations. Therefore, operations and logistics pose critical contingencies for each other that have to be addressed reciprocally during planning.

TASK ENVIRONMENT An organization or task process exists within a context, its task environment. Thompson (1967) identifies two dimensions that are critical to the way an organization is structured. The first is the stability of the environment—how quickly the elements in the environment change. Our discussion of situation awareness suggests that this dynamism can be considered at the three different levels: not only how quickly the battlespace entities change (Level-1), but also how sensitive the situation (Level-2) is to those changes, and how sensitive the projected future (Level-3) is to changes in the situation. The second dimension is heterogeneity—how many different kinds of entities (and by analogy situations and futures) does the organization need to deal with? Thompson proposes that in order to reduce uncertainty to manageable levels, organizations should divide their environment into subdivisions that are as stable and homogenous as possible, and they should create separate organization units (e.g., different CAUs) to deal with each subdivision. Collaborative technology can permit teams to respond faster and, thereby, deal with more dynamic situations than previously possible (e.g.,

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more mobile targets). It can also facilitate collaboration among more units addressing different subdivisions of the environment—this enhances organizational management and expands the scope of battlespace understanding and control. CONCEPT OF OPERATIONS Ultimately, the performance achieved for a given cost of coordination, communication, and time depends upon how well the collaborative task, the concept of operations (CONOPS), and the technology are fitted together. Clearly, the best system (represented by the upper-right box in Figure 7.3) is one where the CONOPS is appropriate for the collaborative task and the CONOPS takes full advantage of technology to achieve the lowest cost coordination possible given the constraints imposed by the task’s various dimensions as described in the CEF. However, in developing new technology for a task, we often find ourselves near the lower-right box. This is the case when new technology simply replicates the function of old technology using the existing CONOPS, without taking advantage of the new technology’s potential to change the CONOPS and reduce costs (or improve performance). When this happens, we often see people develop work-arounds that move toward a more effective CONOPS, even if it is outside the technology’s original design (the middle box). Even though the work-around results in a worse fit for the original design of the technology, the improvement in fit with the task yields improved performance. Often, collaborative technology is designed to permit the kind of coordination possible in face-to-face groups. Video and audio teleconferencing are examples of this kind of collaborative technology. People in different geographic regions can now use this technology to coordinate in the same way as face-to-face groups, typically via mutual adjustment and planned coordination. Web-based (or inspired) collaborative technology offers window and file-sharing capabilities, instant messaging (or chat), and even bulletin boards for electronic drawing. However, as Figure 7.4 illustrates, the biggest gains in more cost-effectively performing collaborative tasks is using technology to move to a new task concept of operations with less expensive coordination. As discussed earlier, standardization requires fewer collaborative behaviors and task transmissions than planned and mutual adjustment coordination. If technology makes task processes less intensive and thereby permits them to deal with more dynamic environments and heterogeneous units than previously possible, then the cost effectiveness of the technology can jump qualitatively rather than incrementally by providing the same or better levels of performance for substantially less communication. This is the long-term goal of the MDC2 program of evaluating collaborative environments like CSE. Finally, underlying all collaborative performance outcomes is the quality of training. The effectiveness of distributing the cognitive load and sharing

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Figure 7.3. Performance per cost is a function of CONOPS task-tool fit.

situation awareness is heavily dependent on the competency of the individuals to execute effectively their portion of the collaborative task. Whether interdependence is pooled, sequential, or reciprocal, the performance of the whole team is impacted when one member cannot perform his or her allotted task effectively. Performing that task effectively is obviously a product of task knowledge (command, operations, intelligence, logistics, and fire support), but it is also a product of tool “usage.” It is common for personnel to learn only the minimum necessary features for using a tool: team members are trained on

Figure 7.4. Hypothetical performance and cost effectiveness of collaborative technology as a function of type of coordination.

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how to operate a tool’s buttons and menus. Establishing proper usage requires defining an effective concept of operations that uses the tools to accomplish the task efficiently. Therefore, a technology can appear to be ineffective, even if it inherently supports required collaborative behaviors and task transmissions, because team members do not know how best to use the tool within the task context. APPLYING THE COLLABORATION EVALUATION FRAMEWORK The value of the CEF is illustrated by applying it to the technology, concepts of operation, and tasks observed in MDC2 Experiments 6 and 7. Experiment 6 was the first experiment utilizing multiple (simulated) MDC2 vehicles for a CAT with two subordinate CAUs. As described earlier, each vehicle was equipped with a CSE containing a COP with a synchronized view available to ensure all recipients saw a presenter’s screen, full voice and digital-chat communications capabilities, and various planning tools. We can characterize the MDC2 collaborative task as follows using the CEF: • A classic example of a battlefield intensive task process. • Typically, there was a reciprocal interdependence among MDC2 personnel and the entities they control (e.g., weapons and intelligence-gathering platforms) because the units pose critical contingencies for each other that had to be resolved successfully in order to coordinate successfully. • The MDC2 experiment’s task environment was designed to be dynamic. In addition, the environment was heterogeneous because there were many different kinds of friendly and enemy entities (and by analogy, situations and futures) that the MDC2 personnel have to deal with quickly. • The principal form of coordination observed among these reciprocally interdependent units performing intensive tasks in dynamic environments was mutual adjustment to each other’s activities. The needed constant communication was facilitated by the CSE.

Given that Experiment 6 was the first exploration of CSE with multiple C2 vehicles, and that CONOPS development was exploratory, it was not unexpected that we would find ourselves in the central and lower-right regions of Figure 7.3. This is the region of the figure illustrating improvements in task performance, but where additional performance can be achieved by taking advantage of the collaborative technology’s potential to better fit the technology and CONOPS to the task. Therefore, the following observations can provide a basis for developing concepts of operation that are more effective, identifying requirements for training and identifying requirements for the CSE and similar innovative C2 systems. Developments in each of these areas should improve effectiveness.

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IMPACT ON MISSION-ORIENTED THINKING Perhaps the paramount illustration of the collaboration concepts presented above was how the full potential of the CSE was realized when commanders adopted a hierarchical organization in Experiment 7 (actively maintaining a mission-command perspective) versus a flat organization in Experiment 6. The battlefield visualization provided by the CSE technology enabled the CAT and CAU commanders’ focus on individual entities. Moreover, an entity focus can have positive consequences, as is illustrated in the cases below. However, there is a double bind in this entity-level engagement. First, engagement at this entity-level level of abstraction creates a type of cognitive tunnel vision—a situation colloquially referred to as “not being able to see the forest for the trees.” Second, in the battlespace, the entity-level tempo became very high whenever the situation diverged from the battle plan; even though at such a time a mission-command perspective would be most important, engaged as they were at the entity level, the commanders sometimes did not have time for mission assessments or real-time replanning. As opposed to the hypothetical hierarchical organization Figure 7.2, a flat organization (all at the same level of abstraction) of Figure 7.5 was often observed in Experiment 6. In this organization, the CAT commander regularly discussed the operational battle damage assessment with the intelligence manager and directed the intelligence manager to direct the effects manager on what specific targets to fire upon. There were often times when the CAT commander took direct control of weapons to fire upon targets or directly maneuvered subordinate units through their maneuver interface. As can be seen in the figure, we observed the CAT effects manager getting fire requests from their effects manager counterparts in CAU-1 and CAU-2, who in turn were being directed by their commanders. It was not clear to what extent the CAT commander

Figure 7.5. Often observed flat task organization. See Appendix for explanation of abbreviations.

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was aware of this tasking of his own effects manager coming from other (subordinate) units. The process trace of the loss of MCS1–2 during one experiment (Run 8) is shown in Table 7.2. This example illustrates how the CAU-1 commander becomes heavily engaged at the entity level. The ultimate result is the loss of both of his MCSs and consequently his ability to complete his mission— although, revealingly, he never seems to make (or at least did not articulate) that mission-command assessment. At the beginning of this trace, the CAU-1 commander is indeed involved in directing fires on specific targets. In fact, he is engaged by CAU-2’s commander in mutual-adjustment coordination to fire on one of CAU-2’s targets. For whatever reason, the commanders (and the rest of the command cell) did not use the CSE grid-coordinate or latitude-longitude coordinate systems. Without an absolute coordinate system in use, difficulty in establishing common ground leads to confusion over just where this target is. Because of this, at least 60 seconds are spent on this coordination, which is hypothetically at too low a level of abstraction for either commander and should have been performed by their maneuver managers or effects managers. In the meantime, an ATD pops up in front of MCS1–2 and kills it at 13:50:21. Because of a number of interface considerations, the CAU-1 commander does not become aware of this situation until over three minutes later, when his attention is drawn to it by the CAT commander! Even then, he does not fully understand that this is a firepower-kill and mobility-kill until 13:59. He does not appear to assess the importance of this loss with respect to accomplishing his mission; even when his second MCS is killed, he continues to insist that he can take his objective, clearly showing a lack of Level-2 and Level-3 SA. We see further evidence of the CAU-1 commander’s lack of missionoriented assessment in his report to the CAT commander at 13:56. In that report, with inadequate surveillance resources, the CAU-1 commander erroneously concludes there are no enemy forces counterattacking. Moreover, having lost most of his surveillance assets, he does not direct his intelligence manager to develop and execute plans to compensate to provide the CAU-1 commander with better situation awareness of the enemy’s status. The intelligence manager could have better deployed the remaining class-2 UAVs, used the CSE’s “range fans” to better visualize the UAVs’ capabilities and limits, or collaborated with other intelligence managers for more complete UAV surveillance coverage. Without direction from the CAU-1 commander or taking initiative on his own, the intelligence manager does not provide the needed mission-oriented situation awareness, and CAU-1 is destroyed. However, in Experiment 7, the commanders intentionally tried to maintain a more hierarchical organization. They took better advantage of the CSE capabilities to maintain a mission perspective and consequently were more successful in maintaining their strategic standoff and their survival.

Table 7.2 Process Trace of the Event When CAU-1 Lost Its MCSs. See Appendix for Explanation of Abbreviations Time

Speaker

Recipient

Message

Comments

13:46:00

CAU1 CDR

CAU1 BSM

CAU1 CDR slowing MCS down to let infantry go ahead.

Appropriate level of abstraction.

13:47:00

CAU1 CDR

CAU1 EM

Fire at two target sets.

Too detailed level for CAU1 CDR; should be CAU1 BSM?

13:47:00

CAU2 CDR

CAU1 CDR

Can you fire infantry near my target P20?

Discussion ensues about exactly which target that is. Without clear markers, there again is confusion about location. CAU2 CDR highlights the target (which does show up on CAU1 CDR display—but CAU1 CDR does not appear to see it because he asks for clarification). CAU2 CDR? Says it’s “south of 27”—but 27 is far north of the target.

13:48:00

CAU1 EM

CAU2 CDR

We’re shooting those.

Not clear they have identified the same target.

13:49:46 13:49:47

ATD pops up in front of northern MCS1-2 on CAT right screen. NLOS CAU2 fires PAM at ATD Unknown.

186

13:50:00

CAU1 CDR

Where the hell is 17?

13:50:21

MCS fire/mobility kill—indicated in simulation kill data base.

13:50:25

MCS fire/mobility kill displayed on CAT’s screen as a blue X over MCS1-2. X is almost same color as MCS1-2, and the ATD at 13:50:37 partially covers it. The “tab” for MCS1-2 disappears from CAU1’s Weapons dialogue box.

13:50:37

ATD pops up in front of northern MCS on CAU1’s screen.

13:50:58

CAU1 CDR

13:51:58

CAU1 CDR

CAU1 BSM

ATD directly front of MCS. Halt him for no more than 40 seconds.

To let infantry go ahead. MCS1-2 is actually already dead! CAU1 BSM should be able to know this at least from the movement dialogue—but unlike the Weapons dialogue, it doesn’t appear to be reflected there.

13:53:30

CAT CDR

CAU1 CDR

Something happened to your MCS.

MCS is still halted! What happened can’t be seen on CAU1 CDR’s display as noted above. It is unclear why CAT CDR must ask “what happened?”

13:53:48

CAU1 CDR

CAT CDR

I think it was a firepower kill.

It’s removal from his Weapons dialogue tells him that it’s a firepower kill. Blue “FIRE/MOBILITY . . .”? kill color is the same as Tiger’s blue color, making it hard to see.

(continued) 187

Table 7.2 Process Trace of the Event When CAU-1 Lost Its MCSs. See Appendix for Explanation of Abbreviations (continued) Time

Speaker

Recipient

Message

13:56:00

CAU1 CDR

CAT CDR

“Mobility kill” to one MCS. Not seeing CAU1 CDR told CAT CDR that is now a “mobility” additional resistance. kill? See 13:59—he may have “misreported” the status. He is “red on eyes” (loss 1 class 2 and 2 class 3 UAVs)—but not “seeing” any counterattacking enemy forces advancing! Representation of surveillance quality is missing—“if I don’t see it then it is obviously not there.” Does have class 1’s—maybe basing on their limited view, without recognizing their range (w/o range fan displayed).

13:59:00

CAU1 CDR

Appears that he is a mobility kill and firepower kill.

Tries to move him and can’t.

14:02:00

CAU1 CDR

Out of comms with MCS1-1.

As indicated by simulation system—red triangle.

14:02:33

CAU1 CDR

Reports out of comms with “Lead” MCS.

CAT CDR at first thinks he is talking about MCS1-2.

CAT CDR

Comments

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HIGH COGNITIVE COSTS OF MUTUAL ADJUSTMENTS The COP window is a central component of digital C2 systems. The CEF distinguishes the COP’s impact on sharing situation awareness and the COP’s support for collaborative behaviors and for task transmissions. The MDC2 experiment illustrated these three points of impact. For example, the shared COP allowed the CAU-2 commander to point out a target to the CAU-1 commander (ignoring for the moment that this interaction should be handled by their staffs). However, the CAU-1 commander did not see the CAU-2 commander’s highlighting of the target—he appeared to be unaware that the CAU-2 commander had in fact even made a change to the COP by introducing this highlight; instead he focused on a protracted verbal communication to establish common ground on the location of the target. This kind of verbal exchange to establish the location of entities was quite typical. There a number of ways that the target highlighting could have been better notified (e.g., flashing a more contrasting highlight symbol). This cursor-on-target approach also would have eliminated the need for the verbal discussion of location, leaving more time to discuss perhaps the mission-level rationale and justification for the requested action. In fact, although the COP did provide a graphical knowledge repository for Level-1 situation awareness, collaboration about that knowledge mostly occurred over the verbal communications channel. For example, when new information was posted to the COP that was of significance to another party, the collaborative behaviors of notification and confirmation were typically done over voice communications. In fact, voice communications were used for all of the collaborative behaviors and the task transmissions. This raises the issue regarding when verbal communication is an effective communications mode. Verbal communication by its nature usually requires some mutual adjustment, although organizations like the military often try to standardize terminology and use standardized phrases to enhance establishing and maintaining common ground with minimum adjustment. Verbal communication is also by its nature ephemeral, unless it is recorded, relying on human perception and memory to understand and maintain a record of what was said. It is also sequential, which is known to put a burden on human memory when the sequence is long, particularly when the information is abstract. However, verbal communication of complex abstract ideas can be quickly produced relative to writing text of the same content. Nevertheless, other forms of computerrepresentable gestures (such as circling a desired target on a shared screen) when feasible can be quick, unambiguous, and persistent. Therefore, verbal communication would be less desirable when information can be easily transmitted and persistently recorded as, for example, an overlay on a map. On the other hand, verbally communicating a short abstract rationale for an order might be a most efficient approach. This should not preclude looking for other efficient approaches. For example, if a reasonable

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number of standardized rationales can be predetermined, communication could require only selecting the desired rationale from a list. Programs such as MDC2 can facilitate trying alternative designs to examine the trade-offs between various modes of supporting collaboration in a C2 context. Given the prevalence of the COP concept in C2 systems, there seems to be a general belief that having a shared COP should inherently lead to a common understanding of each other’s state, rationale, and justification for actions. A number of examples from the MDC2 experiment illustrate the need for additional rationale and justifications to establish and maintain common ground. For example, in one battle, a FRAGO required CAU-1 to halt its attack on an objective and begin a flanking movement. The CAT commander was observed regularly urging CAU-1 to hurry. Even though the movement of CAU-1’s entities and the context (terrain, vulnerability of positions) of CAU-1’s movement were visible to the CAT commander, the CAU-1 commander had to explain that the rate of his movement was due to a need for ground surveillance of enemy positions in the forest area that ran parallel to his path. Thus, merely sharing a common Level-1 SA did not lead to a common Level-2 SA (i.e., establishing common ground regarding the rationale and justification for the observed situation). The CSE did not provide automation support for commander’s taskspecific transmissions, such as the Red Assessment, Blue Combat Effectiveness Assessment, and the Assessment of Action in Relation to the Tactical Plan. All of these were delivered verbally. However, some of the necessary knowledge is beginning to be incorporated into the CSE to provide the Level-1 SA for making these assessments. For example, the CSE has an Automated Guidance Matrix to show weapon status, ISR fans for visualizing sensor coverage, and an animation-based planning tool to illustrate movement and provide traveltime estimates. In time, the CSE (and future collaborative C2 technology in general) should be able to alert personnel of the situation awareness Level-2 and Level-3 (i.e., higher order) implications of (selective) battlespace changes for their current (and possible future) course of action and support the necessary task transmissions required to evaluate them in addition to the COP. And the greater the extent to which the concept of operations for collaborative technology can be moved from the more costly form of mutual adjustment to more standardized forms, the more dynamic and heterogeneous the environment it will be able to deal with effectively. COLLABORATION IN A DISRUPTED COMMAND Yet even relatively imperfect collaboration support can have remarkably positive impact. Evidence of effective collaboration becomes particularly salient under extreme conditions, such as a severe disruption of the command structure. Experiment 7 provided a unique opportunity to observe the effects caused by the loss of a C2V and its entire command cell due to enemy action. We were able to analyze how such a major loss and the subsequent command

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succession affected the situation awareness, collaboration, and, ultimately, the decision-making capability of the remaining command cells. During the eight experimental runs, there were two episodes when the enemy was able to destroy a single C2V and the command cell riding in the C2V. The CAU-2 lost its C2V in Run 5, and the C2V of CAT was lost in Run 7. When a C2V was destroyed during an experimental run, the commander and staff occupying the destroyed C2V lost the capability to communicate via radio and lost the use of the CSE and the capability to control assets. Then the experiment control cell ordered the “destroyed” command-cell members to exit their command stations without telling the remaining command cells about their destruction. Shortly thereafter, the CSE interface viewed by all remaining command cells displayed a “lost communications” symbol on the icon of the destroyed C2V and on icons of all assets under the control of that C2V. However, the same symbol could also appear on the interface for other reasons, such as temporary loss of communications due to heavy network traffic, and thus did not always signify a battle loss of a platform. In short, like in a real-world battle, the surviving command cells did not have any clear Level-2 SA that a C2V was destroyed. It was the survivors’ responsibility to establish that Level-2 SA and then to mark the C2V as destroyed via the CSE interface. To do that, the surviving command cells would need corroborating Level-1 SA, such as observing the enemy artillery fire coming at the destroyed C2V. Without such corroborating information, it could take them a fairly long time to recognize the loss. Instead, they were likely to assume a communications problem. Even after they made repeated attempts to reestablish radio communications with the unresponsive C2V, the explanation with the highest baseline probability was a technical problem because such problems were more common. In the face of realistic ambiguities in Level-1 SA, establishing an accurate Level-2 SA would be a function of experience and training. When a C2V remained silent for an unreasonably long time, this became sufficient corroboration for a commander or staff member at one of the remaining cells to conclude that the C2V was destroyed and mark it as such through the CSE interface. At this point, the CSE offered the commander and staff at the next higher echelon a standardized process to reassign assets that were previously under the control of the destroyed C2V, thereby eliminating the need for any mutual adjustment to achieve this. Any of the assets could be assigned to any of the remaining command cells. The process of command succession was completed when all assets were reassigned and communications reestablished with any available manned platforms. A key challenge, of course, was to do all this very quickly. Once a C2V was lost, its assets became largely disoriented, idle, and highly vulnerable to enemy fires. It was important that the remaining cells quickly recognized the loss of the C2V, reassigned the assets, and reevaluated the mission to determine the impact of the C2V loss and the necessary mission changes, if any. Clearly, much depended on prior experience with such episodes and on how quickly the survivors recognized the loss (Table 7.3).

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Table 7.3 The Timelines Observed in Two Episodes of Command Succession Time to Recognize C2V Is Destroyed and Mark It Dead in the BCSE

Time to Reassign the Assets Previously Controlled by Lost C2V

Total Time for Command Succession

Run 5

8 minutes, 45 seconds 15 minutes, 5 seconds 23 minutes, 50 seconds

Run 7

1 minutes, 30 seconds 5 minutes, 13 seconds 6 minutes, 43 seconds

Average

5 minutes, 8 seconds

10 minutes, 9 seconds 15 minutes, 17 seconds

Run 5 was the first time the command cells had experienced the loss of a C2V and exercised the command succession procedures, and the tardy timeline reflects the inexperience and the attending confusion. When the same command cells experienced the loss of a C2V again in Run 7, they improved their timing considerably. Moreover, the average times shown indicate actually a very short period required to accomplish these tasks with the CSE, as compared to a conventional, non-network-enabled environment. As opposed to a conventional environment, the CSE interface clearly improved Level-1 SA by providing visual stimulus such as “loss of communications” symbols, as well as indicators for artillery and external fire impacts detected by sensors. These assisted the commanders and staff in determining when a C2V may have taken fire and reduced the time required to recognize the loss of a C2V. Furthermore, once the C2V was marked destroyed, that information was distributed out to the force in real time, and the CSE interface provided a standardized command succession tool that allowed the commander to quickly view and reassign all assets that were previously under the control of the destroyed C2V. The commander or staff responsible for reassigning the assets could also use the CSE interface to view the status of all units as well as the mission and tasks of each unit. They could see which units needed additional assets and which units might not be able to handle the additional workload. With this information, the commander or staff could make effective decisions regarding the reassignment of assets. In addition, the network allowed any commander or staff member to control any asset in the battlespace, if so assigned. There was no need for them to relocate or physically be in the same area as the assets that were under their control. It is instructive to consider the dynamic changes in the volume of collaborations associated with the command succession events. Table 7.4 shows the average collaborations index across all the experimental runs. Interestingly, the volume of collaborations for the two runs where a C2V was lost (i.e., Runs 5 and 7) did not show a significant increase or decrease versus the average for all runs. In fact, the collaborations for each run fall within the 95 percent confidence interval for the overall average of all runs. Given that in these two

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Table 7.4 Impact of a Command-Cell Destruction on Average Volume of Collaboration Activity Collaboration Avg. Run 1

Before C2V loss

After C2V loss

Collaborations not recorded

Run 2

0.2569"

Run 3

0.3175"

Run 4

0.3550"

Run 5

0.3924"

Run 6

0.5240"

Run 7

0.3204"

Run 8

0.4412"

Avg.

0.3725"

95% Conf. (±)

0.082268801"

0.4178

0.3854

0.3314

0.3081

runs, one-third of the human command resources have been lost, one might have expected the number of collaborations to decrease by perhaps one-third or more since there were fewer people to engage in collaborations. Instead, the collaboration statistic before and after the loss of the C2V in Runs 5 and 7 shows only a minor decrease in collaborations that is not statistically significant. Apparently, we see a compensating increase in collaborations as the CSE enabled the remaining command cells to mutually adjust and to work together more intensively to address the loss of the C2V, coordinate on reassignment of assets, and revise the mission accordingly. This result should be at least partially attributed to the shared situation awareness provided through the CSE interface that enables collaboration. Additionally, the CSE provides several collaboration tools that facilitate collaboration through means other than radio transmissions. Overall, the CSE demonstrated an effective capability to allow the remaining command cells to quickly identify the loss of a C2V and to mitigate it collaboratively, yet without an excessive, counterproductive increase in the volume of the required collaborations. Providing improved collaboration is only one element in a complex set of elements that can help command decision making.

CHAPTER 8

The Time to Decide: How Awareness and Collaboration Affect the Command Decision Making Douglas J. Peters, LeRoy A. Jackson, Jennifer K. Phillips, and Karol G. Ross

Ultimately, it is the command decision, and the resulting action, that affects the battle outcome. All the processes we have discussed to this point— collection of information, collaboration, and formation of situation awareness—contribute to the success of the battle only inasmuch as they enable effective battle decisions. Figure 8.1 depicts but a small part of the complex relations between actions, decisions, collaboration, situation awareness, and automation, as we observed them in the MDC2 program. Command decisions—both the command cell’s decisions and the automated decisions—lead to battle actions. These, in turn, alter the battlefield situation, bring additional information, often increase or decrease uncertainty, and engender or impede collaboration. Changes in the availability of information lead to a modified common operating picture, automated decisions produced by the system, and further actions. These changes also lead to changes in the awareness of the battle situation in the minds of the human decision makers. Collaboration impacts the human situation awareness both positively and negatively (as we have seen in the previous chapters), which in turn affects the quality and timeliness of decisions and actions. Still, the complexity of these relations in itself does not indicate that decision making in such an environment is difficult, or at least does not inform us what makes it difficult. Yet, as the previous chapters have told us, the command-cell members often find it very challenging to arrive at even a remotely satisfactory decision. Why, then, is decision making so difficult in this environment? After all, we provide the cell members with a powerful information gathering, integration, and presentation system. We give them convenient tools to

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Figure 8.1. Commander Decision Environment—complex relations between actions, decisions, collaboration, situation awareness, and automation. See Appendix for explanation of abbreviations.

examine the available information and to explore its meaning in collaboration with other decision makers. The CSE system offers many automatically generated decisions, such as allocation and routing of resources for fire and intelligence collection tasks. The cell has established effective procedures for allocation and integration of decision-making tasks. Yet effective decision making continues to be a challenge, in spite of all these aids. One highly visible culprit is the lack of usable information: incompleteness of battlespace information, doubts about the reliability of the available information, and uncertainty about the likelihood of a decision’s consequences or about the utility of the respective alternatives. A theorist of military command argues that the lack of information and its uncertainty are the most important drivers of command: “The history of command can be understood in terms

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of a race between the demand for information and the ability of command systems to meet it. The quintessential problem facing any command system is dealing with uncertainty” (van Creveld 1985). Another major source of challenges involves the limits on the rationality of human decision makers (Simon 1991). Such limitations are diverse: constraints on the amount and complexity of the information that a human can processes or acquire in a given time period and multiple known biases in decision making. In particular, time pressure is a well-recognized source of errors in human decision making—as the number of decision tasks per unit time grows, the average quality of decisions deteriorates (Louvet, Casey, and Levis 1988). In network-enabled warfare, when a small command cell is subjected to a flood of information much of which requires some decisions, the time pressure can be a major threat to the quality of decision making (Kott 2007). Galbraith, for example, argued that the ability of a decision-making organization to produce successful performance is largely a function of avoiding information-processing overload (Galbraith 1974). Human decision-making biases are surprisingly powerful and resistant to mitigation. Many experiments demonstrate that real human decision making exhibits consistent and pervasive deviations (often termed paradoxes) from the expected utility theory, which for decades was accepted as a normative model of rational decision making. For example, humans tend to prefer those outcomes that have greater certainty, even if their expected utility is lower than those of alternative outcomes. For this reason, it is widely believed that bounded rationality is a more accurate characterization of human decision making than is the rationality described by expected utility theory (Tversky and Kahneman 1974; Kahneman and Tversky 1979). The anchoring and adjustment biases, for example, can be very influential when decision makers, particularly highly experienced ones, follow the decisions made in similar situations in the past (naturalistic decision making [Klein 1999]). Although such biases can be valuable as cognitive shortcuts, especially under time pressure, they also are dangerous sources of potential vulnerabilities. For example, deception techniques are often based on the tendency of human decision makers to look for familiar patterns, to interpret the available information in light of their past experiences. Deceivers also benefit from confirmation bias, the tendency to discount evidence that contradicts an accepted hypothesis (Bell and Whaley 1991). With a system like CSE, one might expect that biases are at least partially alleviated by computational aids. Decision-support agents like the Attack Guidance Matrix that we discussed earlier can greatly improve the speed and accuracy of decision making, especially when the information volume is large and time pressure is high. But they also add complexity to the system, leading to new and often more drastic types of errors, especially when interacting with humans (Perrow 1999). Additional challenges of decision making stem from other factors, such as social forces within an organization, which go beyond the purely information-

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processing perspectives. For example, groupthink—the tendency of decision makers within a cohesive group to pressure each other toward uniformity and against voicing dissenting opinions ( Janis 1982)—can produce catastrophic failures of decision making. Indeed, our observations of failures on command cells’ collaboration point to possible groupthink tendencies, particularly in view of the fact that information overload encourages groupthink ( Janis 1982, 196). Which of these factors, if any, impact the decision making in networkenabled warfare, and to what extent? How much can a system like the CSE alleviate or perhaps aggravate such challenges to human decision making? As a key part of the MDC2 program, we sought to evaluate the ability of the command-cell members—commanders and staff—to make effective decisions in the information-rich environment of network-enabled warfare. Understanding the decision-making process of the commanders, the use of automated decision aids, and the presentation of critical information for decisions were crucial to this evaluation. We begin this chapter by exploring how we collected information to support our decision-making analysis throughout the experimental program. This section chronicles not only the progression of the approaches we took, but also what we learned from the methods themselves and how they were adapted to yield a richer set of data. We then proceed to discuss some of the lessons learned in the analysis of the data and their potential relevance to the development of future command tools. COLLECTING THE DATA ABOUT DECISION MAKING A key effort in the MDC2 experiments was to devise mechanisms for capturing the data about decision making. We found it remarkably challenging to obtain the data that would give us the desired insights. In a trial-and-error fashion, we proceeded through a number of approaches. To begin with, we built automated loggers that captured an enormous quantity of data for each experimental run. For example, CSE automated decisions and contextual decision-making information (such as the SAt curves) were available directly from the data loggers. However, the raw data from these loggers were of limited direct use in evaluating human decision making because they could not quantify the commander’s cognitive processes and his understanding of the situation. At best, they were helpful to support findings and to understand what was happening during critical decisions. In addition to automated data logging, five other mechanisms were used to collect decision-related information: analytic observers, focus groups, operator interviews, surveys, and battle summary sessions. These were developed and refined throughout the experimental campaign, especially over the last five experiments in the campaign, beginning with Experiment 4a. In Experiment 4a, we employed several traditional tools of the analyst’s toolbox. During the experiments, analytic observers recorded significant events

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and characterized the effectiveness of the battle-command environment with respect to the commander’s ability to execute his mission. Within our cadre of observers, one person was dedicated to record and classify every decision that the commander verbalized. Each decision was identified as relating to seeing (for example, repositioning sensors or classifying imagery), striking (for example, when and where to place fires), or moving (for example, how to array the forces for movement). Additionally, each decision was characterized according to the associated complexity. We classified decisions that were prompted by a clear trigger and appeared to be made according to a small set of understandable rules as automatable decisions. Examples of automatable decisions were “Fire at that tank” and “Let’s get BDA (Battle Damage Assessment) on that engagement.” Those decisions that were based on a well-understood and limited set of variables but required a degree of human judgment not reducible to wellunderstood rules, were classified as adjustment decisions. An example of an adjustment decision was to determine when the necessary conditions are satisfied to begin operations. Finally, decisions that required a broad, holistic understanding of the situation, encompassing a wide range of variables, and that fundamentally changed (or confirmed) the entire operation’s strategy were characterized as complex decisions. An example of a complex decision from Experiment 4a: the commander identified a deficiency in his plan and saw the need to develop contingency plans: “If the enemy gets into Granite Pass, it is going to be very difficult for us to get through him. We need to look at some other maneuver options.” Collecting this information allowed us to characterize the decision making in a number of ways. Analyzing the types of decisions made by the commanders, we identified the battle functions on which the commander focused most of his attention. Likewise, decision complexity characterizations helped us better understand whether the commander was making decisions that could be automated with a tool or making frequent complex decisions. Together, these two characterizations enabled us to identify specific areas of the CSE that could better be tailored to support the decision maker’s needs. Figure 8.2 shows a partial analysis of decisions by type from Experiment 4a. Surveys, on the other hand, proved much less useful. After each experimental run, we asked each commander to complete a survey—his assessment of how well the run went and what challenged him during the run. These surveys, while containing occasional nuggets of interesting information were largely ineffective because the questions were not specific to the events of a given run, and because being the last event of a long day, surveys did not elicit sufficiently detailed responses from the fatigued commander and his staff. Overall, although the decision characterization was useful to help improve the functions CSE, it did not tell us much about the effectiveness of the decisions, or about the specific information and conditions supporting effective decisions. Therefore, in Experiment 4b (a repeat of the Experiment 4a

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Figure 8.2 Experiment 4a summary of decisions by type and complexity.

with a different, less-experienced team of operators), we added a qualitative assessment of decisions. We conducted this assessment in postexperiment analysis sessions with the help of military subject matter experts after watching a replay of the battle and events leading to the decision in question. The following criteria, derived from the Network Centric Operations Conceptual Framework (Evidence Based Research 2003), were used to evaluate the quality of a decision as follows: • Appropriateness: consistency of the decision with situation awareness (the situation as was known to the decision maker at the moment), mission objectives, and commander’s intent. • Correctness: consistency of the decision with ground truth (i.e., with the actual situation). • Timeliness: whether the decision is made within the window of opportunity. • Completeness: the extent to which all the necessary factors are considered in making a decision. • Relevance: the extent to which the decision is directly related to mission.

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• Confidence: the extent to which the decision maker is confident in a decision made. • Outcome consistency: the extent to which the outcome of the decision is consistent with the intended outcome. • Criticality: the extent to which the decision made is critical to mission success.

Although this approach provided us with extensive data on the quality of the decisions (e.g., see Figure 8.3), it also proved to be of limited use. Without determining the context and the reasons for a decision, and the information that led to the decision, we could not pinpoint ways for the CSE to improve the decision-making environment. In addition to the study of decision quality, we introduced another datacollection approach that showed its initial promise in Experiment 4b but then became a core analytic tool for later experiments. Process tracing (Shattuck and Miller 2004) examines a single episode of an experimental run in detail. This methodology connects collaboration to changes in SA and SA to decision making with a focus on the operators and their use of the CSE. Process tracing externalizes internal processes (Woods 1993) and tries to explain the genesis of a decision by mapping out how an episode unfolded, including information elements available to the operators, what information was noted by operators, and operators’ interpretations of the information in immediate and larger contexts. In Experiment 4b, we completed process tracing for a single event, and although we were unable to draw any significant insights from one event, the methodology showed promise for understanding both the context of a decision and the challenges that faced the decision maker at the time of the decision.

Figure 8.3. Expert assessments considered the correctness, timeliness, relevance, and other characteristics of decisions.

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With the introduction of a manned dismounted platoon for Experiment 5, the complexity of the decision-making environment increased significantly. Now, instead of communicating his thoughts and decisions to staff members located in the same vehicle, the commander had to convey his intent and orders to subordinate commanders reachable via the radio and shared displays, with sufficient clarity and detail. Our analysts also examined information requirements for warfighters conducting dismounted operations. The process-tracing techniques were well suited for this complex environment, and we focused on identifying key decisions during each run and analyzing those decisions in detail. The detailed process tracing combined video and audio playback of events leading to a decision, audio logs of the communications, query results from the automated loggers, the SAt curve, observer notes, and interview records. All these components together supported a very detailed study of short-duration events. To facilitate these process tracings, we compiled critical information into a single source. By plotting different types of information across a common time axis, we were able to show what was happening at various time points during the battle. Because these charts were developed by stacking multiple variables against a common time axis, we referred to these composite views as “tacked charts. An example is shown in Figure 5.10. This particular stacked chart was developed to help us simultaneously view decision making, collaboration, information availability, and battle tempo data. The relations between these elements helped us understand what events shaped a key decision. Of particular value in this methodology is a technique for extracting critical information through interviews. Given our earlier lack of success with end-of-run surveys, we were eager to try a technique that would allow us to identify details of critical decisions. The critical decision method of interviewing (Klein, Calderwood, and MacGregor 1989) uses a two-person team to identify a single decision made during a run and explore it in detail. There are four steps to this interviewing technique: Step 1 is incident identification. The interviewer presents a situation or a critical event and asks the decision maker to talk about the event from his perspective with a particular focus on the role he played during the event. The interviewer does not interrupt the interviewees with clarifying questions (these come later in Step 3), and the interview team takes careful notes regarding the actions and decisions made during the event. Step 2 establishes the timeline of the event. The interviewer repeats the story back to the interviewee with special emphasis on the timing of events and decisions. Through this process, the interviewer becomes familiar with the subcomponents and timing of events, and how they impacted the outcomes and decisions made. Special attention is paid to decision points, shifts in situation awareness, gaps, and anomalies. Step 3, deepening, tries to uncover the story behind the story. Here most of the detailed information becomes apparent—why things were done as they were, why decisions

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were or were not made, what information and experiential components contributed the most. This stage uses the event timeline and explores it in detail. Anomalies or gaps in the story are investigated during this phase. Step 4 focuses on the what-if queries. The purpose of this step is to consider what conditions may have made a critical difference in how the situations unfolded and in the decisions that were made. It also asks the question of what a less-experienced person may have done in the same situation to further draw out the subtle factors that enable the interviewee to make effective decisions.

Because both the process traces and the interviews proved to be effective in Experiment 5, Experiments 6 and 7 built on these analytic tools and introduced two additional tools. The first additional tool—a detailed timeline of a run—became necessary due to the increased complexity and duration of runs. Although we had extensive and detailed records of what happened during each run (including video and audio recordings), the task of producing a unified, concise description of what happened during a run was difficult after the experiment was complete. Therefore, after each experimental run, a group of analysts who had closely observed the various echelons and cells (friendly and enemy) wrote a short but complete synopsis of the run. In the synopsis they were able to capture concisely the flow of the battle and detail the most significant events of the battle from both the Blue and Red perspectives. The second tool we introduced in the later experiments was focus groups. Organized for each command cell, a focus group session was relatively short (less than one hour) and was facilitated by a member of the core analysis team who observed that cell during planning and execution. The facilitator began the focus group session with candidate decisions of interest identified by the analysis team during or immediately after the run. A recorder took notes. After the focus group session, the facilitator or recorder briefed the entire analytic observer team on key findings. At the focus group sessions, we tried to understand the battle in general, and the key events specifically, from the perspective of the operators. Facilitators used the following questions to guide the focus group and to ensure that all members participated in the session. • Ask the operators to summarize the battle from their perspective. Brief back the key elements of the battle summary. Use the operator’s words to the maximum extent possible. Introduce the decisions of interest, placing them in the context of the battle summary. • Ask the operators to describe the events that led to a specific decision. Listen for decision points, collaborations, shifts in situation awareness, gaps in the story, gaps in the timeline, conceptual leaps, anomalies or violations of expectations, errors, ambiguous cues, individual differences, and who played the key roles. Ask clarifying questions and then brief back the incident timeline. • Ask those operators who played key roles questions about situation assessment and cues. Listen for critical decisions, cues and their implications, ambiguous cues, strategies,

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anomalies, and violations of expected behavior with respect to the commander’s intent. • Ask operators to describe CSE-related issues. Ask probing questions as necessary. What worked well? What features helped their situation awareness? What features did you use to collaborate? How did you use automated decision-support functions? What did you not use and why? What would you automate? • Ask operators to describe procedure-related issues. Ask probing questions as necessary. What responsibilities were assigned to each operator? What tools were associated with the assigned responsibilities? When were the operators overwhelmed with the work-load? How did the commander adjust staff roles during the mission? What new procedures did you implement and why? What did you struggle with? Why did you use a certain procedure?

The combination of focus group and CTA interviews along with the other quantitative data logs gave us an ability to reconstruct the battle, to examine how decisions were made, and to identify issues that may affect battle command in the future force. The following sections describe some of the resulting conclusions.

THE HEAVY PRICE OF INFORMATION Because the notional future force represented in our experimental program was heavily armed but lightly armored, availability of information was exceptionally critical to mission success. The cost of stumbling upon an undetected enemy asset was inevitably the loss of a critical piece of equipment. However, if the commander could find the enemy, he could use his precision weapons to engage the enemy at great distance. In order to find the enemy at long range, the force was equipped with a rich set of sensor platforms. The sheer number of sensors, along with the well-understood importance of information about enemy assets, led the commander to focus more attention on information needs than is common for commanders of today’s forces. This additional emphasis on information was not only the result of the increased importance of information but also due to the increased availability of information. Because even a single enemy entity could have a major impact on this lightly armored force, our commanders focused much of their attention on intelligence gathering regarding individual enemy platforms, in addition to the more conventional tasks of aggregating information on enemy formations and possible enemy courses of action. Our commanders needed to know where individual enemy entities were and, just as importantly, where they were not. In addition, they paid attention to the classification of detected entities and the condition of targets after they were engaged BDA. In fact, the commander’s strong focus on “seeing the enemy” at the expense of other functions became obvious when we analyzed the content of his decisions. For example, in Experiment 4a, almost half of the decisions verbalized by the command-cell members were characterized as see decisions (the other

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common types, move and strike decisions, accounted for about 25% each; see Figure 8.2). Still, the commanders in our experiments tended to delegate the entitybased information-gathering responsibility to the intelligence manager. This helped devolve a substantial cognitive load from the commander and also served to unify control of the sensor assets. On the other hand, this delegation deprived the cell of the critical big picture of the enemy since the intelligence manager was focused on finding and characterizing individual battlespace entities instead of developing an aggregated understanding of the enemy. In Experiment 6, one of the commanders recognized this deficiency and saw that his intelligence manager was overloaded with tasks, while the effects manager was being underutilized (since many of the engagement tasks were automated or assisted by the CSE). The commander made the effects manager responsible for coordinating with the intelligence manager to obtain images for BDA and to conduct BDA assessments. The advantage of placing this responsibility with the effects manager was obvious—not only did it alleviate the cognitive load placed on the intelligence manager, but it also enabled a rapid reengagement of assets that were not destroyed by the original engagement. In general, the flexibility of CSE facilitated opportunities for creative and unconventional allocation (and dynamic reallocation during the battle) of responsibilities between members of the command cell. BDA proved to be particularly critical and demanding throughout the experimental program, and commanders struggled with obtaining quality assessments from their available images. More often than not, BDA images (produced with realistic imagery simulator) did not provide enough information to make definitive conclusions about the results of an engagement. Thus, about 90 percent of BDA images from Experiment 4a were inconclusive (Figure 6.5 of Chapter 6). This ultimately led to frequent reengagements of targets in order to ensure they were destroyed. In Experiment 4a, 44 percent of targets were reengaged, and in Experiment 4b, 54 percent were reengaged. The need to understand the state of enemy entities through effective BDA was clearly demonstrated in Experiment 4a, Run 6, where a single enemy armored personnel carrier destroyed enough of the Blue force to render the unit combat ineffective. This particular enemy entity had been engaged early in the battle and suffered a mobility-kill. However, the intelligence manager classified the asset as dead based on a BDA picture. This mistake was not found until it was too late. The Blue force was unable to continue its mission. Undoubtedly, tomorrow’s commanders will greatly benefit from the rich information available to them. At the same time, they will be heavily taxed with the need to process the vast information delivered through networked sensors—both initial intelligence and BDA. Commanders should expect to spend more time, perhaps over half of their time, on “seeing” the enemy. Part of the solution is to equip them with appropriate information-processing

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tools. In addition, the staff responsibilities should be continually reevaluated and reallocated to ensure that all critical duties are well covered. ADDICTION TO INFORMATION Information can be addictive. We often observed situations when commanders delayed important decisions in order to pursue an actual or perceived possibility of acquiring additional information. The cost of the additional information is time, and lost time is a heavy price to pay, especially for the future force that relies on agility. As with today’s commanders, uncertainty is present in all decisions, and decisions are often influenced by aversion to risk in the presence of uncertainty. Unlike today’s commanders, however, our commanders had the tools readily available to them to further develop their information picture. They could reduce their uncertainty by maneuvering sensor platforms into position to better cover a critical area. This availability of easy access to additional information was a double-edged sword because it often slowed the Blue force significantly. Commanders commonly sacrificed the speed advantage of their lightly armored force in order to satisfy their perceived need for information. These delays enabled the enemy to react to an assault and move to positions of advantage. An example of this occurred in Experiment 4a, Run 8, where the commander incorrectly assessed that the enemy had a significant force along the planned axis of advance. Even after covering this area several times with sensors and not finding many enemy assets, the commander ordered “. . . need to slow down a bit in the north . . . don’t want you wondering in there.” At this time in the battle, the average velocity of moving Blue platforms dropped from 20 km/h to 5 km/h. The commander exposed his force to enemy artillery for the sake of obtaining even more detailed coverage of the area. On the other hand, commanders also frequently made the opposite mistake when they rushed into an enemy ambush without adequate reconnaissance. An example of this occurred in Run 8 of Experiment 6 where several critical sensor assets were lost early in the run, and the CAU-1 commander quickly outran the coverage of his remaining sensors. In cases like this, the commander was lulled by the lack of enemy detections on his CSE screen and advanced without adequate information—perhaps perceiving the lack of detections as sufficient information to begin actions on the objective. This event is discussed in detail in the following section. Today’s commanders are often taught that the effectiveness of a decision is directly related to the timeliness of the decision. However, while timeliness will remain critical, tomorrow’s commanders will need to pay more attention to the complex trade-offs between additional information and decision timeliness. Effective synchronization of information gathering with force maneuver is a formidable challenge in information-rich (and therefore potentially information addictive) warfare. Both specialized training and new tools are

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Figure 8.4. SAt curve for Experiment 6, Run 8. See Appendix for explanation of abbreviations.

required to prevent the failures that commanders experienced so often in our experiments. THE DARK SIDE OF COLLABORATION Effective decision making can also be delayed and even derailed by collaboration. In certain cases, we observed a commander’s understanding of the current Blue or Red disposition degraded as a result of collaborations with subordinates, peers, or higher headquarters commanders. Unlike in chapter 7 where we discuss cases of ineffective collaboration, here collaboration itself went well. However, the effects of the collaboration on a commander’s decisions were highly detrimental. Run 8 of Experiment 6 provides an interesting example of how collaboration can lull a decision maker into complacency by validating incorrect conclusions. In this run, the CAU-1 commander’s force was destroyed by a strong enemy counterattack. Figure 8.4 shows the SAt curve for Run 8 with an overlay of time points when Blue entities were destroyed. At 32 minutes into this run (vertical dashed line), the CAT commander assessed that the enemy was defending heavy forward (i.e., mainly in the CAU-2 sector). Several minutes later, the CAU-2 commander seemed to confirm that assessment with his report “I suspect [the enemy’s] intent is to defend heavy forward [in CAU-2 sector].” This assessment was derived from several detections made very early in the run. The figure shows that little new information about the enemy is acquired before the CAU-1 commander announces that “I’m not see-

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ing any counterattacking forces moving towards us [i.e., CAU-1]. I think the majority of the enemy force is in [CAU-2’s] sector” at 52 minutes into the run. This would be a reasonable conclusion if he were using his sensors to develop the picture of the enemy, but in fact CAU-1 had focused his sensors on his flank and did not have any sensor coverage in the area where he was moving his troops. Soon thereafter, CAU-1 stumbled into a major Red counterattack force and was combat ineffective within minutes. So, the obvious question is, why did the CAU-1 commander not make more effective use of his sensors? Certainly, one important factor was a tactical blunder early in the run that led to the destruction of several key sensor assets, leaving him with fewer sensors to conduct his mission. With this reduced set of sensors, the commander had to protect his flank, scout forward to the objective, and conduct necessary BDA. At 44 minutes into the fight, the commander tasked his staff member to reposition the sensors to scout the objective but was distracted by the collaboration with a staff member who declared that he had found several enemy assets far to the west. Because of this collaboration, the commander neglected his intended mission of covering the area ahead of his force and began focusing attention far to the western flank of the advancing force. Yet, less than 10 minutes later, and with no new information about the objective, the commander was secure enough in his assessment that he began his offensive and was met with a major enemy counterattack force that decimated his unit. There were several reasons for this poor decision to begin operations without conducting proper reconnaissance. The collaborative assessment of the situation with CAU-2 commander and with CAT commander led the CAU-1 commander to expect few enemy forces in his zone. Later, the commander’s collaboration with a staff member confirmed his erroneous understanding that the enemy force was far from his zone. Though this was a rather extreme example of a collaboration negatively affecting decision making, there were many other examples throughout the experiments that showed collaborations either distracting the commander from making critical decisions or lulling him into accepting an incorrect understanding of the battlespace. In fact, of seven collaboration process traces chosen for detailed analysis in Experiment 6, only three cases of collaboration yielded improved cognitive situation awareness for the operators. In the remaining four cases, collaboration dangerously distracted the decision maker from his primary focus or reinforced an incorrect understanding of the current Red or Blue disposition. Consider that commanders in our experiments were equipped with a substantial collection of collaboration tools—instant messaging, multiple radio frequencies, shared displays, graphics overlays, and a shared whiteboard. Although the commanders took full advantage of these tools and found them clearly beneficial, there was also a significant cost to collaboration. To minimize such costs, future command cells will need effective protocols—and

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corresponding discipline—for collaborating: how often and under what circumstances collaboration occurs, with what tools, and in what manner. AUTOMATION OF DECISIONS Commanders and staffs used automated decisions extensively and could use them even more. However, the nature of these automated decisions requires an explanation. In effect, the CSE allowed the commander to formulate his decisions before a battle and enter them into the system. Then, during the operations, a set of predefined conditions would trigger the decisions. Thus, the decisions were actually made by the commander and staff. It was only the invocation and execution of these decisions that was often performed automatically when the proper conditions were met. One type of such automatically triggered decision was the automated fires. The conditions for invoking a fire mission included staff-defined criteria for confidence level, type of target, the uncertainty of its location, and targetacquisition quality. Recall that in chapter 3 we discussed the Attack Guidance Matrix (AGM), an intelligent agent within the CSE that identified enemy targets and calculated the most suitable ways to attack them with Blue fire assets. It could also execute fires; for example, it could issue a command to an automated unmanned mortar to fire at a particular target, automatically or semiautomatically, as instructed by the human staff member. Typically, a commander or an effects manager would specify the semiautomatic option: the AGM recommended the fire to them and would execute it only when a command-cell member approved the recommendation. Occasionally, in extreme situations, they would allow fully automated fires, without a human in the decision loop. Another similar type of automated decision making was an intelligent agent for automated BDA management. This agent used the commanderestablished rules to determine which sensor asset was the most appropriate to conduct BDA and would automatically task that asset to perform the BDA assignment. For example, it would automatically command a UAV to collect information about the status of a recently attacked target. Such decisions were made based on the specified criteria regarding the available sensor platforms, areas of responsibility, and enemy assets to be avoided. In each experiment, we found that command-cell members used the automated fires feature effectively and frequently. Commanders and effects managers spent ample time prior to the beginning of battle defining the conditions for automated fires. During the runs, these settings were rarely changed and almost every run had instances of automated engagements of enemy assets. However, there were also many manual engagements that could have been automated but weren’t. Instead, a cell member would manually identify a Red target, select a Blue fire asset and suitable munitions, and then issue a command to fire—overall, a much more laborious and slower operation than a semiautomated fire. One reason for preferring such manual fires was that it often took too long to accumulate enough intelligence on an enemy tar-

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get to meet the preestablished criteria for an automated or semiautomated fire decision—they had to be fairly general and therefore too stringent. For example, since in our experimental scenarios there were relatively few civilian tracked vehicles in the battlespace (a bulldozer being an obvious exception), the effects manager would often engage any vehicle classified as tracked even before there was a clear indication that it was an enemy asset. At the same time, he was hesitant to allow automatic fires on all tracked targets. In such cases, a manual engagement was intentional, but in other cases, the staff wondered aloud why an enemy vehicle was not being engaged. To the effects manager’s eye, the specified conditions were apparently met, and the AGM should have initiated a fire event when in fact the situation had not met the full set of the prespecified trigger conditions. The staff’s puzzlement over why an automated fire was not happening had an adverse affect. Because the CSE was not performing as expected by the effects manager, his confidence in the capability of the tool diminished. Unable to understand why the AGM refused to fire, the effects manager tended to apply simple and very specific rules so that only the most critical targets were automatically engaged. The automated BDA tool suffered from this lack of understanding, which lead to a lack of trust, much more so than with the AGM. One would think that the seemingly less critical and, nonlethal nature of BDA would lead to more ready acceptance by the operators. After all, the automated BDA tool was developed at the request of commanders in an early experiment where they routinely tasked a UAV to take a picture of engaged Red assets. The commanders felt that if this task was automated, not only would it lighten the load of the staff, but it would also ensure that the task was conducted in a timely fashion. This seemed like an obvious task to develop effective rules, and the CSE developers set to work automating these seemingly obvious BDA tasks. The solution worked exactly as expected by the tool designers and by the command staff who originally requested the automation. Unfortunately, the new command-cell members participating in the next experiment had rather different expectations. Early in the experiment, they used the automated BDA tool and became utterly confused. The information manager controlling the UAVs would wonder aloud, “Who is moving my UAV?” and “Where is that thing going now?” What was originally designed to lighten the load of the command cell quickly turned into a perceived loss of control over critical assets. The automated BDA tool became available in Experiment 4b, and in each subsequent experiment, commanders and their staffs began by using the functionality but then quickly abandoned it because of the perceived loss of control. So, what decisions can and should be automated? Why was the automated fires capability well received while the automated BDA was not? Based on our experience, we believe the difference comes down to the following considerations. First, the commanders and staff must trust the system. Not only must the system be reliable enough to work as expected every time, but it must also

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be simple enough for the operators to understand when it will act and when it won’t. In particular, there must be a very clear and easily understandable distinction between the computer control and human control. For example, in case of the automated fires, it was very clear whether the human or the computer was to make the final decision, and once a munition was launched, there were no opportunity for—or confusion about—the control. However, in case of the BDA management, there was continuous uncertainty about who was in control of a given platform—a human or a computer—and the information manager had no means to collaborate with the system to answer his questions about control. Second, it should be easy for the operator to enter rules that govern an automated decision-making tool. For example, it may initially seem obvious to the developers of an automated tool to call for fires on detected enemy tanks as soon as possible. However, when low on ammunition, a commander might want to fire only at those tanks that are able to affect his axis of advance. Likewise, he may not want to automatically engage tanks near populated areas or if a civilian vehicle was spotted nearby. The more rules and tweaks, the harder it is to understand the decisions made by the tool and the sooner an operator will build distrust when the tool does not perform as he expects. Naturally, other nontechnological factors also affect the extent to which automated decisions will be available to a future force. Perhaps our commanders accepted the automated fires so easily because the experiments were merely a simulation: the consequence of a wrong automated decision was the destruction of computer bytes and not of real people. In today’s practice, a human is personally accountable for every fire decision, and great care is taken to avoid accidents. With any automation of decisions related to either lethal fires or to any other battle actions come many challenging questions about responsibility and accountability. THE FOREST AND THE TREES Decision making can suffer from an excessive volume of detailed information offered by the network-enabled command system. In our experiments, we observed several mechanisms by which the richness of information negatively impacted the decision making. First, recall that all operators’ displays were tied to the same underlying data source. Therefore, soon after an enemy asset was detected, every screen of every command-cell member in every command vehicle would show this new information. At first glance, this seems to be exactly the right behavior of the system, and the operators indeed desired to see all such information. And yet, this faithful delivery of detailed information proved to be a major distraction to the cell members’ decision making, especially to commanders. Instead of focusing on understanding the enemy course of action and how to best counter likely enemy actions, commanders became mesmerized with the screen, hunting for changes in the display and reacting to them.

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This so-called looking-for-trees behavior had at least two very adverse impacts on the commander’s ability to understand the battlespace. On one hand, the commander gravitated to a reactive mode: he responded to changes on his display and frequently lost the initiative in the battle. This was especially true when inadequate sensor management led to detections of enemy assets outside of the truly critical areas of the battlespace. In such cases, the commander’s fixation on the screen led him to focus on largely irrelevant topics while losing the grasp of main events in the unfolding battle. On the other hand, responding to frequent updates on the screen prevented the commander from spending the necessary time thinking about the bigger picture of the situation. For example, in Experiment 4b, we noticed the excessive frequency with which the commander shifted his attention. He was almost constantly scanning the display for new information, moving his cursor from one entity to another to determine if new information was available, and reacting to the appearance of an enemy icon or alert box on the screen. In Run 4, he shifted his attention 26 times over a 13-minute period—an average of once every 30 seconds. During a 16-minute period in Run 6, he shifted his attention 60 times, for an average dwell time of about 16 seconds. The implications of this frequent attention shifting are interesting and disturbing. The more often a decision maker shifts attention, the shorter the dwell time on a data element, and the more shallow the cognitive processing. The decision maker may determine, for example, that an enemy vehicle has been detected and may decide how to react to it. Then he shifts his attention to another change in his screen, without having enough time to reason about the broader issues—the implications of the detection of that type of vehicle at that place in the battlespace. Furthermore, the commander would often “drag” the other cell members along with him as he shifted attention—announcing the updates he was noticing or issuing reactive tasks such as “DRAEGA just popped up, let’s get a round down there.” Such unnecessary and counterproductive communications about the newly arriving information were depressingly common. For example, in Experiment 6, as the commander watched on his screen the reports of Red artillery rounds landing around one of his platoon leader’s vehicle, he felt compelled to keep announcing this fact to the beleaguered platoon leader. Of course, the platoon leader was well aware that he was under fire, and the commander’s communications only served to distract him.

Concluding Thoughts Alexander Kott

It is intriguing to consider how many diverse factors converge to provide the impetus for the new paradigm of network-enabled battle command. One of them, as we discussed in chapter 1, is the centuries-old trend toward the greater dispersion of forces in the battlespace. Eventually, the dispersion of even a small unit reaches the point where human voice, hearing, and sight no longer suffice for obtaining or communicating information. Man-made devices, such as acoustic and imaging sensors along with wireless networks, become indispensable. These begin to produce and deliver more information than a commander can handle, which in turn creates the need for automated processing—information translation, fusion, and interpretation. Closely related to the force dispersion, but of a different nature, is the emergence of intelligent weapons and platforms, such as unmanned aerial and ground vehicles, and missiles for precision standoff engagements. Now the commander has both the need and the opportunity to see and to command at ranges far beyond the human sight. It is the first time in history when even ground forces possess both the desire and technological means to fight largely beyond the line of sight. This too calls for reliance on nonhuman sensors and command links, and the attending need for interpreting and generating voluminous data. With new information technologies delivering war images to every TV and every Web site in the world, the commander is under a great pressure to minimize both friendly and civilian casualties. For this, he must rely on greater detail, timeliness, and precision of battlespace intelligence, plans, and execution. His challenge is exacerbated by the increasing standoff lethality of enemy combatants, even irregular dismounted fighters with weapons like RPGs and

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IEDs. Their preference for operations in urban environments creates an even greater complexity: physical clutter, truly three-dimensional terrain, presence of civilians, and great opportunities for enemy cover and concealment. All this greatly multiplies the range of possibilities and details the commander must consider. Once again, he needs better means to collect, process, and coordinate information and to generate effective decisions. On the other hand, the incessant arguments about the need to create lighter ground forces and therefore to trade “armor for information” (Wilson, Gordon, and Johnson 2004) are somewhat misleading. Battle-command systems suitable for network-enabled warfare can be of great value to any force: to heavy mechanized forces, light infantry, and anything in between. The lightness of the battle vehicles and the richness of the information should not be conflated. These are orthogonal issues; each should be evaluated on its own merit. In fact, without waiting for any major changes in either platforms or communication networks, network-enabled battle command is already emerging in practice. In the few years it took us to develop and explore the command tools of the MDC2 program, they ceased to appear far-fetched and futuristic. Recent successful developments like the Command Post of the Future (http://www. globalsecurity.org/military/systems/ground/cpof.htm) and practical experiments like the Air Assault Expeditionary Force (Bailey 2005) already demonstrate some of the ideas that underpin MDC2. However, as battle-command technologists are learning to provide commanders with more information in network-enabled warfare, the challenge of a cognitive bottleneck is growing in importance. On one hand, as we see in the MDC2 experiments, tomorrow’s commanders will benefit from the rich information available to them. At the same time, they will be heavily taxed with the need to process the vast amount of information. Our experiments show a strong tendency for the commander to reallocate the bulk of his resources to the battle of cognition—particularly the efforts to maintain situation awareness. Somewhat alarmingly, in spite of our best efforts to enhance the CSE and to optimize the command cell’s processes, the commander and staff face a very heavy cognitive load. Gaps and misinterpretations in their situation awareness are surprisingly common. Even with adequate SAt (the measure of correct information that the command system presents to the commander), we find numerous cases when the commanders and staff fail to interpret the situation correctly, resulting in low SAc (the measure of information the commander understands correctly). Human psychological biases are the likely mechanisms behind these deficiencies. The challenge, then, is to build battlecommand tools that match the minds of human commanders and staffs—both their strengths and their weaknesses. THE TOOLS OF NETWORK-ENABLED COMMAND Perhaps the most important achievement of the MDC2 program is simply the proof of existence—the experimental evidence that it is possible

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to prototype a working, real-time, multi-echelon, network-enabled battlecommand system. Using a suite of decision-support applications, the CSE demonstrates the ability to help the commander and staff manage a large volume of information arriving at high rate, acquire situation awareness, and execute the battle by issuing commands to a demanding array of assets. It also shows that distinct battlefield functional areas (maneuver, intelligence, logistics, and fires—in conventional practice supported by different systems) can be integrated into a single application that the operator can tailor to his specific needs while still having access to all these functions. Further, the CSE offers a working example of how planning and execution processes—conventionally performed separately and with separate tools—can be performed from a single unified application in a spiral process. Whether such technical innovations can deliver a value to warfighters is a matter of experimental confirmation. Although never fielded, the CSE does show strong benefits in multiple simulation-based experiments. The Blue command cells execute extremely demanding missions with agility, precision, and coordination that far exceeds what would be possible given the present-day battle-command tools and processes. The battle-command tools of CSE help commanders and staff in a number of ways. First, they increase the commander’s situation awareness and reduce his uncertainty in situation understanding. They help the commander visualize the current situation and project it into the future. The commander is able to recognize emergency situations and rapidly reconfigure his assets to meet the requirements of an emerging tactical situation. The increased speed of command helps the commander dictate the operational tempo to the enemy. The sharing of situation awareness across several command cells enables them to collaborate and cooperate: they assist each other with their sensing and fire assets even when separated by exceptionally large (by contemporary standards) distances. Admittedly, one must maintain some healthy skepticism about the degree to which such promising advantages can transfer from simulated war games to the real-world battlespace. On the other hand, one can certainly transfer many of the lessons learned in the MDC2 program to the development of real network-enabled command systems. To begin with, the development process requires an extensive investment into rigorous experimentation, much more extensive than is typical in today’s practice. In systems where the key challenge is a subject as poorly understood as human cognition, science still provides relatively little help to an engineer, while reliance on common-sense, seat-of-the-pants solutions is often outright harmful. Even more misguided is the practice of producing voluminous requirements documents based on predevelopment assumptions and guesses and then driving the development process by adherence to the requirements. Instead, the process should be spiral and driven by frequent series of experiments. (These should not be confused with user acceptance tests or system validation tests.) The experiments should be heavily instrumented to enable detailed and quantitative analysis of the relevant aspects of the operators’

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cognitive processes. For example, we instrumented the experimental environment in order to automatically collect data regarding the information available to each operator of the command cells, and the ground truth of the battlespace, as well as the real-time qualitative assessments of specially trained observers. Additional techniques for postexperiment data collection included interviews that traced critical events and decisions, and a temporal analysis to correlate quantitative trends with battle events and decisions. Much of this data was processed in real-time and displayed to experiment controllers. In our experience, instrumentation and quantitative analysis of the operator’s situation awareness, with often surprising results, was a key factor guiding the system development. If at all possible, the series of experiments should be designed to provide quantitative comparison with an alternative approach, and the experimental results should be statistically significant. Naturally, capable simulation tools are critical to success of simulation-based experiments. In order to meet the needs of our experiments, we had to make a significant investment in upgrading the available simulation system. Among the difficult design questions that only experiments can answer is the appropriate extent of automated support to decision making. What decisionmaking functions should be automated or supported, and to what degree? The answers may not be obvious, and the critical factors involved in the choice can be subtle. We find that decision automation (or partial automation) is very important to alleviating the cognitive load in network-enabled command. Yet it requires a careful exploration to determine the exact place and form that the automation should take. For example, we found operators gave dramatically different receptions to two nearly identical decision aids, one for automation of fire decisions and another for automation of BDA missions. Counterintuitively, the former became highly popular, while the latter was utterly rejected. To the extent that our limited experience can be generalized, much depends on the cognitive cost-benefit ratio. A decision-aid’s intervention tends to be more successful when the human operator can either simply reject it or readily transform it into an automated action, with no further complications to his cognitive processes. Not surprisingly, the higher the cognitive load, for example in high-tempo operations or when a part of the command cell is out of action, the greater is the use of low-overhead decision aids. An important consideration is the degree of control that operators have over the decisionsupport tool. For example, because many of the CSE’s decision aids use rules, we find that operators must be given the means to easily modify and adapt the rules to their preferences. Although building successful decision aids is difficult, their value to a cognitively overloaded commander can be enormous. Our findings stress the need for additional decision-aids tools in the CSE. One example is a sensorcoverage management tool. We find that operators exhibit consistent difficulties in knowing what they (or rather their sensors, such as a UAV-based camera) have seen and what they have not. Surprisingly often, a commander would believe—incorrectly and disastrously for his forces—that an area was

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adequately reviewed by his sensors and was devoid of enemy assets. A tool could help operators maintain awareness of areas and threats that have been (or not) seen by various sensors, the capabilities of the sensors, and the time passed since the sensors visited the area. It could also proactively highlight to the operator the areas that were inadequately explored or explored too long ago. On a related note, tools that predict possible actions of the enemy, such as the RAID system developed by DARPA (Ownby and Kott 2006), can alert a commander to potential threats he may not have considered. The necessity and difficulty of developing decision aids point to a yet more challenging and overarching question—the nature of relationships between the tools of command and the human mind of commanders. There is a wellrespected genre of literature dedicated to the history and the relationships of technology and warfare (Boot 2006). A common theme of such works is the assertion that technology is important but generally subordinate to other nontechnological factors, such as tactics and training. In other words, technology is a collection of physical things, tools, artifacts, and as such it is entirely distinct and different from tactics, techniques, procedures, education, training, and other things that exist in the human mind. This view is misleading. More insightful definitions of technology stress that technology is not a collection of tools, but rather a know-how of techniques and processes. To explain how this applies to military technology, let us digress into a historical example of military technology. Consider tercio, a successful sixteenth-century invention of the Spanish military (Oman 1937). A formation of about 1,500 to 3,000 soldiers, it was composed of several mobile groups of musketeers and a square of pikemen. Combining firepower, the stability of heavy infantry, and the discipline of its well-trained professional soldiers, tercio was highly effective for over a century. Even though the primary technical implements of tercio were the pike and the musket, it would be misleading to identify them as the technology of tercio. The know-how definition of technology is much more useful. The tercio was a technology system, and its effectiveness as a technology was a product of the collective know-how of its soldiers and commanders: how to make and use pikes and muskets, how to form and operate the solid square and the mobile teams of musketeers, how to maintain discipline and control fear in the face of danger, and how to position and move the tercio. It was the systemic know-how embodied in an integration of the weapons’ hardware and the so-called software of human minds that constituted the technology of tercio. It was not merely the pike and the musket. Similarly, the technology of battle command is not its technical components—a network or a computer or battle-planning software. Instead, it is the collective know-how of the battle-command embodied in an integrated whole: tools with their hardware and software, and human minds with their techniques, procedures, and training. The oft-repeated arguments that differentiate military technology from tactics and training are misleading. The latter is a part of the former; they are an inseparable whole.

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This digression leads to two practical observations. First, as our MDC2 experience confirms, a useful approach to the development of a battlecommand system should focus on identifying and matching the cognitive needs of command-cell operators. The identification of such needs should be a key task of the development process and the focus of well-instrumented, rigorously designed experiments. The most critical needs are often related to cognitive limitations and biases, not obvious to developers and rarely known to the operators themselves (and sometimes even denied by them). For this reason, the true needs are best determined by experiments and not by compilations of preconceived requirements. It helps to think about the human mind not as a user of battle-command technology, but rather as an intrinsic part of such technology—certainly a unique and precious part, but a part nevertheless. The rest of the technology must be built around this unique, predefined component in a way that carefully matches its special strengths and weaknesses. The second observation is the importance of training as an intrinsic part of a battle-command technology. All too often, training is designed around the requirements of a tool. Instead, battle-command tools should be designed around the requirements and constraints of training and trainees. The heavy cognitive load of network-enabled warfare is one factor that amplifies concerns about training requirements. Another concern that became apparent in the course of the MDC2 program was the disproportionate decision-making load—and commensurate requirements for skills and training—on the relatively junior commanders and staffs of company-sized units. With the dispersion and relative independence of such units, and their complex set of assets, the relatively junior commander is responsible for a greater tempo and complexity of decisions than his more senior superiors at the battalion and brigade levels. Clearly, a new level of attention to designs and tools for training becomes mandatory. For example, the training system may focus more specifically on mitigating known cognitive limitations. Instrumentation and measurements similar to those employed in system development, such as used for situation awareness, can help measure the special needs and progress of an individual trainee. Unlike the laboratory-based stationary mock-ups of command vehicles used in MDC2, the trainees may benefit from a combination of a command cell’s live operation with realistically simulated behaviors of other assets. THE CHALLENGES OF NETWORK-ENABLED COMMAND Even with the advanced tools provided by the CSE, commanders and staff find it difficult to acquire and maintain adequate situation awareness. Misinterpretations of the available information, dismissal or inattention to the available information, and failure to collect the most critical information with the available sensing assets—all these are consistent tendencies of the MDC2

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commanders and staff, sometimes with catastrophic results to the Blue force. In view of the extremely strong role played by situation awareness in the success of a battle, this difficulty deserves a great deal of attention. Our experimental data suggest that situation awareness—as measured quantitatively using the instrumentation and techniques described in chapter 5—is the most influential factor in determining the success of a mission. More precisely, the critical factor is the difference between the situation awareness of the Blue command and the situation awareness of the Red command. With a greater positive difference, the Blue has greater chances of winning. Even the temporal dynamics of situation awareness are very influential. When the Blue force fails to develop a positive advantage in situation awareness—usually due to an unsuccessful counterreconnaissance battle—its inadequate situation awareness enters a self-reinforcing cycle that is rarely reversed. What, then, are the challenges of acquiring situation awareness? While the full answer must await further research, some culprits are fairly apparent. Part of the blame can be placed on the CSE tools, especially on the means of presenting the information to the operators. Further work on such tools must focus on more meaningful and insightful presentations than merely the display of icons on the map. More important, however, seem to be the operators’ psychological biases. Learning, for example, can be a double-edged sword. Having noticed a pattern of behavior displayed by the Red force in an earlier war game, the commander tends to “recognize” it in the current situation. With a creative, intelligent Red commander, however, the recognition is not always helpful. Instead, it can lead to an erroneous assessment of the Red situation or, worse yet, into a deception trap. Once a hypothesis is formed, the commander is reluctant to abandon it and tends to ignore or rationalize contradicting evidence. Also of note is the common and apparently unintentional tendency to look for new enemy targets at the expense of assessing the damage done to an already engaged target. Of a somewhat similar nature is what appears to be the near-obsessive behavior of a commander who eagerly watches for a change to appear on his screen— often a new enemy platform detected by Blue sensors—and then vigorously explores the new information in every detail and immediately proceeds to issue related commands. Instead of concentrating on the broader meaning of the unfolding battle, such a commander is absorbed in a potentially insignificant detail. Experiment observers refer to such behavior as missing the forest for the trees. In a related form of this behavior, the commander is paralyzed by an endless cycle of hunting for new information—as an enemy asset is detected, he calls for additional reconnaissance of the area while delaying any action, and so on. While only a few commanders exhibit such behaviors consistently, most succumb to them on occasion. Other challenges have more obvious and rational causes. For example, an important requirement for a force reliant on standoff engagements is to synchronize maneuver and information collection: the maneuver assets should not move into an area until it has been properly explored by sensing assets,

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such as UAVs or unmanned ground sensors, and cleared of enemy assets if any are found. Although the concept is fully understood by the commandcell operators, the proper execution of such synchronization turns out to be surprisingly difficult. In some cases, commanders slow down the force excessively in order to acquire more information, making it vulnerable to the Red’s long-range fires or dismounted attacks. In other cases, the commander rashly moves his force into an enemy-occupied territory and stumbles unintentionally into a direct-fire fight. Both failures can be exhibited by the same commander and even in the same war game. A big part of the cause here is simply the difficulty of assessing the time required to collect the information, given the nature of the area, the enemy assets, and available sensors. Additional tools and focused training may cure this problem. In addition to individual biases and limitations, the command cell’s operation as a team presents its own complications (Kott 2007). Conventional allocation of functions between the cell members is not necessarily optimal and may require adjustments as the battle unfolds. In particular, information management (including collection management and BDA) consumes a much greater fraction of the command cell’s efforts than in a conventional force. Unlike in a conventional force, commanders find that BDA has emerged as a highly demanding task, critical to proper situation awareness. Without effective BDA, the force slows down (becoming vulnerable) and engages a target multiple times (wasting assets). Generally, in our experiments, the fires manager is the least overloaded, followed by the maneuver manager, and the information manager, in order of increasing cognitive load. The commander himself often dedicates at least half of his time to information management as well. Experiments confirm that the CSE permits the commander to arrange alternative allocations of responsibilities, depending on situation and staff attrition, even during the fight. However, such a reorganization can be confusing when it has to be done in the midst of a high-tempo action. With the appropriate allocation of responsibilities and supported by effective collaboration tools—instant messaging, multiple radio frequencies, shared displays, graphics overlays, and a shared whiteboard—command cells can support each other both within a given cell and between cells. Experiments show many remarkable examples when collaboration enables widely dispersed small units to effectively support each other by sharing information, sensor resources, fires, and long-range munitions. Unfortunately, many other examples are less encouraging. In many cases, collaboration either distracts the commander from making critical decisions or induces him into accepting an erroneous understanding of the situation. The network-enabled command environment also seduces the staff and commanders into an excessive amount of discussion, often to the detriment of their direct responsibilities. Although our number of observations is not adequate for statistically significant conclusions, we find that a large fraction, possibly even a majority, of intercell collaborations result in diminished situation awareness for at least some of the collaborators. Clearly, the benefits of collaboration come with a substantial

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cost. To minimize such costs, future command cells will need specialized training, discipline, and protocols for collaborating. These should guide the proper frequency, conditions, and modes of collaboration. Still, with all the organization, training, procedures, and tools, battlecommand technology cannot and will not produce perfect situation awareness. This will remain the inescapable fact of warfare as long as it involves an intelligent enemy who works hard to disguise the situation from his opponents. Not only is perfect situation awareness impossible, it is also unnecessary. Recall that we find the key determinant of success is not the absolute level of situation awareness but the difference between the Red and Blue situation awareness levels. A modest measure of situation awareness suffices when the enemy is left with an even smaller measure. Critics of network-enabled warfare sometimes lampoon the concept by arguing that it relies on an impossibility—perfect intelligence (Kagan 2003). The argument is fallacious. Network-enabled warfare neither requires nor relies on perfect intelligence. In modern warfare, the fog of war is bound to grow thicker, and a key contribution of network-enabled approaches should be to enable operations under conditions of greater, not lower, levels of uncertainty in battlespace intelligence. The proliferation of technology and the gradual reduction in the technology gap between the United States and its adversaries, the urbanization of combat, the growing sophistication of irregular warfare practiced by the adversary, information warfare, and the rigorous adherence to the laws of war by U.S. forces—all these contribute to the thickening of the fog. More disturbing than the relatively low level of achievable situation awareness is the poor ability of commanders to self-assess their situation awareness. In our experiments, we find limited correlation between the actual situation awareness and the commander’s perception of his situation awareness. In some cases, the commander gloomily worries about unknown dangers while in fact possessing a nearly perfect picture of the enemy situation. In other cases, with a grossly misunderstood situation, the commander marches confidently into an ambush. Self-awareness seems even harder than the awareness of the enemy. Can some tools help in this matter? It appears doubtful. Is it possible that some yet unknown type of training will help? There is a particularly troubling possibility: what if the very nature of network-enabled command—with its massive flows of information, vivid displays, and challenged cognition—leads the commander to reduced self-awareness? Such doubts aside, developments in battle-command technologies can help the commander cope with the cognitive challenge, even if they cannot eliminate it. To explain this point, let us resort to another historical analogy— armored warfare. From the fifteenth-century battlewagons of Jan Zizka (see Oman 1960) to the present-day development of the Future Combat System, the struggle to provide warfighters with greater protection and lethality always demands greater weight and propulsion power, which in turn strains mobility and logistics. The progress of technology helps us reach increasingly

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better compromises between these conflicting demands but cannot eliminate the underlying conflict itself. Similarly, giving warfighters greater situation awareness demands greater flows of inevitably foggy information, which in turn taxes the commander’s cognitive processes at the expense of decision making. Although these conflicts cannot be eliminated, better technologies— in the broad sense of know-how—can and will help us find increasingly more effective compromises.

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Appendix: Terms, Acronyms, and Abbreviations

The terms here are defined in the way they are used in this book, which may differ from the usage accepted elsewhere. Some of the terms and abbreviations describe the systems used by the hypothetical Red and Blue forces in our experiments. In the experimental war games, the equipment of the Blue force was partly inspired by—but not identical to—the U.S. Army FCS family of systems. More information on FCS-related systems can be found at the FCS Web site (http://www.army. mil/fcs/). Also see the 2005 FCS Briefing Book at http://www.boeing.com/ defense-space/ic/fcs/bia/041029_2005flipbook.html. The equipment of the experimental Red force was modeled usually as upgrades of existing non-U.S. systems. Below, in describing such systems, we often refer to a comparable modern, currently existing system. The reader can find more information regarding the modern weapon systems at Web sites such as Wikipedia (www.wikipedia.org), Globalsecurity (www.globalse curity.org), and FAS (www.fas.org). AAEF: Air Assault Expeditionary Force AAR: After Action Review Abrams: the main battle tank used by the U.S. military ADA: Air Defense Artillery AGM: Attack Guidance Matrix AIB: Azeri Islamic Brotherhood (a fictional organization) airstrike: an attack by airborne assets on an enemy ground position AO: Area of Operations

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ARI: Army Research Institute ARK-1: see Ricebag ARK-1M ARV-A(L): Blue Armed Robotic Vehicle for Assault (light); a robotic, wheeled, light armored platform used by CAU for direct-fire support to infantry, with XM307 gun and multiple Javelin missiles, with acoustic, DVO, IR, LRF sensors; a hypothetical system partly inspired by the eponymous system of FCS ARV-RSTA: Blue Armed Robotic Vehicle for Reconnaissance, Surveillance, and Target Acquisition; a robotic, wheeled, light armored platform used by the CAU for remote reconnaissance and BDA, with XM307 gun, with acoustic, GSR, DVO, IR, LRF sensors; a hypothetical system partly inspired by the eponymous system of FCS asset: here an entity or a group of entities that serve significant military purpose (e.g., a tank, a soldier, a battalion) ASTAMIDS: Airborne Standoff Mine Detection System ATD: Automatic Target Detection attentional: pertaining to human attention battle cruiser: lightly armored but heavily armed warship battlespace: the entire environment in which a battle unfolds, including terrain and airspace BCSE: Battle Command Support Environment BCT: Brigade Combat Team BDA: Battle Damage Assessment BDAGM: Battle Damage Assessment Guidance Matrix BFA: Battlefield Functional Area Blue: refers to the friendly force BM: Battle Manager BSM: Battlespace Manager Bradley: a tracked armored infantry carrier vehicle used by the U.S. military BRDM-2: here a hypothetical upgrade of the modern Russian BRDM-2; Red lightarmored close-combat amphibious recon vehicle with DVO, IR, and radar sensors brigade: here a military unit of several thousand soldiers, including several CATs BTRA: Battlespace Terrain Reasoning and Awareness C2: Command and Control C2V: Blue Command and Control Vehicle is wheeled, fast, lightly armored and carries the commander and staff (four people plus the driver and the gunner), C2 computers and communication equipment onboard, DVO, IR, LRF and acoustic sensors, XM307 gun; inspired by the C2V of the FCS program C4ISR: Command, Control, Communications, Computers, Intelligence, Surveillance, and Reconnaissance CA/CB: Counterartillery/Counterbattery Radar CAT: Combined Arms Team catastrophic-kill: condition of an asset in which the asset has no useful combat functions

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CAU: Combined Arms Unit CCIR: Commander’s Critical Information Requirement CDR: Commander CEF: Collaboration Evaluation Framework cell: in this book, a command cell is a group of human decision makers (a commander and his staff) that collectively command a unit CEP: Circular Error Probable CFLCC: Coalition Forces Land Component Commander CFR: Counter-fire Radar CIM: Collective Intelligence Module CJMTK: Commercial/Joint Mapping Tool Kit CL I UAV: Blue Class I UAV provides RSTA to the dismounted warfighter; manportable, short-endurance, operates in complex urban and jungle terrains with a vertical takeoff and landing capability; inspired by the eponymous system of FCS CL II UAV: Blue Class II UAV has greater endurance and capabilities than Class I, with vertical takeoff and landing capability; used at CAU for reconnaissance, security/early warning, target acquisition and designation; carried by warfighters or on a vehicle; inspired by the eponymous system of FCS CL III UAV: Blue Class III UAV range and endurance to support RSTA at CAT and CAU echelons, with capabilities of the Class I and Class II UAVs but also serves for communications relay; mine detection; chemical, biological, radiological and nuclear detection; and meteorological survey; inspired by the eponymous system of FCS CL IV UAV: Blue Class IV long-range, long-endurance UAV carries COMINT, ELINT, MTI/SAR, or FOPEN SAR sensors; generally belongs to echelons above CAT; inspired by the eponymous system of FCS COA: Course of Action COL: Colonel COMINT: Communications Intelligence communications-kill: condition of an asset in which the asset is unable to communicate but can perform other functions CONOPS: Concept of Operations COP: Common Operating Picture counterfire: fire intended to neutralize or destroy enemy weapons, often in response to enemy fire CPOF: Command Post of the Future CS: Collaboration Server CSE: Commander Support Environment CTA: Counter-battery Target Acquisition DARPA: Defense Advanced Research Projects Agency Darya: Red self-propelling air-defense artillery system, with 35 mm cannon and surface-to-air missiles, tracked, medium armored; a hypothetical upgrade of the modern Russian 2S6 Tunguska

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DCSINT: Deputy Chief of Staff for Intelligence deployability: the ease with which an asset can moved at a significant distance DI: Dismounted Infantry DIL: Dismounted Infantry Leader DIU: Dismounted Infantry Unit DoD: Department of Defense Draega: Red heavily armored main battle tank, 152 mm gun, 30 mm gun, ATGMs, DVO and IR sensors; a hypothetical upgrade of the modern Russian T-90 Draega Decoy: Red static decoy that emulates the Draega system visually and by electronic and heat emissions DSS: Decision Support System DVO: Direct View Optics ELINT: Electronic Intelligence EM: Effects Manager entity: here a physical thing within the battlespace such as a tank, a soldier, a house, a civilian person EO: Electro-optical ERDC: the U.S. Army Engineer Research and Development Center exfiltrate: to move from enemy-held or hostile areas to areas under friendly control exploitation: transformation of raw data into useful information F-117A: a stealth ground-attack aircraft used by the U.S. military FCS: Future Combat System firepower-kill: condition of an asset in which the asset is unable to fire but can perform other functions fireteam: a small military unit, usually of four or fewer warfighters first detect: the initial acquisition of a target FLOT: Forward Line of Troops FOPEN: Foliage Penetrating Radar FRAGO: Fragmentary Order FTTS-MS: Blue supply carrier, Future Tactical Truck System for Maneuver Sustainment, wheeled, unarmored, can resupply robotic vehicles, with XM307 gun and acoustic, DVO, IR, LRF sensors; a hypothetical system partly inspired by the eponymous system of FCS Garm: Red infantry combat vehicle; tracked, heavy-medium armor, 45 mm cannon, ATGMs, DVO and IR sensors; a hypothetical upgrade of the modern Russian BTR-T GCM: Graphical Control Measure GDTA: Goal-Directed Task Analysis GPS: Global Positioning System GSR: Ground Surveillance Radar

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GSTAMIDS: Ground Standoff Mine Detection System HHQ: Higher Headquarters HIMARS: Blue High Mobility Artillery Rocket System; wheeled, unarmored, carries and fires multiple rockets of several types at a long distance in automatic or manual mode; operated by a crew of three; a hypothetical extension of the currently existing system (see http://www.army-technology.com/projects/himars/) HPT: High Payoff Target HQ: Headquarters HUD: Heads-Up Display ICV: Blue Infantry Carrier Vehicle; tracked, light-armored and carries up to a squad (nine persons) of warfighters, communications and C2 equipment, XM307 gun, with acoustic, target ranging, LRF, IR and DVO sensors; inspired by the eponymous system of FCS IED: Red concealed stationary mines of various designs, antipersonnel and antiarmor, activated by a remote observer or by autonomous sensors Igla MRL: Red multiple rocket launcher; a hypothetical upgrade of the modern Russian 9A52 Smerch IK: Interface Knowledge IM: Information (or Intelligence) Manager INET UGS: Internetted Unattended Ground Sensor IPB: Intelligence Preparation of the Battlefield IR: Infrared ISR: Intelligence, Surveillance, and Reconnaissance IUGS: hypothetical Internetted Unmanned Ground Sensors Systems; used by both Red and Blue forces for surveillance and target detection JROC: Joint Requirements Oversight Council KPP: Key Performance Parameters LAM: Blue Loitering Attack Munition; a missile that can attack light-to-heavy armored targets at great distances, loiter over the battlefield for a significant period of time before detecting and attacking the target, and be redirected in flight; a hypothetical system LD: Line of Departure LER: Loss Exchange Ratio LOC: Location logistics: planning and carrying out the movement and maintenance of military forces LOS: Line of Sight LOS weapon: a weapon effective only against an enemy located within line of sight from the weapon LRF: Laser Range Finder LRS: Long Range Surveillance LS: Launch System

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LTC: Lieutenant Colonel MAJ: Major MCS: Blue Maneuver Combat System is a robotic tracked, light-armored platform with 105 mm MRAAS and XM307 guns for close combat fire, with acoustic, GSR, DVO, IR, LRF sensors; a hypothetical system MDC2: Multicell and Dismounted Command and Control MDMP: Military Decision-Making Process MDT: Most Dangerous Targets METT-TC: Mission, Enemy, Troops, Terrain, Time, and Civilians MMR: Blue Multi-Mission Radar for counterfire, target acquisition, and air defense on robotic, wheeled, lightly armored vehicle with XM307 gun; a hypothetical extension of the current developmental MMR system mobility: an ability of the asset to move in space and to overcome obstacles mobility-kill: condition of an asset in which the asset is unable to move but can perform other functions MOE: Measures of Effectiveness Mohajer: Red reconnaissance UAV; twin-boom with pusher engine and DVO sensors; a hypothetical upgrade of the modern Iranian Mohajer 4 MoM: Measures of Merit MOP: Measures of Performance MPERM: Multi-Purpose Extended Range Munition MRAAS: Multi-Role Armament and Ammunition System (MRAAS) is a 105 mm lightweight gun for firing a broad range of munitions; a hypothetical system partly inspired by the eponymous system of FCS MRB: Motorized Rifle Battalion MTI: Moving Target Indicator MTLB ELINT/COMINT: Red electronic combat system; hypothetical equipment carried on the modern Russian MTLB tracked, light-armored vehicle MULE: Blue Multifunctional Utility/Logistics and Equipment; robotic wheeled vehicle supports dismounted operations by carrying warfighters’ equipment; can also carry countermine sensors GSTAMIDS and a minefield breaching plow; a hypothetical system partly inspired by the eponymous system of FCS multicell: refers to a force commanded by multiple command cells NAI: Named Area of Interest NCW: Network-Centric Warfare NKILO: Nagorno-Karabakh Internal Liberation Organization (a fictional organization) NLOS: Non-Line of Sight NLOS-C: Blue Non-Line of Sight Cannon; a robotic, light-armored, tracked platform with 155 mm howitzer for LOS and NLOS fires, with GSR, DVO, IR, LRF sensors; a hypothetical system NLOS-LS: Blue Non-Line of Sight Launch System; a robotic, wheeled unarmored vehicle, carries the launch unit with multiple missiles including PAMs and LAMs; a hypothetical system

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NLOS-M: Blue Non-Line of Sight Mortar; a robotic, tracked, light-armored platform with 120 mm mortar for short-range NLOS fires, with acoustic, GSR, DVO, IR, LRF sensors; a hypothetical system NLOS weapon: a weapon effective even against an enemy that is not located within line of sight from the weapon Nona: Red self-propelled 120 mm mortar, wheeled, light-armored; a hypothetical upgrade of the modern Russian 2S23 Nona-SVK NTC: National Training Center O&O: Organizational and Operational Plan OAEF: Operation Enduring Freedom OBJ: Objective OIF: Operation Iraqi Freedom OneSAF: One Semi-Automated Force OODA: Observe, Orient, Decide, Act OPFOR: Opposing Force OPORD: Operations Order ORD: Operational Requirements Document Orel: Red reconnaissance and combat vehicle; tracked, heavy-medium armor, 45mm cannon and ATGMs, DVO and IR sensors; a hypothetical upgrade of the modern Russian BRM-3 OSD: Office of the Secretary of Defense OTB: OneSAF Tested Baseline overwatch: the state of one unit supporting another while executing fire and movement tactics PAM: Blue Precision Attack Munition; a missile capable of attacking heavy-armor and other targets many kilometers away with great precision; can be redirected inflight; a hypothetical system PEO: Program Executive Office PL: Platoon or Platoon Leader; alternatively, Phase Line platform: here a military vehicle, ground based or airborne, capable of carrying warfighters, weapons, or sensors PM: Project Manager PSE: Platform Support Environment Purga Decoy: Red static decoy that emulates the Purga system visually and by electronic emissions Purga: Red self-propelled, tracked, medium-armored 150 mm howitzer; a hypothetical upgrade of the modern Russian 2S19 Msta R&D: Research and Development R&S: Reconnaissance and Surveillance RDEC: Research and Development Center reasoner: a software component that performs functions reminiscent of human reasoning

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recon: reconnaissance Red: refers to an enemy force RedSEM: Red Sensor Effects Module resupply: replenishing stocks in order to maintain required levels of supply retasking: assigning a new or modified task to an asset RFA: Restricted Fire Area Ricebag ARK-1M: Red counterbattery artillery-locating radar on a tracked, mediumarmored platform; a hypothetical upgrade of the modern Russian ARK-1M Rys ROE: Rules of Engagement RPG: Rocket-Propelled Grenade RPG-22: Red infantry’s and insurgent’s man-portable rocket-propelled grenade launcher; a hypothetical upgrade of the modern Russian RPG-22 RSTA: Reconnaissance, Surveillance, and Target Acquisition SA: Situation Awareness SA-13: Red mobile, short-range, low-altitude air-defense surface-to-air missile system, tracked, medium armored; a hypothetical upgrade of the modern Russian 9K35 Strela-10 SA-15 Decoy: Red static decoy that emulates the SA-15 system visually and by electronic emissions SA-15: Red mobile, low-to-medium-altitude air-defense surface-to-air missile system, tracked, medium armored; a hypothetical upgrade of the modern Russian 9K330 Tor SA-18: Red man-portable surface-to-air missile system; a hypothetical upgrade of the modern Russian 9K38 Igla SAc: Situation Awareness: Cognitive SAF: Semi-Automated Forces SAGAT: Situation Awareness Global Assessment Technique SAM: Surface-to-Air Missile SAR: Synthetic Aperture Radar SASO: Stability and Support Operation SAt: Situation Awareness: Technical SCT-TM: Scout Team SEM: Sensor Effects Module sensor: a device that responds to a stimulus, such as heat or light, and generates a signal SINCGARS: Single Channel Ground and Airborne Radio System SK: System Knowledge SME: Subject Matter Expert SOF: Special Operations Forces SP: Self-Propelled SPF: Special Purpose Forces

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SSA: SAR Search Area SSE: Soldier Support Environment staff: assistants to the commander who process information and may, under the guidance of the commander, make decisions and issue commands to subordinate assets STRI: Simulation, Training, and Instrumentation subgoal: a goal that has to be met in order to accomplish a broader goal SUGV: Blue Small Unmanned Ground Vehicle; robotic, tracked, unarmored, manportable, controlled by an infantry squad, mounts and dismounts the ICV, used for reconnaissance with acoustic, DVO, IR and LRF sensors; inspired by the eponymous system of FCS SVS: Soldier Virtual System symbology: a set of symbols and the rules for the use of the symbols TacFire: an automated artillery fire direction system used at one time by the U.S. military targetable: refers to an enemy asset that meets the specified requirements for designation as a target tasking: assigning a task to an asset testbed: a system in which experimental tools and products may be deployed and allowed to interact threat: here an enemy asset TM: team TOC: Tactical Operations Center TRAC: TRADOC Analysis Center TRADOC: Training and Doctrine Command TTPs: Tactics, Techniques, and Procedures UAV: Unmanned Aerial Vehicle UE: Unit of Employment; a division-like military organization that includes several BCTs UGS: Unattended Ground Sensor UGV: Unmanned Ground Vehicle unit: here a military organization such as a CAU, a CAT, a brigade. Ural: Red supply-carrier truck; a hypothetical upgrade of the modern Russian Ural 4320–31 U.S.: United States VBIED: Red IED concealed in a civilian vehicle, stationary or moving, and activated by a remote observer, autonomous sensors, or the suicide driver VDSF: Viecore Decision Support Framework VSE: Vehicle Support Environment warfighter: an armed forces member engaged in combat against an enemy force warfighting: actions against an enemy force

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war game: a simulation (manual or computerized) of a contest between opposing forces XM307: Blue 25 mm, belt-fed grenade machine gun with laser range finder and day/night sight; can be robotically operated, lethal against personnel and lightly armored vehicles, man-portable, able to reach into foxholes, behind rocks and walls; a hypothetical improvement of the current developmental XM307 system

Acknowledgments

Not only is this book largely inspired by two research programs, but it also borrows heavily from the programs’ reports and archives. This places the authors in heavy debt to a very large number of people who conceived the programs, built experimental systems, conducted experiments, analyzed the data, and generated many of the ideas we attempted to present in this work. The authors are also acknowledged as important leaders, contributors, and participants in the many activities that are the basis for this book. Unfortunately for the authors, a policy of the U.S. Department of Defense restricts our ability to mention by name—for obvious reasons—the department’s military and civilian personnel who contributed to this program. This in no way diminishes our gratitude and appreciation of their enormous efforts. The best we can do in such cases is to mention the organizations for which these contributors and supporters work. A number of senior leaders of the U.S. Army deserve deep thanks for encouraging, motivating, and sponsoring this research. We are able to mention only a few of them, particularly retired generals Eric Shinseki and Kevin Byrnes. The goals and vision of our work greatly benefited from the experience and wisdom of James Barbarello, Allan Tarbell, John Gilmore, Paul Casselburg, General (retired) David Maddox, and Colonel (retired) Greg Fontenot. Critical operational concepts were provided by Joe Braddock, Lou Marquet, and James Tegnalia; retired generals Paul Gorman, Paul Funk, and Huba Wass de Czege; and retired colonels Ted Cranford, Brooks Lyles, Jack Gumbert, and Dave Redding. Multiple military leaders, managers, and analysts at the Army TRADOC provided continued sponsorship, guidance, and liaison with the army development community. Faculty, cadets, and interns of

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the USMA provided useful studies. Contributions were also provided by the Naval Postgraduate School and the Army Research Lab. Experimental battle command systems and the simulation testbed required a broad range of technologies. Mark Curry, Diane Oconnor, Tom Ince, Rob Lawrence, Craig Klementowski, and Digant Modha of the Viecore Federal Systems Division led the development of the Battle Command Support Environment. John Sausman of Lockheed Martin provided Dismounted Infantry Behaviors software. The U.S. Army Communications-Electronics Research, Development and Engineering Center’s Information and Intelligence Warfare Directorate helped us with the Synthetic Aperture Radar Model, while the Night Vision & Electronic Sensors Directorate supplied the Mine/Countermine Server. Ralph Forkenbrock, Jim Page, Ray Miller, and Mike Dayton of Science Applications International Corp. greatly enhanced the OTB simulation system and the Driver-Gunner Simulation model. The Army Topographic Engineering Center provided the crucial Terrain Server. John Huebner and John Roberts of Atlantic Consulting Services Inc. developed the very useful C2 Tasking Library. Mark Berry and Jim Adametz of Computer Sciences Corp. led the development of the Sensor Effects Model. Integration of such complex systems—and the management of the required multifaceted engineering efforts—were ably handled by the Army Research, Development and Engineering Command; the Army CommunicationsElectronics Command; and the Army Program Executive Office for Simulation, Training and Instrumentation. A great fraction of the efforts in this research was dedicated to experiment design, experiment execution, data collection, and analysis. We are grateful to Darrin Meek, LeeAnn Bongiorno, and retired colonels Steve Williams and Todd Sherrill of Applied Research Associates Inc. for their contributions to the Sensor Coverage Tool and data collection systems; to Don Timian and Rick Hyde of Northrop Grumman for Experiment 1 and 2 design and collection plans; to the researchers of Army Research Institute for human factors performance analysis; to James Hillman and Andrea Kagle of Johns Hopkins University-Applied Physics Lab for the information exchange requirements analysis. Other important contributions to the experimental design and analysis have been made by Beth Meinert and Colonel (retired) Robert Chadwick of the MITRE Corp. and by personnel of the Army Training and Doctrine Command Analysis Center. The extensive laboratory infrastructure that housed and supported the experiments was the work of Jim Seward and Manish Bhatt of David H. Pollack Consulting. Execution of the experiments, particularly the portrayal of the Red force and the after-action reviews for the Blue force, were made possible by the talents of retired colonels Darrell Combs and Al Rose, and their colleagues from Military Professional Resources Inc. The primary funding for this research has been provided by DARPA and by the U.S. Army. Happily, we are allowed to mention and to thank the DARPA leaders and managers who made this work possible: Frank Fernandez, Tony Tether, Dick Wishner, Ted Bially, David Whelan, and Allan Adler. DARPA

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also granted us the permission to use the materials on which this book is partly based; it has been approved for public release, distribution unlimited. The work reflected in chapter 4 was supported through Army Research Laboratory’s Advanced Decision Architectures Collaborative Technology Alliance. Of course, the views, opinions, and findings presented here are those of the authors and should not be construed as those of any agency or organization of the U.S. government. Finally, special thanks to Susan Parks, Scott Fuhrer, James Scrocca, Terry Stephenson, and Michael Ownby who supported this effort in numerous ways.

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Notes

INTRODUCTION 1. Coevolution of technology and warfare is a topic of many excellent studies. A recent example is Max Boot, War Made New (New York: Gotham Books, 2006). 2. A highly influential work is David S. Alberts, John J. Garstka, and Frederick P. Stein, Network Centric Warfare: Developing and Leveraging Information Superiority (Washington, DC: CCRP, 2000). 3. Adoption of unmanned aerial vehicles by all services of the U.S. military has been rapid and rather noncontroversial. A readable introductory history is offered in Laurence R. Newcome, Unmanned Aviation: A Brief History of Unmanned Aerial Vehicles (Reston, VA: AIAA [American Institute of Aeronautics], 2004). 4. U.S. Army, FCS Web site, http://www.army.mil/fcs/. 5. Discussed in A. Bacevich, The Pentomic Era: The U.S. Army Between Korea and Vietnam (Darby, PA: DIANE Publishing Co., 1995). 6. Congressional Budget Office, “The Army’s Future Combat Systems Program and Alternatives,” August 2006, p. XXII. 7. DARPA Web site, http://www.darpa.mil/. 8. Max Boot, War Made New, p. 463. 9. J. Gumbert, T. Cranford, T. Lyles, and D. Redding, “DARPA’s Future Combat System Command and Control,” Military Review (May–June 2003): 79–84. 10. For the sake of brevity, we will refer to the combination of these two programs as MDC2. 11. J. Barbarello, M. Molz, and G. Sauer, “Multicell and Dismount Command and Control—Tomorrow’s Battle Command Environment Today,” Army AL&T (July– August 2005): 66–71. 12. For the sake of simplicity and brevity, we use he when referring to a commander or a staff member. This is not to imply the gender of the person.

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CHAPTER 1 The material in this chapter draws extensively on a report from Carrick Communications Inc., which has kindly given permission for its use. 1. Histories and analyses of Jutland and its subsequent controversies are legion. A very readable online summary of the battle is found at http://www.worldwar1.co.uk/ jutland.html. 2. Andrew Gordon, The Rules of the Game: Jutland and British Naval Command (London: John Murray Publisher Ltd., 2000). 3. U.S. Army, “Battle Command,” in 2003 U.S. Army Transformation Roadmap, http://www.army.mil/2003TransformationRoadmap. 4. Robert Coram, Boyd: The Fighter Pilot Who Changed the Art of War (New York: Little, Brown and Co., 2002), pp. 327–44. 5. Carl von Clausewitz, On War, ed. Michael Howard and Peter Paret (Princeton, NJ: Princeton University Press, 1976), pp. 101–2. 6. As one highly regarded military theorist stated, “The purpose of discipline is to make men fight in spite of themselves.” Charles Ardant du Picq, Battle Studies, trans. Col. John N. Greely and Maj. Robert C. Cotton, 1921, http://www.gutenberg. org/dirs/etext05/8btst10.txt. 7. For more discussion of this challenge, see, for example, Victor Davis Hanson, “Discipline,” in Reader’s Companion to Military History, http://college.hmco.com/his tory/readerscomp/mil/html/mh_015100_discipline.htm. 8. Clausewitz, On War, p. 113. 9. Gordon, The Rules of the Game, p. 21. 10. Clausewitz, On War, p. 101. 11. For a perceptive analysis of two revealing cases of such commander–subordinate disconnection, see Col. Adolf Carlson, A Chapter Not Yet Written: Information Management and the Challenge of Battle Command (Washington, DC: Institute for National Strategic Studies, 1995), http://www.ndu.edu/inss/siws/ch5.html. 12. Clausewitz, On War, p. 120. 13. Atul Gawande, Complications: A Surgeon’s Notes on an Imperfect Science (New York: Henry Holt, 2002). 14. Gordon C. Rhea, The Battle of the Wilderness May 5–6, 1864 (Baton Rouge: Louisiana State University Press, 1994). 15. Quoted in Martin van Creveld, Command in War (Boston: Harvard University Press, 1985), p. 153. Contrast this with Ulysses S. Grant’s view that “the distant rear of an army engaged in battle is not the best place from which to judge correctly what is going on in front,” Ulysses S. Grant, Personal Memoirs (New York: Penguin Books, 1999), p. 185. 16. Timothy Lupfer, The Dynamics of Doctrine: The Changes in German Tactical Doctrine during the First World War (Fort Leavenworth, KS: U.S. Army Command and General Staff College, 1981). See also van Creveld, Command in War, pp. 183–84. 17. At Spottsylvania in 1864, for example, it prompted a bitter dispute between Union generals George Meade and Philip Sheridan that finally had to be resolved by Grant himself. See, for example, Bruce Catton, A Stillness at Appomattox (New York: Doubleday & Company, 1953), pp. 99–100. 18. Also Russia, but Stalin’s prewar purge of his officer corps largely stifled practical implementation, as early Soviet defeats demonstrated only too starkly. 19. Ulysses S. Grant, attributed (http://en.wikiquote.org/wiki/Ulysses_S._Grant).

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20. Field Marshal The Viscount Slim, Defeat into Victory (Philadelphia: David McKay Company, 1961), p. 460. 21. TRADOC Pamphlet 525–3–0, The Army in Joint Operations: The Army Future Force Capstone Concept (Fort Monroe, VA: U.S. Army Training and Doctrive Command, April 7, 2005). 22. Clausewitz, On War, p. 77. 23. As an NCO in Iraq recently stated, “You know that mission we had all planned out? That all just went to s—t.” Margaret Friedenauer, “Soldiers Employ Daring Tactic,” Fairbanks Daily News–Miner, December 21, 2005. 24. van Creveld, Command in War, p. 8. 25. van Creveld, Command in War, pp. 255–56. 26. “The Leadership Legacy of John Whyte,” ARMY, December 2005, p. 64. 27. Army Field Manual 3.0, Operations (Washington, DC: Department of the Army, June 2001), pp. 4–17. Debate persists about whether the term should be replaced by orchestrating to diminish what some see as an unhealthy fixation on scheduling. 28. Mark Adkin, The Charge: Why the Light Brigade Was Lost (South Yorkshire, UK: Leo Cooper, 1996), pp. 125–37. 29. Bill Mauldin, Up Front (New York: W.W. Norton & Co., 2000), p. 225. 30. Col. (Ret) Gregory Fontenot, E. J. Degen, and David John, On Point: The United States Army in Operation Iraqi Freedom (Fort Leavenworth, KS: Combat Studies Institute Press, 2004), p. 220. 31. Clausewitz, On War, p. 119. 32. One of Clausewitz’s modern successors went so far as to argue economy of force to be the foundation of all other principles of war. See J.F.C. Fuller, The Generalship of Ulysses S. Grant (Cambridge, MA: Da Capo Press, 1991), p. 18. 33. In one of his less-quoted comments, Moltke warned that an error in initial deployment might well prove irremediable. He was speaking of operations, but the problem is no less acute for the tactical commander. 34. Clausewitz, On War, p. 75. 35. Col. David Perkins, “Command Briefing,” May 18, 2003, quoted in Fontenot, On Point, p. 295. 36. Maj. John Altman, quoted in Fontenot, On Point, p. 284. 37. Charles B. Macdonald and Sidney T. Matthews, Three Battles: Arnaville, Altuzzo, and Schmidt (Washington, DC: Office of the Chief of Military History, Department of the Army, 1952), pp. 268–71. 38. For the account prompting the comment, see Correlli Barnett, The Desert Generals (Bloomington: Indiana University Press, 1982). 39. An excellent treatment is Donald W. Engels, Alexander the Great and the Logistics of the Macedonian Army (Berkeley: University of California Press, 1980). 40. Quoted in Martin van Creveld, Supplying War: Logistics from Wallenstein to Patton (Cambridge, MA: Cambridge University Press, 1977), p. 232. 41. Fontenot, On Point, pp. 408–9. 42. Chief of Staff, Army Warfighter Conference, Washington, DC, July 1984. The writer was present. 43. Office of the Inspector General, “No Gun Ri Review” (Washington, DC: Department of the Army, January 2001). 44. Proportionately, “human wave” attacks during the 1980–1988 Iran–Iraq War may have come close. See, for example, Efraim Karsh, The Iran-Iraq War 1980–1988 (Oxford, UK: Osprey Publishing Ltd., 2002), pp. 35–36.

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45. For a current example, see Sally B. Donnelly, “Long–Distance Warriors,” Time, December 12, 2005. 46. Several accounts of this incident have been published. One of the better discussions is in Lt. Col. John G. Humphries, “Operations Law and the Rules of Engagement in Operations Desert Shield and Desert Storm,” Airpower Journal, Fall 1992. 47. See, for example, “Pentagon Justifying Incendiary Arms Use,” New York Times, November 17, 2005. 48. Carlson, A Chapter Not Yet Written. 49. Richard Sparshatt and Col. Nick Justice, “Future Battle Command and Control System,” http://www.agile-com.net/agile/documents/FC2S9.pdf. 50. 2003 U.S. Army Transformation Roadmap, pp. 2–5. 51. van Creveld, Command in War, p. 261. 52. Stephen Vincent Benet, John Brown’s Body (Cutchogue, NY: Buccaneer Books Inc., 1986), p. 82.

CHAPTER 2 1. Not a real name. When referring to the future, all names, characters, organizations, places, and incidents featured in this publication are either the product of the authors’ imaginations or are used fictitiously. 2. David L. Grange, Huba Wass De Czege, Richard D. Liebert, John E. Richards, Charles A. Jarnot, Allen L. Huber, and Emery E. Nelson, Air-Mech-Strike: Asymmetric Maneuver Warfare for the 21st Century, ed. Michael L. Sparks (Padukah, KY: Turner Publishing Company, 2002). 3. B. Berkowitz, The New Face of War (New York: The Free Press, 2003), pp. 111–15 4. One example is David S. Alberts, John J. Garstka, and Frederick P. Stein, Network Centric Warfare: Developing and Leveraging Information Superiority (Washington, DC: CCRP, 2000). 5. U.S. Army, FCS Web site, http://www.army.mil/fcs/. 6. Congressional Budget Office, “The Army’s Future Combat Systems Program and Alternatives,” August 2006, pp. 35–39. 7. P.A. Wilson, J. Gordon, and D. E. Johnson, “An Alternative Future Force: Building a Better Army,” Parameters (winter 2003–2004): 19–39. 8. Congressional Budget Office, “The Army’s Future Combat Systems Program and Alternatives,” pp. 31–32. 9. Congressional Budget Office, “The Army’s Future Combat Systems Program and Alternatives,” pp. 40–43. 10. Congressional Budget Office, “The Army’s Future Combat Systems Program and Alternatives,” pp. 44–45. 11. F. Kagan, “War and Aftermath,” Policy Review, August-September 2003, http:// www.hoover.org/publications/policyreview/3448101.html. 12. U.S. Army, Army assessment of Congressional Budget Office study “The Army’s Future Combat Systems Program and Alternatives,” http://www.army.mil/fcs/. 13. J. Gumbert, T. Cranford, T. Lyles, and D. Redding, “DARPA’s Future Combat System Command and Control,” Military Review (May–June 2003): 79–84. 14. U.S. Army, TRADOC Pamphlet 525–3-90, O & O, The United States Army Future Force Operational and Organizational Plan for the Unit of Action (Fort Knox, KY: Unit of Action Maneuver Battle Lab, December 15, 2004).

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15. Boeing, FCS 2005 Flipbook 2005, http://www.globalsecurity.org/military/ library/report/2005/050000-fcs2005flipbook.pdf. 16. J. Barbarello, M. Molz, and G. Sauer, “Multicell and Dismount Command and Control—Tomorrow’s Battle Command Environment Today,” Army AL&T ( July– August 2005): 66–71. 17. Caspian Sea Scenarios, http://www.defenselink.mil/news/Apr2002/ n04292002_200204293.html. 18. OneSAF Testbed Web site, http://www.onesaf.org/onesafotb.html. 19. U.S. Army Field Manual 3.0, Operations (Washington, DC: U.S. Government Printing Office, June 2001), pp. 4–10. 20. Martin van Creveld, Command in War (Cambridge, MA: Harvard University Press), pp. 265–68. 21. Martin van Creveld, “Command in War: A Historical Overview,” in A. Kott, ed., Advanced Technology Concepts for Command and Control (Philadelphia: Xlibris, 2004), pp. 33–36. 22. Martin van Creveld, Art of War (London: Cassell, 2000). 23. J. Galbraith, “Organization Design: An Information Processing View,” Interfaces 4 (May 1974): 28–36.

CHAPTER 3 1. Gheorghe Tecuci, Building Intelligent Agents. An Apprenticeship Multistrategy Learning Theory, Methodology, Tool and Case Studies (San Diego, CA: Academic Press, 1988), p. 1. 2. OneSAF.org, http://www.onesaf.org/onesafotb.html (accessed October 12, 2006). 3. CJMTK, “What is the CJMTK?” 2006, http://www.cjmtk.com//Faq/FaqMain. aspx#Q1 (accessed September 20, 2006). 4. Michael Powers, “Battlespace Terrain Reasoning and Awareness (BTRA),” 2003, http://www.tec.army.mil/fact_sheet/BTRA.pdf (accessed October 12, 2006). 5. Rich Bormann, “A Decision Support Framework for Command and Control in a Network Centric Warfare Environment,” technical report (Eatontown, NJ: Viecore, 2006). 6. Haley Systems Inc., “Rete Algorithm,” 2006, http://www.haley.com/28147578 2021120/brmsoverview/retereport.html (accessed September 20, 2006). 7. Haley Systems Inc., “HaleyRules: Business Rules Engine,” 2006, http://www.haley. com/1548387250868224/products/HaleyRules.html (accessed October 12, 2006). 8. Production Systems Technologies Inc., “Clips/R2,” 2003, http://www.pst.com/ clpbro.htm (accessed October 12, 2006).Figure 3.5a. The SSE provides C2 and decision support to the dismounted warfighter.Figure 3.10. The use of automated code generation minimizes development time and maximizes code reuse.

CHAPTER 4 Bolstad, C. A., and M. R. Endsley. 1999. Shared Mental Models and Shared Displays: An Empirical Evaluation of Team Performance. Proceedings of the 43rd Annual Meeting of the Human Factors and Ergonomics Society, Houston, TX, Human Factors and Ergonomics Society, September 27–October 1, pp. 213–17. Bolstad, C. A., and M. R. Endsley. 2000. The Effect of Task Load and Shared Displays on Team Situation Awareness. Proceedings of the 14th Triennial Congress of

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the International Ergonomics Association and the 44th Annual Meeting of the Human Factors and Ergonomics Society, Santa Monica, CA, Human Factors and Ergonomics Society, July 30–August 4, pp. 189–92. Bolstad, C. A., and M. R. Endsley. 2003. Measuring Shared and Team Situation Awareness in the Army’s Future Objective Force. Proceedings of the Human Factors and Ergonomics Society 47th Annual Meeting, Denver, CO, Human Factors and Ergonomics Society, October 13–17, pp. 369–73. Bolstad, C. A., J. M. Riley, D. G. Jones, and M. R. Endsley. 2002. Using Goal Directed Task Analysis with Army Brigade Officer Teams. Proceedings of the 46th Annual Meeting of the Human Factors and Ergonomics Society, Baltimore, MD, Human Factors and Ergonomics Society, September 30–October 4, pp. 472–76. Collier, S. G., and K. Folleso. 1995. SACRI: A Measure of Situation Awareness for Nuclear Power Plant Control Rooms. In Experimental Analysis and Measurement of Situation Awareness, ed. D. J. Garland and M. R. Endsley, pp. 115–22. Daytona Beach, FL: Embry-Riddle University Press. Dyer, J. L., R. J. Pleban, J. H. Camp, G. H. Martin, D. Law, S. M. Osborn, et al. 1999. What Soldiers Say about Night Operations. In Volume 1: Main Report (No. ARI Research Report 1741). Alexandria, VA: Army Research Institute for the Behavioral and Social Sciences. Endsley, M. R. 1988. Design and Evaluation for Situation Awareness Enhancement. Proceedings of the Human Factors Society 32nd Annual Meeting, Anaheim, CA, Human Factors Society, October 24–28, pp. 97–101. Endsley, M. R. 1990. Predictive Utility of an Objective Measure of Situation Awareness. Proceedings of the Human Factors Society 34th Annual Meeting, Orlando, FL, Human Factors Society, October 8–12, pp. 41–45. Endsley, M. R. 1995a. Direct Measurement of Situation Awareness in Simulations of Dynamic Systems: Validity and Use of SAGAT. In Experimental analysis and measurement of situation awareness, ed. D. J. Garland and M. R. Endsley, pp. 107–13. Daytona Beach, FL: Embry-Riddle University. Endsley, M. R. 1995b. Measurement of Situation Awareness in Dynamic Systems. Human Factors 37(1): 65–84. Endsley, M. R. 1995c. Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors 37(1): 32–64. Endsley, M. R. 1996. Situation Awareness Measurement in Test and Evaluation. In Handbook of Human Factors Testing and Evaluation, ed. T. G. O’Brien and S. G. Charlton, pp. 159–80. Mahwah, NJ: Lawrence Erlbaum. Endsley, M. R. 2000. Direct Measurement of Situation Awareness: Validity and Use of SAGAT. In Situation awareness analysis and measurement, ed. M. R. Endsley and D. J. Garland, pp. 147–74. Mahwah, NJ: Lawrence Erlbaum Associates. Endsley, M. R., and C. A. Bolstad. 1994. Individual Differences in Pilot Situation Awareness. International Journal of Aviation Psychology 4(3): 241–64. Endsley, M. R., B. Bolte, and D. G. Jones. 2003. Designing for Situation Awareness: An Approach to Human-Centered Design. London: Taylor and Francis. Endsley, M. R., and D. J. Garland, eds. 2000. Situation Awareness Analysis and Measurement. Mahwah, NJ: Lawrence Erlbaum. Endsley, M. R., and W. M. Jones. 1997. Situation Awareness, Information Dominance, and Information Warfare (No. AL/CF-TR-1997-0156). WrightPatterson AFB, OH: United States Air Force Armstrong Laboratory.

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Endsley, M. R., and W. M. Jones. 2001. A Model of Inter- and Intrateam Situation Awareness: Implications for Design, Training and Measurement. In New Trends in Cooperative Activities: Understanding System Dynamics in Complex Environments, ed. M. McNeese, E. Salas, and M. Endsley, pp. 46–67. Santa Monica, CA: Human Factors and Ergonomics Society. Endsley, M. R., and E. O. Kiris. 1995. The Out-of-the-Loop Performance Problem and Level of Control in Automation. Human Factors 37(2): 381–94. Endsley, M. R., and M. M. Robertson. 2000. Training for Situation Awareness in Individuals and Teams. In Situation Awareness Analysis and Measurement, ed. M. R. Endsley and D. J. Garland. Mahwah, NJ: LEA. Endsley, M. R., S. J. Selcon, T. D. Hardiman, and D. G. Croft. 1998. A Comparative Evaluation of SAGAT and SART for Evaluations of Situation Awareness. Proceedings of the Human Factors and Ergonomics Society Annual Meeting, Chicago, Human Factors and Ergonomics Society, October 5–9, pp. 82–86. Gugerty, L. J. 1997. Situation Awareness during Driving: Explicit and Implicit Knowledge in Dynamic Spatial Memory. Journal of Experimental Psychology: Applied 3: 42–66. Hockey, G.R.J. 1986. Changes in Operator Efficiency as a Function of Environmental Stress, Fatigue and Circadian Rhythms. In Handbook of perception and performance, vol. 2, ed. K. Boff, L. Kaufman, and J. Thomas, pp. 44/41–49. New York: John Wiley. National Research Council. 1997. Tactical Display for Soldiers. Washington, DC: National Research Council. Sharit, J. and G. Salvendy. 1982. Occupational Stress: Review and Reappraisal. Human Factors 24(2): 129–62. Strater, L. D., D. Jones, and M. R. Endsley. 2003. Improving SA: Training Challenges for Infantry Platoon Leaders. Proceedings of the 47th Annual Meeting of the Human Factors and Ergonomics Society, Denver, CO, Human Factors and Ergonomics Society, October 13–17, pp. 2045–49. U.S. Army. 2001. Concepts for the Objective Force. Washington, DC: U.S. Army.

CHAPTER 5 Brownlee, Les, and Peter J. Schoomaker. 2004. “Serving a Nation at War: A Campaign Quality Army with Joint and Expeditionary Capabilities.” Parameters 34, no. 2 (Summer):18. Klein, G. A., R. Calderwood, and D. MacGregor. 1989. Critical Decision Method for Eliciting Knowledge. IEEE Transactions on Systems, Man, and Cybernetics 19(3): 462–72. Woods, David D. 1993. Process Tracing Methods for the Study of Cognition Outside of the Experimental Psychology Laboratory. In Decision Making in Action: Models and Methods, ed. G. Klein, J. Orasanu, R. Calderwood, and C. Zsambok, pp. 228–51. Norwood, NJ: Ablex Publishing Corporation.

CHAPTER 6 Cheikes, B. A., M. J. Brown, P. E. Lehner, and L. Alderman. 2004. Confirmation Bias in Complex Analysis. Technical Report No. MTR 04B0000017. Bedford, MA: MITRE.

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Endsley, Mica R. 2000. “Theoretical Underpinings of Situation Awareness—A Critical Review.” In Situation Awareness Analysis and Measurement, ed. Mica R. Endsley and Daniel J. Garland. Mahwah, NJ: Lawrence Erlbaum Associates. Endsley, Mica R., and D. G. Jones. 1995. Situation Awareness Requirements Analysis for TRACON Air Traffic Control (TTU-IE-95-01). Lubbock: Texas Tech University. Endsley, Mica R., Cheryl A. Bolstad, Debra G. Jones, and Jennifer M. Riley. 2003. “Situation Awaremess Oriented Design: From User’s Cognitive Requirements to Creating Effective Supporting Technologies.” Proceedings of the 47th Annual Meeting of the Human Factors & Ergonomics Society. Human Factors & Ergonomics Society, Santa Monica, CA, 268–72. Jones, Debra G., and Mica R. Endsley. 1996. “Sources of Situation Awareness Errors in Aviation.” Aviation, Space and Environmental Medicine 67(6): 507–12. Jones, Debra G., and Mica R. Endsley. 2000. “Overcoming Representational Errors in Complex Environments.” Human Factors 42(3): 367–78. Miller, Nita L., and Lawrence G. Shattuck. 2004. “A Process Model of Situated Cognition in Military Command and Control.” Paper presented at the Command and Control Research and Technology Symposium, San Diego, CA, May 2004. Nickerson, R. S. 1998. “Confirmation Bias: A Ubiquitous Phenomenon in Many Guises.” Review of General Psychology 2: 175–220. Woods, D. D., L. Johannesen, R. I. Cook, and N. B. Sarter. 1994. Behind Human Error: Cognitive Systems, Computers, and Hindsight (CSERIAC SOAR Report 94-01). Wright-Patterson Air Force Base, OH: Crew Systems Ergonomic Information and Analysis Center.

CHAPTER 7 Brehmer, B. 1991. Organization for Decision Making in Complex Systems. In Distributed Decision Making: Cognitive Models for Cooperative Work, ed. J. Rasmussen, B. Brehmer, and J. Leplat. New York: Wiley and Sons. Clark, H. H. 1996. Using Language. New York: Cambridge University Press. Endsley, M. R. 1995. Toward a Theory of Situation Awareness in Dynamic Systems. Human Factors 37(1): 32–64. Field Manual 6–0. 2003. Battle Command: Command and Control of Army Forces. Washington, DC: Headquarters, Department of the Army. Flake, G. W. 1998. The Computational Beauty of Nature. Cambridge, MA: MIT Press. Garstka, J., and D. Alberts. 2004. Network Centric Operations Conceptual Framework Version 2. Vienna, VA: Evidence Based Research. Katz, D., and R. L. Kahn. 1978. The Social Psychology of Organizations. New York: Wiley. Klein, G. A. 1999. Sources of Power. Cambridge, MA: MIT Press. Rasmussen, J., A. Pejtersen, and L. Goodstein. 1994. Cognitive Systems Engineering. New York: John Wiley and Sons. Simon, H. A. 1996. The Sciences of the Artificial. Cambridge, MA: MIT Press. Thompson, J. D. 1967. Organizations in Action. New York: McGraw-Hill.

CHAPTER 8 Bell, J. B., and B. Whaley. 1991. Cheating and Deception. Edison, NJ: Transaction Publishers.

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Evidence Based Research, Inc. 2003. Network Centric Operations Conceptual Framework Version 1.0. http://www.iwar.org.uk/rma/resource/new/new-conceptualframework.pdf. Galbraith, J. 1974. Organization Design: An Information Processing View. Interfaces 4 (May): 28–36. Janis, I. L. 1982. Groupthink: Psychological Studies of Policy Decisions and Fiascoes. Boston: Houghton Mifflin Company. Kahneman, D., and A. Tversky. 1979. Prospect Theory: An Analysis of Decision under Risk. Econometrica 47(2): 263–92. Klein, G. 1999. Sources of Power: How People Make Decisions. Cambridge, MA: MIT Press. Klein, G. A., R. Calderwood, and D. MacGregor. 1989. Critical Decision Method for Eliciting Knowledge. IEEE Transactions on Systems, Man, and Cybernetics 19(3): 462–72. Kott, A., ed. 2007. A Model of Self-Reinforcing Defeat in Command Structures Due to Decision Overload. In Information Warfare and Organizational DecisionMaking, ed. A. Kott, pp. 135–41. Norwood, MA: Artech House. Louvet, A-C., J. T. Casey, and A. H. Levis. 1988. “Experimental Investigation of the Bounded Rationality Constraint.” In Science of Command and Control: Coping with Uncertainty, ed. S. E. Johnson and A. H. Levis, pp. 73–82. Washington, DC: AFCEA. Perrow, C. 1999. Normal Accidents: Living with High-Risk Technologies. Princeton, NJ: Princeton University Press. Shattuck, L. G., and N. L. Miller. 2004. A Process Tracing Approach to the Investigation of Situated Cognition. Proceedings of the Human Factors and Ergonomics Society’s 48th Annual Meeting, New Orleans, pp. 658–62. Simon, H. 1991. Models of My Life. New York: Basic Books. Tversky, A., and D. Kahneman. 1974. Judgment under Uncertainty: Heuristics and Biases. Science 185: 1124–31. van Creveld, M. 1985. Command in War. Cambridge, MA: Harvard University Press. Woods, David D. 1993. Process Tracing Methods for the Study of Cognition Outside of the Experimental Psychology Laboratory. In Decision Making in Action: Models and Methods, ed. G. Klein, J. Orasanu, R. Calderwood, and C. Zsambok, pp. 228–51. Norwood, NJ: Ablex Publishing Corporation.

CONCLUDING THOUGHTS Bailey, Tracy A. 2005. “Air Assault Expeditionary Force Tests Technologies.” Army News Service, December 1. Boot, M. 2006. War Made New. New York: Gotham Books. Kagan, F. 2003. War and Aftermath, Policy Review (August–September). http://www. hoover.org/publications/policyreview/3448101.html. Kott, A., ed. 2007. Information Warfare and Organizational Decision Process. Norwood, MA: Artech House Publishers. Kott, A., and W. McEneaney, eds. 2006. Adversarial Reasoning: Computational Approaches to Reading the Opponent’s Mind. New York: Chapman and Hall, CRC Press.

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Oman, C. 1937. History of the Art of War in the Sixteenth Century. New York: E.P. Hutton. Oman, C. 1960. The Art of War in the Middle Ages. Ithaca, NY: Cornell University Press, pp. 152–59. Wilson, P. A., J. Gordon, and D. E. Johnson. 2004. An Alternative Future Force: Building a Better Army. Parameters (Winter): 19–39.

Index

Abrams tank, 42 Acute incidents, definition, 153 Adjustment decisions, 198 Afghanistan, noncombatants in, 28 Air Assault Expeditionary Force, 213 Air weapons, 30–31. See also Unmanned aerial vehicles (UAVs) Alerts, 53, 153 Alert Tracker, 68 Alexander the Great, 26, 167 Al Firdos bunker destruction, 32 Alternative evaluation, 171 Alternative generation, 171 Analytic observation: about decision making, 197–98; situation awareness scores, 134; use of, 121–24 Animation-based tools, 190 Appropriateness. definition, 199, 200 Army Field Manual 3.0, Operations, 21–22 Army Transformation Roadmap, 34–35 Artillery fire detection systems, 20 Asset agents, 66, 67 Assets, localization of, 76 ASTi simulated radio and communication system, 85 Attack Guidance Matrix (AGM): command-cell interface, 82; description, 208–9; function of, 53, 66, 68, 81; impact on timing, 196

Attention shifting, 151–52, 211 Audio logs, 132 Automated Guidance Matrix, weapon status, 190 Automatic route generation, 87 Automatic Target Detections (ATDs), 158 Auto recon tasking, 53 Azerbaijan, battle scenario, 55 Balaclava, Crimea, 22 Battle command: battlefield enlargement and, 15–16; challenge of tactical agility, 31–32; collaboration in, 167; definitions of, 11–12; deletation and, 20–21; disrupted, 190–93; functions of, 12; future of, 33–36; history of, 12; human biases in, 143–47; human dimension of, 35–36; information processing, 43, 61–63; judging timing, 25–26; key tasks, 18–23; motivation and, 23; multiplication of domains, 17; Napoleonic, 15, 62–63; organizational complexity of, 16–17; pattern recognition and, 18; planning and, 19; recurring dilemmas, 24–29; simultaneity, 30; situation awareness in, 95–119; synchronizing and, 21–22; 247

248

Index

Battle command (Continued) technology and, 216; time compression challenge, 29; tools for, 64–94; transparency of, 32–33 Battle Command Support Environment (BCSE): C2 architecture of, 65–75, 68; Commander Support Environment and, 67; communication displays, 85–86; control of, 67; decision support units, 74–75; definition, 64; tools within, 70 Battle command systems: collaboration-oriented technologies, 168; network-enabled warfare and, 213 Battle Damage Assessment (BDA): automation of, 83–85, 208–10, 214; biased use of, 143–47; collaboration in, 173; correctness of, 145; CSE visualization of, 76; demands of, 219; guidance matrix, 53, 79; image quality, 146; importance of, 204; situation awareness and, 175; sources for updates to, 145; Threat Manager reports, 78–79 Battle Damage Assessment Guidance Matrix (BDAGM), 53, 79, 83–85, 90 Battlefield Functional Areas (BFAs), 65, 69 Battlefields: enlargement, 15–16; handling of casualties, 28; operating systems for, 34; terrain appreciation and, 18 Battle plans, typical history of, 58–61 Battlespace managers, 49. See also Maneuver managers Battlespaces: covered by sensors, 156; perceptions of, 131 (See also Situation awareness); visualization of, 76–77 Battlespace Terrain Reasoning and Awareness (BTRA) system, 87 Battle tempo: entity-level, 184; on process traces, 138, 139; situation awareness and, 135–36 Beatty, David (Vice Admiral), 10–11, 25 Belief persistence, 149–50 Benet, Stephen Vincent, 35 BFAs (battlefield operating systems), 34 Biases: in Battle Damage Assessment, 143–47; of Blue command, 150, 151;

command and, 143–47, 218–19; confirmation type, 149–50, 196; decision making and, 196; overcoming preconceptions, 149–59; role in battle command, 143–47; towards target acquisition, 143, 148 Blue command, 48–50; biases, 150, 151; SAt scores, 138, 141, 142; sensemaking failures, 157–58; situation awareness of, 133 Blue force: battle planning, 58–61; 2018 battle scenario, 37–41; command cell, 41; commander support environment, 50–53; organization of, 58; situation awareness and, 218 Blue unit viewer, 53 Bomb Line, origins of, 17 Bonaparte, Napoleon, 15, 62–63 Boyd, John (Colonel), 12 Bradley personnel carrier, 42 Brehmer, B., 171 Briefing Tools, function of, 77–78 Brigade Command Team (BCT), 58, 59 Brigade Logistics Coordinator, 108 Britain, Battle of Jutland, 10–11 Caspian Sea scenarios, 46, 55–58 Cebrowski, Arthur K. (Admiral), 42 Chance, battle and, 14–15 Chu, Specialist, 40, 87 Churchill, Winston, 13 Civilians. See Noncombatants Clark, H. H., 177 Clausewitz, Carl von: on battles, 18; on the “fog of war,” 13; on “friction,” 14–15; on genius, 30; on the “realms of war,” 13; on the scale of war, 25; on the uncertainty of war, 14 Clips/R2, 92 Code generation, automated, 91 Cognitive load, 162–65, 213, 215 Cognitive processing, 105–6 Cold Harbor casualty rates, 30 Collaboration: behavior types in, 177; coordination and, 176–79; COPs and, 189; dark side of, 206–8; definition, 176; in disrupted command, 190–93; impact on timing, 194–211; information sharing and, 219;

Index mission-oriented thinking and, 184–85; in network-enabled warfare, 167–93; points of impact, 169–70; situation awareness and, 174–76; task transmission and, 170–71; technology and, 169–70, 180–81; training in, 220 Collaboration Evaluation Framework (CEF), 168–69, 183 Collaborative technology, 182 Collaborative Technology Alliances (CTAs), 91 Collateral damage, 28. See also Noncombatants Collection Management tool, 79 Collective Agents, 66, 67, 71 Collective Intelligence Module (CIM), 89 Combat power, situation awareness and, 62 Combat Power tool, 86 Combined Arms Teams (CATs): allocation of responsibilities, 49; battle planning, 58–61; CAU command cells, 48–49; commanders of, 172; composition of, 49, 50; equipment of, 59; organization of, 59, 173; in OTB simulations, 47; reporting structure, 58; SAts, 164 Combined Arms Units (CAUs): allocation of responsibilities, 49; battle planning, 58–61; 2018 battle scenario, 38–41; in Combined Arms Teams, 47; composition of, 49–50, 50; coordination of, 177; C2V, 40, 49; directing fire, 184–85; equipment of, 40; loss of MCSs, 186–88; mission-oriented assessments, 185; organization of, 40, 45, 173 Command: changing context of, 15–17; description of cells, 46; destruction of cells, 193; functions of, 75–87; succession, 86, 192 Command and Control (C2): Data Model, 94; execution centric, 64; experimental design and, 165–66; situation awareness and, 98–107; task transmission and, 171; in team operations, 112–17; VDSF and, 91

249

Command and Control Vehicles (C2Vs), 191–92, 193 Command Center, description of, 70 Commander Decision Environment, 195 Commanders: audio logs of, 132; challenge of force dispersion, 212–13; confidence in subordinates, 21; coping skills of, 15; personal influence of, 23; self-assessment by, 220; situation awareness cognitive, 131–32; staff agents and, 66 Commander’s Critical Information Requirements (CCIR), 69, 70, 85, 153 Commander Support Environment (CSE): battle command system interfaces, 147–48; 2018 battle scenario, 40; Blue force, 50–53; command functions, 75–87; command succession functions, 86; COP limitations, 147–49; customization of user interfaces, 77; function of, 68; interface clutter, 153; key functions of, 53; knowledge management functions, 69; loss of communication symbols, 191; MDC2 and, 51–53; screen view, 54; situation awareness maintenance, 143–49; suite of tools in, 68–69; tiers of, 68, 71–73; tools of, 51, 52 Command Post of the Future, 168, 213 Commercial Joint Mapping Toolkit (CJMTK), 86–87 Common ground preservation, 177 Common operating picture (COP): CSE generation of, 67, 69; definition, 64; impact on situation awareness, 189; limitations of, 147–49; overtrust in, 148; screen views, 69; sensor detections in, 128; shared data, 159–62; terrain perspectives on, 87 Communications: bandwidth management, 86; BCSE display of, 85–86; collaborative technology and, 181–83, 189–90; delegation and, 21; history of, 22–23; organizational complexity and, 16; transmission, 177; verbal, 189–90 Completeness, definition, 199, 200 Computer-generated forces, 47

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Index

Computerization, enterprise structure and, 63 Concept of operations (CPNOPS), 181–83 Confidence, definition, 200 Confirmation, collaboration and, 177 Confirmation bias, 149–50, 196 Connection, collaboration and, 177 Context sensitivity, definition, 80 Contingency planning, 24 Control, definition of, 12 Control functions, CSE, 53 Coordination: collaborative technology and, 182; costs of, 181; definition, 176; types of, 176–79 Correctness, definition, 199, 200 Courses of action (COAs), 75–76 Crimean War, 28 Critical Decision Method, 133–34 Criticality, definition, 200 Danger: battle and, 13–14; impact of automation, 31 DARPA (Defense Advanced Research Projects Agency): 2018 battle scenario, 37–41; history of, 44–46; MDC2 project and, 18; RAID system, 216 Data collection. See also Multicell and Dismounted Command and Control (MDC2) project: about decision making, 197–203; data filtering and, 152–53; for situation awareness, 120–24 Decision making: adjustment decisions, 198; automatable, 198, 208–10; automated support, 214; battle command and, 12; collaboration and, 206; complex decisions, 198; data collection about, 197–203; information overload and, 210–11; information processing and, 61–63; problem solving and, 19–20; rationality and, 196; risk assessment and, 19; social forces and, 196–97; timing of, 194–211; tools for, 73; translation to action, 12 Decisions: characteristics of, 200; evaluation of, 199–203; types of, 198–99 Decision support framework, 90–94

Defense Advanced Research Projects Agency. See DARPA Delegation, 20–21, 30 Democratic societies, 13 Detection Catalog tool, 83 Device Dependent Interface (DDI)/ Device Translator (DX), 92–93 Device Independent Interface (DII), 93 Differentiation, 171, 178 Direct vision optics (DVO) sensors, 155 Discipline, resistance to fear and, 13 Draega, timing of, 81 DSS Reasoner, 93 Economic imperatives, battle command and, 24 Effects managers, 49. See also Fires managers Election, collaboration and, 177 Electronic components, noninterchangable, 27 Enemies, awareness of, 97 Engagement status, visualization of, 76 Engineers, situation awareness requirements, 115–16 Execution Synchronization Matrix, 80 Experimental design: analysis and, 165–66; data collection, 120–24, 152–53, 197–203; interviews, 121, 133–34, 201–2; MDC2 project, 53–58 Face-to-face conferencing, 181 Fear, resistance to, 13 Fires: automation of, 81, 214; control of, 82; manual execution of, 81–82 Fires managers, 49. See also Effects managers Fire Support Coordination Line, 17 Focus groups, 202–3 “Fog of war,” 13, 64–94 Fragmentary orders (FRAGOs), 190 Friendly situation, awareness of, 97 Fuel consumption, visualization of, 76 Future Combat System (FCS): collaboration-oriented technologies, 168; development of, 220–21; information rich platforms, 43; origins of, 42–43; VDSF and, 91

Index Future Combat Systems Command and Control (FCS C2), 44–45, 46 Future Force leaders, 18 Future Force Warrior (FFW), 91 Gantt charts, 80 Garm, reconnoitering of, 80 Garstka, John, 42 Gawande, Atul, 15 Geodetic coordinate system, 87 Geographic Intelligence Overlays, 77 Georeferenced satellite imagery, 87 Germany, Battle of Jutland, 10–11 Gettysburg, Confederate artillery at, 16 Globalization, enterprise structure and, 63 Goal-Directed Task Analysis (GDTA), 107, 108, 113 “Going sour” incidents, 153 Gordon, Andrew, 11 Grand Fleet, United Kingdom, 10–11 Grant, Ulysses S., 16, 18 Graphical Control Measures (GCMs), 76, 87 Ground surveillance radar (GSR), 88 Group interviews, 121 Groupthink, 197 HaleyRules, 92 Hammurabi [Division], 25 Heisenberg, Werner, 14 High Mobility Artillery Rocket Systems (HIMARS), 58 High-payoff targets (HPT), 127–28 High Seas Fleet, Germany, 10–11 Huertgen Forest, 26 Human perceptions, 13 Identification, collaboration and, 177 Incident identification, 202 Information: abstraction, 171–74; addiction to, 205–6; availability of, 204; cost of, 203–5; deficiencies in, 18; degree of urgency, 157; distribution of, 167–68; drivers of, 171; gaps in, 105, 147–48, 213; hierarchical levels, 171–74; indication of certainty, 157; networked, 34; presentation of, 218; processing

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of, 61–63; shared, 157–62, 175–76 (See also Collaboration); sharing of, 219; task-specific, 171; transformation of, 172; visualization of, 70. See also Common operating picture (COP) Information advantage rules, 140–43 Information managers, 49, 73. See also Intelligence managers Information overload: cognitive filtering, 152–53; CSE interface clutter, 153; groupthink and, 197; during the invasion of Iraq, 22–23; negative impact of, 210–11; robotic sensors and, 50–53; situation awareness and, 104–5 Insurgents, identification of, 28 Intelligence, 115–16, 117. See also Information Intelligence, Surveillance, and Reconnaissance (ISR) protocols: development of, 144; information transformation and, 173–74; integration of, 163–64; visualization of sensor coverage, 190 Intelligence gaps, misinterpretations and, 149–57 Intelligence management: coordination and, 176; overload, 204; Picture Viewer function, 82–83 Intelligence managers, 49. See also Information managers Intelligence Preparation of the Battlefield (IPB) process, 141–42 Intelligent Agents: components of, 67; CSE Tier 2, 71; CSE Tier 3, 71–72; definitions, 65; functional areas of, 66–67 Intel Viewer tool, 83, 84 Intensive processes, 179 Interdependence: collaboration and, 180; types of, 180 Interviews: group, 121; methodology, 133–34; scoring, 201–2 Iraq: insurgence, 33; invasion of, 22–23, 26–27; noncombatants in, 28 Javelins, history of, 21 Jellico, John (Sir), 10–11, 14

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Index

Johnson, Captain: battle scenario, 37–41, 87–90; commander support environment for, 51; situation awareness of, 63 Justifications, 171, 178 Jutland Peninsula, 10–11, 15, 25 Karbala Gap, Iraq, 25 Klein, G. A., 178 Knowledge bases, 67 Kura Brigade, 37–38, 46–47 Leadership. See also Command: battle command and, 12; from the front, 15 Lee, Robert E., 16 Lethality, trends in, 30–31, 212–13 Leuthen, Battle of, 13 Line of Sight (LOS) tools, 77 Logistical risks: CSE visualization of, 76; displays of assets, 70; management of, 26–27; simultaneity and, 30 Logistical tools: within CSE, 86; situation awareness requirements, 115–17; supply needs analysis, 109 Long-linked processes, 179 Long Range Surveillance (LRS) soldiers, 82 Maneuver managers, 49. See also Battlespace managers Maps, 86–87 Map sets, 77 Mauldin, Bill, 23 Media, transparency to, 33 Mediating processes, 179 Military grid reference system (MGRS), 87 Military procurement, 43 Military socialization, 13 Misinterpretations, situation awareness and, 213 Mission-complete estimates, 172 Mission-oriented thinking, 184–85 Missions, awareness of, 98 Mission Workspace, 76 Moltke, Helmuth von, (General), 19 Molz, Maureen, 45 Most dangerous targets (MDTs), 127–28

Motivation, battle command and, 23 MULEs (mine-detecting sensors), 158, 159 Multicell and Dismounted Command and Control (MDC2) project: achievements of, 213–17; Battle Command Support Environment, 64; central focus, 31, 35; commander support environment and, 51–53; data collection approaches, 122, 197–203; experimental design, 53–58; experimental testbed, 46–48; history of, 45, 46; hypothetical organization, 173; launch of, 18; long-term goals of, 181; study of collaboration issues, 168; VDSF and, 91; visual experimentation model, 73 Multi-Role Armament and Ammunition System (MRAAS), 88 Munitions-on-Hand tool, 86 Mutual adjustments: behaviors required for, 178; cognitive costs of, 189–90; coordination and, 176–77 Nagorno-Karabakh Internal Liberation Organization (NKILO), 39–41 Named Areas of Interest (NAI), 141–42 Network Centric Operations Conceptual Framework, 199 Networked automation: decision making and, 20; diagnostic functions and, 18–19; functions of, 34; human interface with, 36; impact on collateral damage, 28; logistical problems and, 27; management of information overload, 22–23; prioritization dilemma and, 24; redundancy of command and, 32; synchronizing and, 22; system limitations, 34–35; tactical headquarter footprint and, 32; timing judgments and, 25; trust in the system, 210 Network-enabled command: challenges of, 217–21; tools of, 213–17 Network-enabled warfare: battle command systems and, 213; battle of 2018, 41–44; collaborative potential of, 167–93; critics of, 220; decision-making environment, 196

Index Neutral forces, simulated, 48 Noncombatants: 2018 battle scenario, 59; casualties among, 32; coping with, 27–29; level-1 situation awareness of, 97; logistical burden of, 28–29 Non-Line of Sight (NLOS) assets, 49, 88 Objective SAINTS, 25 Office of Force Transportation, 42 OneSAF Testbed (OTB), 46–48, 73–74 OODA Loop (Observe, Orient, Decide, Act), 12 Operation Desert Storm, 28–29 Operation Iraqi Freedom, 23 Operations: concept of, 181–83; situation awareness requirements, 115–16 OPORD, changes to, 77 Organizational complexity, 16–17 Outcome consistency, 200 Paradoxes, 196 “Paranoia factor,” 132 Pattern recognition, 18 Patton, George, 23 Performance measures: collaboration and, 174–75; of decision making, 199–203; for situation awareness, 110–11 Physical exertion, battle and, 14 Picture Viewer, 82–84 Planning: animation-based tools for, 190; battle command and, 19; coordination and, 176; within CSEs, 75–76; event sequences in, 26; Mission Workspace, 76; unexpected changes in mission, 31–32 Platform-centric computing, 42 Platform Support Environments (PSEs), 72, 88–89 Precision weapons, intolerance to error, 31 Preconceptions, overcoming, 149–59 Prioritization, battle command and, 24 Prisoners of war, 28–29 Problem solving, decision making and, 19–20

253

Process traces, 137–38, 186–88, 201 Prognostics, self-alerting, 27 Prohibit Fire tool, 80 Public opinion, 28, 33 Quick Fire tool, 80, 81–82 Rahim, Sargent, 40–41, 51 RAID system, 216 Rationalization, definition, 170 React to Contact behaviors, 47 Reasoning engines, 67 Red command: SAt scores, 138, 141, 142; situation awareness of, 134 Red force: battle planning, 58–61; 2018 battle scenario, 38–41; command cells, 48; Kura Brigade command, 47; size of, 54 Redundancy: of command, 32; command succession, 86; in data models, 67; of information models, 65; in ship design, 32 Relevance, definition, 199, 200 Remote control, 31 Representational errors, 149–50 Request for Fire tools, 82 Resident Agents, function of, 71–72 Resource Availability tool, 79 Resource utilization: commander support environment for, 51; dispersal of tactical units, 31–32; planning and, 19; prioritizing requirements, 24; simultaneity and, 30; synchronizing and, 21–22 Rete Algorithm, 91 Risk assessment, decision making and, 19 Robotic platforms, 51. See also Unmanned aerial vehicles (UAVs) Robotic sensors. See also Sensors Rommel, Erwin (German), 26 Route generation, tools for, 87 Royal Navy, 10–11 RSTA (Reconnaissance, Surveillance and Target Acquisition) tools, 88–89 Rule making, 210 Rules Engine, description, 93 Rules Helper Methods, 93

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Index

Rule Trigger Method, 94 Russo-Japanese War, 16 Sauer, Gary, 44 Scheer, Reinhard (Admiral), 10–11 Schlieffen, Alfred von (General), 16, 35–36 Selection, 171, 178 Self-awareness, of commanders, 220 Semiautomated forces, 47 Sense-making failures, 157–58 Sensor coverage, 128–31; analysis tools, 154–55; SAt and, 155; situation awareness and, 153–57; visualization of, 190 Sensor Coverage tools, 156, 214–15 Sensors: 2018 battle scenario, 39–41; control of, 58, 80; with direct vision optics, 155; gaps, 147, 148; information overload and, 50–53; target detection by, 31 Sherman, William Tecumseh (General), 27 Ship design, redundancy in, 32 Simulations, 46–48, 165 Simultaneity, resource utilization and, 30 SINCGARS, 85 Situation Awareness—Cognitive (SAc), 130–35 Situation Awareness Global Assessment Technique (SAGAT), 111, 112 Situation Awareness (SA): acquisition component, 125–26; attention shifting and, 151–52; battle command effectiveness and, 95–119; battle tempo and, 135–36; C2 challenges, 98–105; cognitive load, 162–65; collaboration and, 174–76; combat power and, 62; common operating picture and, 64; confirmation of, 170, 178; CSE tools for, 78–79; data collection for, 120–24; definition, 95; gaps in, 149–57; impact on timing, 194–211; individual limitations, 100, 102; information advantage rules, 140–43; information gaps and, 213; information overload and, 210–11;

information processing and, 61–63; level-1, 95, 97–98, 107, 114–15, 175; level-2, 96, 99–100, 107, 114–16, 175; level-3, 96, 98, 101–2, 107, 114, 116, 151, 175; location component, 124–25; maintenance of, 143–49; measurement of, 110–11; misinterpretations and, 213; misinterpretations in, 149–57; overload, 104–5; overtime for Blue and Red, 160; perceptual constraints, 103; quantitative analysis of, 214; requirements analysis, 107–9; shared information and, 157–62; sources of, 103; state component, 126; stressors, 103–4; system design for, 105–10; in team operations, 112–19; underload, 104–5 Situation Awareness—Technical (SAt): analysis, 166; calculation of, 127; component scores, 125–28; curve over time, 138; development of, 124; information advantage rules, 141–43; level-1, 159, 162–63; level-2, 159; overtime for Blue and Red, 160; sensor coverage and, 153–57; UAV losses and, 206 Slim, William (British Field Marshal), 18 Soldiers, willingness to fight, 13 Soldier/Vehicle Support Environment (SSE), 72–74 Soviet Union, 41–41 Special Operations Force teams, 58 Stacked charts, 136–39, 201 Standardization, coordination and, 176 Standoff lethality, 212–13 Subordinates: commander confidence in, 21; fostering initiative in, 30 Suffering, battle and, 14 Sun Microsystems, 42 Supply lines, logistical risk and, 26–27 Surveys, 131, 198–99 Survivability estimates, 66 Synchronization: battle command and, 21–22; collaboration and, 177; Commander Support Environment, 53; ViewSync and, 77–78 System design, 107–11, 147–48

Index TacFire, 20 Tactical agility, 31–32 Tactical engagements, 29 Tactical headquarters footprints, 32 Tactical tasking, 53 Tactical unit dispersal, 31–32 Tank warfare, impact of, 17 Targets: acquisition, 143, 148, 209; detection of, 31, 76, 81; identification of, 49; status of, 76 Targets Reconnaissance tasks, 80 Task analysis, 107–8, 169 Task assignment tools, 79–81 Task Decomposition, 70 Task environments, 176–77, 180–81 Task organization, 184 Task processes, 179 Task Synchronization Matrix, 68 Task transmission, 170–71, 179, 189 Technology: collaborative, 180–81; “fog of war” and, 64–94; human dimensions of battle and, 33–34; support for collaboration, 169–70; system design issues, 105–10 Tecuci, Gheorghe, 65 Tercio, effectiveness of, 216 Terrain: appreciation of, 18; situation awareness of, 97, 115–16 Terrain Analysis, 70, 86–87 Thompson, J. D., 176, 180 Threat analysis, 66, 78–79 Threat Manager, 53, 68, 78–79 Time compression, 29 Timelines, of runs, 202 Timeliness: definition, 199, 200; information trade-offs and, 206 Timing: command judgments, 25–26; decision making and, 194–211 Transaction Processor, 93 Transmission, collaboration and, 177

255

Transparency, battle command and, 32–33 Trust, importance of, 210 2018 battle scenario, 37–41, 55–59 Uncertainty, 14, 196 Unit Viewer tool, 83 Unmanned aerial vehicles (UAVs), 31, 67, 72, 88 U.S. Army Engineer Research and Development Center (ERDC), 87 U.S. Army Training and Doctrine Command (TRADOC), 44, 55–58 User interfaces, 77, 156 Vetronics Technology Integration Program (VTI), 91 Video conferencing, 181 Viecore Decision Support Framework (VDSF), 90, 93 Vietnam War, 20–21, 28 ViewSync tool, 77–78 Visualization: Battle Damage Assessment, 76; of battlespace, 76–77; of information, 70; Picture Viewer function, 82–83; sensor coverage, 190; situation awareness and, 214 Washington, George, 23 Waterloo, Battle of, 13, 15–16, 23 Wavell, Sir Archibald, 26, 33 Weapon-to-target pairing, 66 Weather, 97 Wellington, Duke of, 15, 23 White phosphorus, 33 Workspaces, customization of, 77 World War I, 16–17 World War II, 17 Zizka, Jan, 220

256

About the Contributors

LEONARD ADELMAN’s research focuses on judgment, decision, and collaborative processes; cognitive systems engineering; and decision support and expert system evaluation. Adelman is a tenured, full professor in the Department of Systems Engineering and Operations Research at George Mason University. He is also the coordinator for the Command Support Technical Area in GMU’s Center for Excellence in Command, Control, Communications, Computing, and Intelligence. Adelman has authored or coauthored more than 50 journal papers, book chapters, and conference proceedings. His three books are Evaluating Decision Support and Expert Systems (Wiley 1992); Cognitive Systems Engineering for User-Centered Interface Design, Prototyping, and Evaluation (with Stephen Andriole, LEA 1995); and Handbook for Evaluating Knowledge-Based Systems (with Sharon Riedel, Kluwer 1997). In addition, he has participated in developing and evaluating prototypes for improving operational systems, including AWACS and Patriot. Adelman is a member of the Judgment & Decision Making Society, Human Factors & Ergonomics Society, Brunswik Society, and IEEE (elected senior member). He earned his PhD from the University of Colorado in 1976. Prior to joining George Mason University, Adelman cofounded the Decision Sciences Section of PAR Technology Corp. and, as the manager of this section from 1984–1986, led R&D efforts designing, developing, and evaluating decision system prototypes for all three branches of the armed forces. In the MDC2 program, Adelman focused on the collaboration aspects. RICHARD J. BORMANN JR. is the senior director of research and development for Viecore FSD. Bormann is responsible for the architecture, design,

257

258

About the Contributors

and development on several defense research Battle Command programs, as well as several decision support systems for handheld and robotic systems. In particular, he leads the work on extending Viecore FSD’s capabilities in the area of artificial intelligence, expert systems, and robotic elements supporting the Future Force. In 1998, Bormann held the title of Distinguished Member of the Technical Staff at AT&T, where he worked in the research area to provide expert systems for improving call center performance and efficiency. He received his BS in computer science from Kean University and a master’s in computer science from Stevens Institute of Technology, both in New Jersey. Bormann led the development of MDC2 program’s technical architecture and much of its software. MICA R. ENDSLEY is president of SA Technologies, a cognitive engineering firm specializing in the development of operator interfaces for advanced systems, including the next generation of systems for aviation, air traffic control, power, medical, and military operations. Prior to forming SA Technologies, she was a visiting associate professor at MIT in the Department of Aeronautics and Astronautics and associate professor of Industrial Engineering at Texas Tech University. Endsley received a PhD in Industrial and Systems Engineering from the University of Southern California. She is a registered professional engineer and certified professional ergonomist and is a fellow of the Human Factors and Ergonomics Society. She has published extensively in the areas of situation awareness, decision making, and automation and is coauthor of Situation Awareness Analysis & Measurement and Designing for Situation Awareness. Endsley’s ideas on situation awareness were a major influence on the MDC2 program. LEROY A. JACKSON is deputy director and senior operations research analyst at the U.S. Army Training and Doctrine Command Analysis Center in Monterey, California. He has more than 11 years of experience conducting applied research to enhance army analysis of future force advanced concepts and requirements, including his work on the MDC2 program. His research interests include artificial intelligence and human cognition. He is a member of the Military Operations Research Society where he has served as the cochair of several symposium and workshop working groups. Jackson has more than 29 years of experience in military operations and battle command, having served more than 24 years on active duty as a soldier, noncommissioned officer, and commissioned officer in the Field Artillery and as a military operations research and systems analyst. He served for 4years at the Field Artillery Board conducting operational tests of Field Artillery battle command systems. He served in 105 mm, 155 mm, 175 mm, and 8 inch cannon artillery battalions. He is a member of the honorable Order of Saint Barbara, and his many awards include the Legion of Merit. Jackson received an MS in operations research from the Naval Postgraduate School in 1995 where he

About the Contributors

259

was the distinguished Department of Defense graduate. He is a recipient of the U.S. Army Chief of Staff’s Award for Excellence in Operations Research. Jackson also earned a BA in Mathematics with highest honors from Cameron University in 1990 and is a member of Phi Kappa Phi collegiate honor society and the Pi Mu Epsilon mathematics honor society. STEPHEN KIRIN, a retired colonel of the U.S. Army, has been a member of the MITRE Corp. since July 2000. Since January 2006, he has served as the lead of the Operations Research—Systems Analysis Division in the Joint Improvised Device Defeat Organization. Kirin’s culminating active duty assignment was as the deputy director for the TRADOC Analysis Center. During his four years at TRAC, he was the lead analyst for a number of key experiments and studies to underpin Army Transformation. In his 27 years of service, Kirin served at every level from platoon to corps and has been a student of the issues associated with battle command. Since joining MITRE and prior to his support to JIEDDO, he has continued to investigate and analyze operational issues with a focus on battle command. Kirin received a bachelor of science in engineering from the United States Military Academy and a master’s of science in operations research and applied mathematics from Rensselaer Polytechnic Institute. He was a U.S. Army Rand Fellow for two years and is a graduate of the U.S. Naval War College. GARY L. KLEIN focuses his work on modeling how people acquire and use information. As the senior principal scientist in Cognitive Science & Artificial Intelligence in the C2C, he is responsible for developing and promoting both of those technical areas with respect to supporting the development of enhanced decision support. He also is developing the application of cognitive systems engineering throughout MITRE. His current work is applying cognitive systems engineering to the army’s 1st Information Operations Command (Land). The objective is to identify transformational technology opportunities related to 1st IO Command, which have new start potential for the Defense Advanced Projects Agency. He and Leonard Adelman developed the Collaboration Evaluation Framework originally to assess collaborative tools in intelligence analysis in terms of their impact on collaboration per se. In an extension of that effort, he led a team to help the intelligence community’s Disruptive Technology Office (formerly ARDA) assess intelligence analysis tools with regard to their ergonomic, cognitive, and collaborative suitability. In other work, to improve understanding how policy changes lead to changes in decision making and subsequently organizational behavior, Klein developed the Adaptive Decision Modeling Shell (ADMS) for creating cognitively realistic agent-based social simulation models. For MITRE’s Center for Advanced Aviation Systems Development, he recently led a C2C technical team in using ADMS to develop a social simulation model of airline-scheduling decision making. Dr. Klein led the MDC2 program’s research in collaboration within and between command cells.

260

About the Contributors

ALEXANDER KOTT is a program manager in the Defense Advanced Research Projects Agency, the central R&D organization of the U.S. Department of Defense. He earned his PhD from the University of Pittsburgh, Pennsylvania, where his research focused on applications of artificial intelligence for innovative engineering design. Later he directed R&D organizations at technology companies including Carnegie Group, Honeywell, and BBN. Kott’s affiliation with DARPA included serving as the chief architect of DARPA’s Joint Forces Air Component Commander (JFACC) program and managing the Advanced ISR Management program as well as the Mixed Initiative Control of Automa-teams program. He initiated the DARPA Realtime Adversarial Intelligence and Decision-making (RAID) program and also managed the MDC2 program. Kott’s research interests include dynamic planning in resource-, time-, and space-constrained problems in dynamically changing, uncertain, and adversarial environments; and dynamic, unstable, and “pathological” phenomena in distributed decision-making systems. He has published more than 60 technical papers and served as the editor and coauthor of several other books, including Advanced Technology Concepts for Command and Control, Adversarial Reasoning, and Information Warfare and Organizational Decision-Making. DOUGLAS J. PETERS leads the Live, Virtual, and Constructive Modeling group for Applied Research Associates (ARA). In this role and in his prior role leading the Command and Control Concepts group, he has led experimental design and analysis efforts for several DARPA programs. On the MDC2 program, Peters led portions of the analysis as well as ARA’s efforts to collect experimental data, query the data, and develop innovative visualizations for the data. On the DARPA Real-time Adversarial Intelligence and Decision-making (RAID) project, Peters was responsible for experimental design, data collection, information elicitation, and analysis. Currently, he is leading weapon engagement modeling for U.S. Army live training systems. Also at ARA, he has led the Hard Target Uncertainties program and the tunnels defeat portion of the Integrated Munitions Effects Assessment, both sponsored by the Defense Threat Reduction Agency. Peters is interested in developing models of engineering phenomena and experimental metrics and has been developing/implementing models for 11 years. He currently serves on the Research and Development Subcommittee for the Interservice/Industry Training, Simulation and Education Conference. Peters received his bachelor’s degree in architectural engineering from the Pennsylvania State University and his master’s degree in civil engineering from North Carolina State University. He is a registered professional engineer in the state of North Carolina. JENNIFER K. PHILLIPS is the president and principal scientist of Cognitive Training Solutions, LLC. Her interests include skill acquisition, cognitive performance improvement, and the nature of expertise. MDC2 is one of the

About the Contributors

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programs in which she has conducted research studies to examine and model naturalistic human cognition. In a program of research sponsored by the army, she investigated the process by which individuals make sense of situations as they unfold and developed a model of sense making. Phillips has applied her research to the development of several training interventions focused on improving complex cognitive skills such as decision making, sense making, and situation awareness, and problem detection. She has extensive experience conducting Cognitive Task Analysis to elicit expert knowledge and generate design and training requirements. She has conducted studies to develop decision-making training scenarios at all echelons of military command and using a range of media, including Web- and computer-based simulations. She has also studied the role of instructors as facilitators of the learning process and has developed instructor guides and train-the-trainer workshops to ensure a focus on the cognitive elements of decision making. In addition, Phillips has developed assessment measures and conducted evaluation studies to determine the effectiveness of training interventions for improving cognitive skills. Phillips received a BA in psychology from Kenyon College in 1995. STEPHEN RIESE is a member of the Senior Professional Staff at the Johns Hopkins University Applied Physics Laboratory (APL), where he directs the APL counterimprovised explosive device (IED) analysis program. He joined APL after completing 24 years of service in the U.S. Army as a combat engineer and operations research analyst. He taught systems engineering design to cadets at the U.S. Military Academy at West Point, led engineer forces as part of the NATO Peace Implementation Force in Bosnia-Herzegovina, led analysis of future army combat systems and command and control structures at the army’s Training and Doctrine Command Analysis Center, and directed the analysis of strategic deterrence operations at the U.S. Strategic Command at Offutt Air Force Base. Riese earned an undergraduate degree in architecture from the University of Notre Dame, a master’s degree in industrial engineering from Kansas State University, a master’s degree in military history from the U.S. Army Command and General Staff College, and a doctorate in systems engineering from the University of Virginia, where his research focused on empirical spatial forecasting models. Continuing this research, he currently develops spatial analysis methods for the U.S. Army Topographic Engineering Center to be deployed in the Threat Mapper geospatial analysis tool set and used to support operational analysis in the counter-IED fight and the global war on terrorism. Riese contributed extensively to the analysis of MDC2 experimental data. KAROL G. ROSS is a research psychologist currently working at the Institute for Simulation and Training, University of Central Florida. Her area of expertise in applied research includes qualitative methods for the assessment of expertise and the development of training interventions for tactical

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About the Contributors

thinking in military environments. Her current project at the lab focuses on scientific oversight for a program of research and development regarding IED (improvised explosive device) defeat training for the U.S. Marine Corps. At her previous position with Klein Associates, she conducted research and development for the U.S. Army, the U.S. Marine Corps, the U.S. Air Force, and the Office of Naval Research. She recently directed and participated in research to develop a framework of expertise for guiding the development of technology-supported training, an online tool to support subject-matter experts in developing training vignettes, cognitive task analysis for the design of antiterrorism training, a new assessment method for tactical thinking skills, and online training scenarios for coalition warfare. In addition, she conducted research into knowledge management processes for the U.S. Army’s Battle Command Knowledge System. She has developed and conducted workshops on qualitative research methods for the military and industry. In the MDC2 program, she helped to design the experimental study of command decision making. Ross has previously held positions at the U.S. Army Research Laboratory and the Army Research Institute. GARY SAUER is a 1979 graduate of the United States Military Academy, West Point, where he majored in civil engineering. Upon graduation he served 22 years in the U.S. Army in a variety of command and staff positions in which he was instrumental in authoring command and control doctrine, developing and fielding command and control technology, and assessing their uses in the operational and joint environments. In 1998 he was appointed to the Defense Advanced Research Projects Agency (DARPA) as the agency director’s operational liaison. While at DARPA he was instrumental in assisting the agency in the creation of the Future Combat Systems Program with the army. Sauer additionally held the positions of director, Office of Management Operations, and program manager, Future Combat Systems Command and Control (FCS C2, MDC2) through 2005. Currently, he is the director, Combat Identification and Antenna Programs at BAE Systems Inc., responsible for the development of combat identification and advanced antenna solutions to support DOD and HLS requirements. He holds a master of science, business administration, Central Michigan University, and a master of military arts and science, School of Advanced Military Studies. He is also a senior executive fellow, JFK School of Government, Harvard University, and national security fellow, Massachusetts Institute of Technology. RICHARD HART SINNREICH retired from the U.S. Army in June 1990. A 1965 West Point graduate, he earned a master’s degree in foreign affairs from Ohio State University and is a graduate of the U.S. Army’s Command and General Staff College and the National War College. His military service included field commands from battery through division artillery; combat service in Vietnam; teaching at West Point and Fort Leavenworth; tours on the

About the Contributors

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Army, Joint, National Security Council, and SHAPE staffs; and appointment as the first Army Fellow at the Center for Strategic and International Studies. As first deputy director and second director of the army’s School of Advanced Military Studies, he helped write the 1986 edition of the army’s capstone AirLand Battle doctrine and has published widely in military and foreign affairs. Since retiring from military service, he has consulted for a number of defense agencies, including the army’s Training and Doctrine Command, Joint Forces Command, the Institute for Defense Analyses, and the Defense Advanced Research Projects Agency. His defense column in the Lawton (OK) Constitution has been reprinted by the Washington Post, ARMY Magazine, and other journals. His most recent book, with historian Williamson Murray and others, is The Past as Prologue: The Importance of History to the Military Profession, Cambridge University Press, May 2006. He led a team of military experts that advised the MDC2 program and guided the program’s experiments. THOMAS WILK is a lead operations research analyst with the MITRE Corp. in McLean, Virginia. In this capacity, Wilk has led, or been a team member in, analysis teams for several experiments investigating concepts related to future battle command for both the DARPA/Army cosponsored Multi-cell & Dismounted Command & Control Program (MDC2) and the 2006 PdM C4ISR On-the-Move Experiment at Fort Dix, New Jersey. Earlier, Wilk served 14 years as an infantry officer and operations research analyst in the U.S. Army, in a variety of command and staff assignments. His military awards include the Meritorious Service Medal, Army Commendation Medal, Armed Forces Expeditionary Medal, Humanitarian Service Medal, the Joint Meritorious Unit Citation, the Combat Infantryman’s Badge for service in Somalia, and the Expert Infantryman’s Badge. While serving in the infantry, Wilk was Ranger, airborne and air assault qualified. He holds a BS in mechanical engineering (aerospace) from the United States Military Academy and an MS in operations research from the Naval Postgraduate School. His master’s thesis concerned modeling Theater Level ground logistics within a low-intensity combat simulation.

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