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Scientific Basis and Engineering Approaches for Improving Small Unit Decision Making

The topic of decision making has been studied in a number of fields, and each offers possibilities for improving the decision making abilities of small unit leaders given the operational and technical challenges facing small unit leaders in today’s operational environment, the existing abilities of the Marine Corps, and the findings presented in Chapter 2. The breadth of material related to decision making is substantial and beyond the scope of this report. What follows is a selective review based on the knowledge and experience of the committee members who were particularly interested in reviewing theories and perspectives that could address the operational gaps identified in the previous chapter and that would lead to actionable recommendations. This is an area where committee members are not in unanimous agreement. While the material in this chapter represents the majority opinion, a dissent can be found in Appendix G.

No single theory of decision making or human performance can account for the complex and diverse decisions that small unit leaders must make. The nature of the decision and the context in which it occurs will instead have an effect on which theories (and associated interventions) best support improved decision making performance. The committee focused on two areas: the scientific basis for decision making (cognitive psychology, cognitive neuroscience), and engineering support for decision making (engineering approaches to support decision making, physiological monitoring, and augmented cognition).

Philosophers and historians of science, most notably Thomas Kuhn, describe the evolution of scientific inquiry in any field of knowledge according to phases. Fields that have developed a scientific approach are said to have “paradigms,”1

line

1 Thomas S. Kuhn. 1977. The Essential Tension: Selected Studies in Scientific Tradition and Change, University of Chicago Press, Chicago, Ill., p. 294.



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3 Scientific Basis and Engineering Approaches for Improving Small Unit Decision Making The topic of decision making has been studied in a number of fields, and each offers possibilities for improving the decision making abilities of small unit leaders given the operational and technical challenges facing small unit leaders in today’s operational environment, the existing abilities of the Marine Corps, and the findings presented in Chapter 2. The breadth of material related to decision making is substantial and beyond the scope of this report. What follows is a selec - tive review based on the knowledge and experience of the committee members who were particularly interested in reviewing theories and perspectives that could address the operational gaps identified in the previous chapter and that would lead to actionable recommendations. This is an area where committee members are not in unanimous agreement. While the material in this chapter represents the majority opinion, a dissent can be found in Appendix G. No single theory of decision making or human performance can account for the complex and diverse decisions that small unit leaders must make. The nature of the decision and the context in which it occurs will instead have an effect on which theories (and associated interventions) best support improved decision making performance. The committee focused on two areas: the scientific basis for decision making (cognitive psychology, cognitive neuroscience), and engineering support for decision making (engineering approaches to support decision making, physiological monitoring, and augmented cognition). Philosophers and historians of science, most notably Thomas Kuhn, describe the evolution of scientific inquiry in any field of knowledge according to phases. Fields that have developed a scientific approach are said to have “paradigms,” 1 1 Thomas S. Kuhn. 1977. The Essential Tension: Selected Studies in Scientific Tradition and Change, University of Chicago Press, Chicago, Ill., p. 294. 50

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51 SCIENTIFIC BASIS AND ENGINEERING APPROACHES which are shared commitments to a certain understanding of the real world among members of a scientific group. Paradigms are used to guide the collection of data through normal science.2 Those efforts produce results that are often consistent with (and at times anomalous with respect to) the group’s accepted rules. Those in the group who are bothered by anomalies eventually instigate research efforts to challenge, not to reinforce, the paradigm.3 This perceived failure of the existing rules creates a prelude to the search for new rules. The revolutionary science that ensues strives to develop a new set of rules that will better fit both the accepted and the anomalous data. This effort often involves the pursuit of a new language to represent the new model.4 After the acceptance and adoption of a new paradigm, normal science resumes. Regarding the stages of evolution among the fields of knowledge that this report considers, naturalistic decision making (NDM) has evolved into normal science since its inception in the late 1980s. However, other fields that the chapter refers to are in earlier stages of evolution and have potential to change accepted models of thought. Cognitive neuroscience is an example of a field that may pro - vide a deeper understanding of decision making over the longer term. This chapter is organized in three major sections and closes with a brief summary and the committee’s seventh and final finding. The first major section, “3.1 Cognitive Psychology,” summarizes one aspect of the scientific basis for understanding decision making: the broad field of cognitive psychology, includ - ing prescriptive and descriptive approaches, and the emerging field of resilience theory. The next major section, “3.2 Cognitive Neuroscience,” summarizes a second aspect: the emerging field of cognitive neuroscience and its potential for understanding the fundamental neurophysiological mechanisms underlying human decision making. The last major section, “3.3 Engineering Approaches to Support Decision Making,” provides a broad overview of existing and potential engineering approaches to aiding the decision maker, including approaches to information integration, tactical decision aiding, human-computer interface (HCI) design, and physiological monitoring and augmented cognition. Also included in that section is a brief discussion of human-centered design methods that can help develop promising concepts related to decision aiding into useful and usable decision aids for the small unit leader. 2 Thomas S. Kuhn. 1970. The Structure of Scientific Revolutions, 2nd ed., University of Chicago Press, Chicago, Ill., pp. 25, 68. 3 Gary A. Klein. 1997. “An Overview of Naturalistic Decision Making Applications,” p. 141 in C.E. Zsambok and Gary A. Klein (eds.), Naturalistic Decision Making, Lawrence Erlbaum, Mahwah, N.J. 4 Thomas S. Kuhn. 2000. The Road Since Structure: Philosophical Essays, 1970-1993, University of Chicago Press, Chicago, Ill., p. 30.

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52 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS 3.1 COGNITIVE PSYCHOLOGY Approaches to modeling human decision making behavior have evolved through various phases, as Figure 3.1 shows. According to prescriptive theories, such as economic theory5 and expected utility theory,6 humans consider available options in a formal and systematic way and then “choose the one with the high- est expected return.”7 “Specifying principles and contraints derived from formal or mathematical systems such as deductive logic, Bayesian probability theory, and decision theory,” normative research explores “how people ought to make decisions”; in this vein, “the need to improve decision making arises because human decision makers systematically violate normative constraints.”8 That is, people often do not behave in a manner that is consistent with what is prescribed by rational, optimized models. These normative approaches are described below. In contrast, descriptive models were built to capture specific decision making processes based on the actual behavior of individuals and teams, typically within natural settings. Six cognitive approaches to descriptive modeling of decision making are reviewed below. Finally, this section on cognitive psychology concludes with a discussion of resilience, what it means for decision makers operating in uncertain environments, and how resilience engineering can help improve decision outcomes in uncertain and rapidly changing situations. 3.1.1 Prescriptive Theories 3.1.1.1 Subjective Expected Utility9 Subjective expected utility (SEU) is a mathematical model regarding choice that is at the foundation of most contemporary economics, theoretical statistics, and operations research (OR). Blume and Easley consider SEU as one class of 5 John von Neumann and Oskar Morgenstern. 1947. (2007, 60th Anniversary Edition). Theory of Games and Economic Behavior, Princeton University Press, Princeton, N.J. 6 Ralph L. Keeney and Howard Raiffa. 1993. Decisions with Multiple Objectives: Preferences and Value Tradeoffs, Cambridge University Press, New York. 7 Christopher Nemeth and Gary A. Klein. 2011. “The Naturalistic Decision Making Perspective,” in James J. Cochran (ed.), Wiley Encyclopedia of Operations Research and Management Science, Wiley, New York. 8 Raanan Lipshitz and Marvin S. Cohen. 2005. “Warrants for Prescription: Analytically and Empirically Based Approaches to Improving Decision Making,” Human Factors 47(1):102-120. See also Jonathan Baron, 2007, Thinking and Deciding, Cambridge University Press, New York. 9 This section is taken in large part from: Herbert A. Simon, George B. Dantzig, Robin Hogarth, Charles R. Piott, Howard Raiffa, Thomas C. Schelling, Kenneth A. Shepsle, Richard Thaier, Amos Tversky, and Sidney Winter, 1986, Research Briefings 1986: Report of the Research Briefing Panel on Decision Making and Problem Solving, National Academy of Sciences, National Academy of Engineering, Institute of Medicine, National Academy Press, Washington, D.C.

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53 SCIENTIFIC BASIS AND ENGINEERING APPROACHES FIGURE 3.1 Behavioral modeling methods. SOURCE: © Ashgate Publishing. Reprinted with permission from: Jens Rasmussen. 1997. Figure 2 of Chapter 5, “Merging Paradigms: Decision Making, Management, and Cognitive Control,” in Rhona Flin, Eduardo Salas, Michael Strub, and Lynne Martin (eds.), Decision Making Under Stress: Emerging Themes and Applications, Ashgate Publishing Company, Brookfield, Vt., p. 75. Figure 3-1 Bitmapped decision models for choice under uncertainty, and its dominance was understand - able at a time when few alternatives were available.10 SEU assumes that a decision maker has what is termed a “utility function”— an ordering, by subjective preference, among all of the possible outcomes of a choice. In SEU, all of the alternatives are known among which a choice can be made, and the consequences of choosing each alternative can be determined. SEU theory makes it possible to assign probabilities subjectively, which opens the way to combining subjective opinions with objective data. SEU can also be used in systems that aid human decision making. In the probabilistic version of SEU, Bayes’s rule prescribes how people should take account of new informa- tion and respond to incomplete information. Many of the modern approaches to 10 Lawrence E. Blume and David Easley. 2007. “Rationality,” in Lawrence E. Blume and Steven N. Durlauf (eds.), The New Palgrave Dictionary of Economics, June. Available at http://www. dictionaryofeconomics.com/dictionary. Accessed September 9, 2011.

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54 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS optimizing operations research use assumptions of SEU theory, the major ones being that (1) maximizing the achievement of some goal is desired, (2) this can be done under specified constraints, and (3) all alternatives and consequences (or their probability distributions) are known. Satisfying these assumptions is often difficult or impossible in real-world situations. 3.1.1.2 Economic Model Becker contends that “all human behavior can be viewed as involving partici- pants who maximize their utility from a stable set of preferences and accumulate an optimal amount of information and other inputs in a variety of markets.” 11 The economic, or rational-choice, approach equates human rational behavior with instrumentalist (especially economic) rationality. The rational-choice approach applies this concept to all rational activity and explains human behavior as eco - nomic rationality.12 3.1.1.3 Rational Actor The rational-actor (also rational-choice) theory is used to understand economic and social behavior. In this instance, “rational” signals the desire for more of a good rather than less of it, under the presumption of some cost for obtaining it. Models used in rational-choice theory assume that “individuals choose the best action according to unchanging and stable preference functions and constraints.” 13 These assumptions, however, are often violated under real-world conditions in which models are not rich enough to capture all of the behaviors that one might want to examine,14 and actual behavior is not available for observation in the model. According to Hedström and Stern, rational-choice sociologists typically “use explanatory models in which [individuals] . . . are assumed to act rationally . . . as conscious decision makers whose actions are significantly influenced by the costs and benefits of different action alternatives.”15 Rather than focusing on the actions of single individuals, most rational-choice sociologists seek to explain 11 Gary S. Becker. 1976. The Economic Approach to Human Behavior, University of Chicago Press, Chicago, Ill., p. 14. 12 Milan Zafirovski. 2003. “Human Rational Behavior and Economic Rationality,” Electronic Journal of Sociology. Available at http://www.sociology.org/content/vol7.2/02_zafirovski.html. Accessed September 2, 2011. 13 See an entry title “Rational Choice Theory” at http://en.wikipedia.org/wiki/Rational_choice_ theory. 14 Lawrence E. Blume and David Easley. 2007. “Rationality,” in Lawrence E. Blume and Steven N. Durlauf (eds.), The New Palgrave Dictionary of Economics, June. Available at http://www. dictionaryofeconomics.com/dictionary. Accessed September 9, 2011. 15 Peter Hedström and Charlotte Stern. 2007. “Rational Choice and Sociology,” in Lawrence E. Blume and Steven N. Durlauf (eds.), The New Palgrave Dictionary of Economics, June. Available at http://www.dictionaryofeconomics.com/dictionary. September 9, 2011.

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55 SCIENTIFIC BASIS AND ENGINEERING APPROACHES “macro-level or aggregate outcomes such as the emergence of norms, segregation patterns, or various forms of collective action . . . [by studying] the actions and interactions that brought them about.”16 3.1.1.4 Behavioral Decision Theory In decision theory, making effective decisions relies on “understanding the facts of a choice and the implications of [making that choice] . . . well enough to identify [and carry through with] the option in one’s own best interests” from among the available options.17 As Fischhoff explains, choices are described in terms of the following: Options: “actions that an individual might [or might not] take”; • Outcomes: “valued consequences that might follow from those actions”; • Values: “the relative importance of those outcomes”; and • Uncertainties: “regarding which outcomes will be experienced.”18 • These four elements are synthesized in decision rules that enable a choice among options. As a normative analysis, decision theory “can [help to] clarify the structure of complex choices” by identifying the best courses of action in light of the values that a decision maker holds.19 Fischhoff also suggests that descriptive studies and prescriptive research complement normative analysis and should be used iteratively, because (1) “descriptive research [such as approaches described in the next subsection] is needed to reveal the facts and values that normative analysis must consider,” and (2) “prescriptive interventions are needed to assess whether descriptive accounts provide the insight that is needed in order to improve decision making.”20 3.1.2 Descriptive Models of Human Behavior A significant limitation of much of the early work in decision making theory is that training methods and decision aiding systems that were developed from formal, prescriptive systems (including SEU, economic model, rational-actor, and behavioral decision approaches) neither improved decision quality nor were 16 Peter Hedström and Charlotte Stern. 2007. “Rational Choice and Sociology,” in Lawrence E. Blume and Steven N. Durlauf (eds.), The New Palgrave Dictionary of Economics, June. Available at http://www.dictionaryofeconomics.com/dictionary. September 9, 2011. 17 Baruch Fischhoff. 2005. “Decision Research Strategies,” Health Psychology 24(4):S9-S16. 18 Baruch Fischhoff. 2005. “Decision Research Strategies,” Health Psychology 24(4):S9-S16. 19 Baruch Fischhoff. 2005. “Decision Research Strategies,” Health Psychology 24(4):S9-S16. 20 Baruch Fischhoff. 2010. “Judgment and Decision Making,” WIREs Cognitive Science 1:724-735.

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56 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS adopted in field settings.21 Researchers in human behavior and performance found the tools and prescribed methods difficult to use in their own work.22 This is because field settings are typically complex, emergent, poorly defined, and strongly influenced by context. While they were academically appealing, pre - scriptive theories of decision making were rarely the basis for practical changes that improved decision making.23 As a result, newer approaches began to be developed in the 1980s that have been found to be better suited to understanding and improving decision making behavior in the real world, such as that carried out by Marine Corps small unit leaders. A discussion of these newer approaches is presented in the following sections. 3.1.2.1 Heuristics and Biases The heuristics and biases (HB) approach contends that people do not use strategies in the form of algorithms in order to follow principles of optimal per- formance. Instead, individuals rely on rules of thumb to make decisions under conditions of uncertainty and employ them even when expected utility theory, probability laws, and statistics suggest that an individual is likely to choose cer- tain optimal courses of behavior. These heuristics include representativeness (“in which probabilities are evaluated by the degree to which A is representative of B”), availability of instances or scenarios (“in which people assess the frequency of a class or the probability of an event by the ease with which instances of occurrences can be brought to mind”), and adjustment from an anchor (in which “people make estimates by starting from an initial value that is adjusted to yield the final answer”).24 Although heuristics can be “highly economical and usually effective,” their use can also lead to biases resulting in “systematic and predict - 21 One potential explanation is simply that the prescriptive “models” of human behavior do not, in fact, model human behavior and thus are incompatible with how a human accomplishes the unaided task. A more complete discussion of how decision theory models and game theory models in particular fail in representing human behavior in the “real world” can be found in a recent study: National Research Council, 2008, Greg L. Zacharias, Jean Macmillan, and Susan B. Van Hemel (eds.), Behavioral Modeling and Simulation: From Individuals to Societies, The National Academies Press, Washington, D.C., pp. 195-206. 22 J. Frank Yates, Elizabeth S. Veinott, and Andrea L. Patalano. 2003. “Hard Decisions, Bad Decisions: On Decision Quality and Decision Aiding,” pp. 13-63 in Sandra L. Schneider and James Shanteau (eds.), Emerging Perspectives on Judgment and Decision Research, Cambridge University Press, New York. 23 Although the constrained optimization methods that underlie many of the prescriptive theories have found significant application in the development of tactical decision aids, as described below in the section titled “3.3.3 Tactical Decision Aids for Course of Action Development and Planning.” 24 Amos Tversky and Daniel Kahneman. 1974. “Judgment Under Uncertainty: Heuristics and Biases,” Science 185(4157):1124-1131.

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57 SCIENTIFIC BASIS AND ENGINEERING APPROACHES able errors.”25 The HB approach can benefit activities such as training by enabling decision makers to anticipate and avoid such errors. 3.1.2.2 Naturalistic Decision Making26 The naturalistic decision making approach seeks to understand human cogni - tive performance by studying how individuals and teams actually make decisions in real-world settings rather than in a laboratory. NDM researchers typically focus on mental activities such as decision making and sensemaking strategies, while also trying to be sensitive to the context of a situation. Three major criteria have appeared in the literature to describe research that counts as NDM study: such research (1) focuses on expertise, (2) takes place in field (not laboratory) settings, and (3) reflects the conditions such as complexity and uncertainty that complicate our lives. Marine Corps small unit leaders operate in the kind of complex, uncer- tain environment for which the NDM approach is a good fit. NDM has focused on the importance of intuition, as well as on two key models: recognition-primed decision making (RPD) and the data-frame theory (DFT) of sensemaking. 3.1.2.3 Intuition The lay person routinely thinks of intuition as knowledge or belief that is obtained by some means other than reason or perception. In fact, intuition is tacit knowledge, or expertise, that comes from experience. Intuition relies on experi - ence to recognize key patterns that indicate the dynamics of a situation.27 NDM research has helped to “demystify intuition by identifying the cues that experts use to make their judgments, even if those cues involve tacit knowledge and are difficult for the expert to articulate.”28 Intuition-based models account for how people use their experience to rapidly categorize situations, relying on “some kind of synthesis of their experience to make . . . judgments.” 29 These situation categories, implicitly or explicitly, then suggest appropriate courses of action. 30 25 Amos Tversky and Daniel Kahneman. 1974. “Judgment Under Uncertainty: Heuristics and Biases,” Science 185(4157):1124-1131. 26 This section is taken nearly verbatim from: Christopher Nemeth and Gary A. Klein, 2011, “The Naturalistic Decision Making Perspective,” in James J. Cochran (ed.), Wiley Encyclopedia of Operations Research and Management Science, Wiley, New York. 27 Gary A. Klein. 1999. Sources of Power, MIT Press, Cambridge, Mass. 28 Daniel Kahneman and Gary A. Klein. 2009. “Conditions for Intuitive Expertise: A Failure to Disagree,” American Psychologist 64(6):515-526. 29 Christopher Nemeth and Gary A. Klein. 2011. “The Naturalistic Decision Making Perspective,” in James J. Cochran (ed.), Wiley Encyclopedia of Operations Research and Management Science, Wiley, New York. 30 Raanan Lipshitz and Marvin S. Cohen. 2005. “Warrants for Prescription: Analytically and Empirically Based Approaches to Improving Decision Making,” Human Factors 47(1):102-120.

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58 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS 3.1.2.4 Recognition-Primed Decision Making For the past two decades, the Marine Corps has subscribed in varying degrees to the recognition-primed decision making model of human decision making. Given the changing nature of small unit operations as described in Chapters 1 and 2, the use of this model is worth reconsidering. Developed from NDM research, the RPD model (Figure 3.2) “describes how people use their experience in the form of a repertoire of patterns. The patterns highlight the most relevant cues [in a situation], provide expectancies, identify plausible goals, and suggest typical types of reactions.”31 The decision maker relies on specific content expertise and experience.32 The RPD model blends pat- tern matching (intuition as described above) and analysis (specifically, by means of mental simulation).33 In the RPD model, people who “need to make a decision . . . can quickly match the situation [that they confront] to the patterns they have learned. If they find a clear match [between the situation and a learned pattern], they can carry out the most typical course of action. They do not evaluate an option by comparing it to others, but instead imagine—mentally simulate—how [the action] might be carried out, . . . [making it possible to] successfully make very rapid decisions . . . [I]n-depth interviews with fire ground commanders about recent and challenging incidents . . . [have shown] that the percentage of [times that] RPD strategies [were used] generally ranged from 80% to 90%.”34, 35 3.1.2.5 Data-Frame Theory of Sensemaking Sensemaking is the exploitation of information under conditions of uncer- tainty, complexity, and time pressure in order to support awareness, understand - ing, planning, and decision making. Individuals and teams with superior sense - making abilities can be expected to handle situations better in spite of uncertainty and information overload, to make faster and better decisions with regard to an 31 Christopher Nemeth and Gary A. Klein. 2011. “The Naturalistic Decision Making Perspective,” in James J. Cochran (ed.), Wiley Encyclopedia of Operations Research and Management Science, Wiley, New York; Gary A. Klein. 2008. “Naturalistic Decision Making,” Human Factors 50(3):456-460. 32 Terry Connolly and Ken Koput. 1997. “Naturalistic Decision Making and the New Organizational Context,” pp. 285-303 in Zur Shapira (ed.), Organizational Decision Making, Cambridge University Press, Cambridge, U.K. 33 Gary A. Klein. 1993. “Recognition-Primed Decision (RPD) Model of Rapid Decision Making,” pp. 138-147 in Gary A. Klein, Judith Orasanu, Roberta Calderwood, and Caroline E. Zsambok (eds.), Decision Making in Action, Wiley, Norwood, N.J. 34 Christopher Nemeth and Gary A. Klein. 2011. “The Naturalistic Decision Making Perspective,” in James J. Cochran (ed.), Wiley Encyclopedia of Operations Research and Management Science, Wiley, New York; Gary A. Klein. 2008. “Naturalistic Decision Making,” Human Factors 50(3):456-460. 35 Gary A. Klein. 1998. Sources of Power: How People Make Decisions, MIT Press, Cambridge, Mass.

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59 SCIENTIFIC BASIS AND ENGINEERING APPROACHES FIGURE 3.2 Recognition-primed decision making model. SOURCE: Gary A. Klein. 1989. “Recognition-Primed Decisions,” pp. 47-92 in Advances in Man-Machine Systems Research, W.B. Rouse (ed.), Vol. 5, JAI Press, Greenwich, Conn. Figure 3-2 Bitmapped adversary, and to prevent fundamental surprise.36 Success in seeking and using information is essential to sensemaking because this behavior responds to, and is mandated by, changing situational conditions.37 The data-frame theory of sensemaking (Figure 3.3) describes the process of fitting data into a frame (a story, script, map, or plan) and fitting a frame around the data.38 Context informs how an individual views and handles new informa- tion. A frame provides cues, goals, and expectancies and guides attention toward data that are of interest to the frame. Experience-based knowledge helps to create 36 Gary A. Klein, David Snowden, Chew Lock Pin, and Cheryl A. Teh. 2007. “A Sense Making Experiment—Enhanced Reasoning Techniques to Achieve Cognitive Precision,” paper presented at 12th International Command and Control Research and Technology Symposium, Singapore. 37 Brenda Derwin. 1983. “An Overview of Sense-Making Research: Concepts, Methods, and Results to Date,” paper presented at International Communication Association Annual Meeting, Dallas, Tex., May. 38 Gary A. Klein, Jennifer K. Phillips, Erica L. Rall, and Deborah A. Battaglia. 2003. “A Summary of the Data/Frame Model of Sensemaking,” Proceedings of Human Factors of Decision Making in Complex Systems, University of Abertay, Dundee, Scotland.

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60 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS FIGURE 3.3 The data-frame theory of sensemaking. SOURCE: © IEEE. Reprinted with permission from: Gary A. Klein, Brian Moon, and Robert R. Hoffman. 2006. “Making Sense of Sensemaking, 2: A Macrocognitive Model,” IEEE Intelligent Systems 21(5):89. Figure 3-3 an emergent frame, which in turn informs the significance of new information. Bitmapped The frame becomes a sort of dynamic filter that can be questioned, compared to other frames, or elaborated and enriched, as an individual continuously seeks to assess the situation. The deliberate construction and use of information in sensemaking find paral- lels in Revans’s action learning, in which individuals learn with and from others by studying their own actions and experience in order to improve performance. The action learning approach includes four activities: (1) encountering changes in perceptions of the world (hearing); (2) the exchange of information, advice, criticisms, and other forms of influence (counseling); (3) taking action in the world with deliberately designed plans (managing); and (4) following the five stages of the scientific method (authentication).39 3.1.2.6 Team Cognition The notion that team members must share knowledge about their task and each other has been studied for more than 20 years. “Team cognition,” or shared 39 DavidBotham. 1998. “The Context of Action Learning,” in Wojciech Gasparski and David Botham (eds.), Action Learning. Praxiology, The International Annual of Practical Philosophy and Methodology 6:33-61, Transaction Publishers, New Brunswick, N.J.

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71 SCIENTIFIC BASIS AND ENGINEERING APPROACHES ing adversarial intentions and objectives. Finally, the DOD is investing in new methods and techniques to support nonkinetic SA; for example, in the Human Socio-Cultural Behavior (HSCB) modeling program managed by the Office of Naval Research.89 It must be recognized, however, that weakly constrained “non - kinetic” situations present immense methodological and technical challenges for modeling and simulation that must be addressed if programs such as HSCB are to be successful. • Forecasting may benefit from the development of computational models and simulations that have a sound theoretical basis and have undergone rigorous verification and validation in the intended operational scenario. Forecasting also requires that systems exploit up-to-date information in order to ensure operational relevance and accuracy. Many “kinetic” red force (adversary) tracking and projec- tion models have been designed with these capabilities, but providing the same level of reliability in less constrained, “nonkinetic” situations is an immensely more difficult problem. In summary, accurate SA enables the decision maker to monitor events, to determine if the objectives and constraints of a current operational plan or solution are being followed, and perhaps to detect unforeseen opportunities that support additional goals or objectives. The decision maker may choose to pursue the cur- rent plan, or create a new plan to accommodate emerging problems, or capitalize on new opportunities (regarding replanning, see below). Given today’s high- resolution sensors, low-cost flight control and guidance systems, and computing power, such aids are not unrealistic, although they require significant develop - ment, testing, and verification and validation. 3.3.3 Tactical Decision Aids for Course-of-Action Development and Planning Marine Corps Doctrinal Publication One (MCDP1) states: Decision making may be an intuitive process based on experience. This will likely be the case at lower levels and in fluid, uncertain situations. Alternatively, decision making may be a more analytical process based on comparing several 89 See http://www.onr.navy.mil/Science-Technology/Departments/Code-30/All-Programs/Human- Behavioral-Sciences.aspx. Accessed December 3, 2011. The HSCB program seeks to understand the human, social, cultural, and behavioral factors that influence human behavior; improve the ability to model these influences and understand their impact on human behavior at the individual, group, and society level of analysis; improve computational modeling and simulation capabilities, visualization software tool sets, and training and mission rehearsal systems that provide forecasting capabilities for sociocultural responses; and develop and demonstrate an integrated set of model description data (metadata), information systems, and procedures that will facilitate assessment of the software engineering quality of sociocultural behavior models, their theoretical foundation, and the translation of theory into model constructs.

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72 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS options. This will more likely be the case at higher levels or in deliberate plan - ning situations.90 Tactical decision aids (TDAs) have been used to support military deci- sion making for many years. For example, some TDAs have employed case- based reasoning to generate potential courses of action (COAs); examples are BattlePlanner,91 JADE (Joint Assistant for Deployment and Execution),92 and HICAP (Hierarchical Interactive Case-based Architecture for Planning). 93 Others use high-level modeling and simulation, including qualitative reasoning, to help decision makers evaluate COAs.94,95 Such technologies, however, are geared for situations that afford deliberative information processing and assessment—for example, to support decision makers at higher command echelons in assessing order of battle. In contrast, small units engage in both deliberative planning and rapid, high-consequence decision making in real time. The former affords time and resources for deliberate information collection and processing, but the latter does not. Small units may benefit from TDAs that support both modes and which provide small unit leaders with a “playbook” of cues and frameworks to support the accurate and efficient assessment of incoming information, as discussed above in the description of RPD models of decision making. Inexpensive and powerful computers, coupled with the development of effi - cient algorithms,96 mean that portable TDAs may provide small unit leaders with access to efficient and useful optimization techniques. Methods from operations research,97 including optimization formulations (e.g., mathematical programming, dynamic programming) and associated algorithms (e.g., the many variants of the simplex method, branch-and-bound, interior point methods, approximate dynamic programming) have been incorporated into TDAs to identify and evaluate near- best solutions, given constraints such as task scheduling, resource availability, and 90 Gen Charles C. Krulak, USMC, Commandant of the Marine Corps. 1997. Warfighting, Marine Corps Doctrinal Publication One, Washington, D.C., June 20, pp. 85-86. 91 Marc Goodman. 1989. “CBR in Battle Planning,” in Proceedings of the Second Workshop on Case-Based Reasoning, Pensacola Beach, Fla. 92 Alice M. Mulvehill and Joseph A. Caroli. 1999. “JADE: A Tool for Rapid Crisis Action Planning,” in Proceedings of the 4th International Command and Control Research and Technology Symposium , Providence, R.I. 93 Hector Muñoz-Avila, David W. Aha, Leonard A. Breslow, and Dana S. Nau. 1999. “HICAP: An Interactive Case-Based Planning Architecture and Its Application to Noncombatant Evacuation Operations,” in Proceedings of the Ninth Conference on Innovative Applications of Artificial Intelligence, AIAA Press, Orlando, Fla. 94 Johan de Kleer and Brian C. Williams (eds.). 1991. Artificial Intelligence Journal 51 (Special Issue on Qualitative Reasoning About Physical Systems II). 95 Benjamin J. Kuipers. 1994. Qualitative Reasoning: Modeling and Simulation with Incomplete Knowledge. MIT Press, Cambridge, Mass. 96 Jorge Nocedal and Stephen J. Wright. 2006. Numerical Optimization, Springer, New York. 97 Wayne Winston. 2004. Operations Research: Applications and Algorithms, Duxbury Press, Belmont, Calif.

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73 SCIENTIFIC BASIS AND ENGINEERING APPROACHES risk. More recently, genetic and evolutionary algorithms,98,99,100 as well as distrib- uted agent-based approaches such as market-based optimization,101 have provided new techniques to support tactical decision making. Aids that incorporate these techniques can generate “satisficing” solutions relatively quickly, but they also allow more optimal solutions to emerge over time. In addition, such methods can be relatively robust to uncertainty, data staleness, and brittleness of the optimum, all of which are problematic for traditional OR-based approaches. Small units may benefit from technologies that incorporate such methods. Tactical decision aids that incorporate novel optimization algorithms might also be very useful for deliberative planning at the small unit level. For example, the resupply of dispersed units can present significant logistical challenges, but TDAs could be developed to help company commanders ensure that their units have the required materiel. A route-planning TDA could search among possible convoy routes to satisfy traversability constraints, minimize travel time, and maximize protection from possible threats. When the decision maker receives new threat information, the TDA would support modification of the route, just as a vehicle driver might modify a route proposed by Google Maps when learning of a road closure due to, say, flooding. Similarly, a TDA might help small unit leaders manage sensor arrays and collection assets in order to maximize the probability of interdicting insurgents, or help them in making decisions about distributing improvised explosive device (IED) clearance assets over a road network.102 In such scenarios, optimization techniques may be useful in helping small unit leaders generate sets of possible actions with estimates of relative “goodness” with respect to mission objectives, as made explicit to the TDA. TDAs might also have a role in rapidly unfolding situations, such as those encountered by small units when hybrid engagements shift from nonkinetic to kinetic states. As discussed in Chapter 2, many of the difficult decisions faced by Marines are associated with the question of whether to employ fires, given the risk of collateral damage. A very simple TDA could help the small unit leader assess the probability of overall physical damage in a target zone, while a more infor- mative aid could estimate the probability of damage to specific intended targets 98 David E. Goldberg. 1989. Genetic Algorithms in Search, Optimization and Machine Learning, Kluwer Academic Publishers, Boston, Mass. 99 David E. Goldberg. 2002. The Design of Innovation: Lessons from and for Competent Genetic Algorithms, Addison-Wesley, Reading, Mass. 100 David B. Fogel. 2006. Evolutionary Computation: Toward a New Philosophy of Machine Intelligence, 3d ed., IEEE Press, Piscataway, N.J. 101 Dan Schrage, Christopher Farnham, and Paul G. Gonsalves. 2006. “A Market-Based Optimization Approach to Sensor and Resource Management,” in Proceedings of SPIE Defense and Security, Vol. 6229, Orlando, Fla., April. 102 Alan R. Washburn and P. Lee Ewing. 2011. “Allocation of Clearance Assets in IED Warfare,” Naval Research Logistics 58(3):180-187.

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74 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS and associated collateral features. An enhanced map showing relative locations of true and collateral targets, together with damage probability contours, might allow small unit leaders to make more efficient and reliable risk assessments, as opposed to their recalling and mentally processing relevant factors while under stress. In either case, the applicable mathematics are well understood, 103 and the required computations would be easily performed on a handheld or laptop device. It is not difficult to provide additional examples of TDAs that could be used effectively by the small unit leader. However, there are important caveats: • As noted earlier, the “front-end” analysis (e.g., CTA, CSE) is required in order to clearly identify the problem being addressed. This must be done before any technical formulation or algorithm development. Doing it the other way round, and attempting to make the TDA “user-friendly” after the fact, is a sure route to another discarded tactical tool. • A TDA designed for COA development or mission planning will only be as good as the assessed-situation data feeding it. If the TDA is “optimizing” for the wrong situation, the aiding that it offers may be worse than none at all. • Critical attention needs to be paid to what is being “optimized” and what assumptions are being made by the optimization algorithms. If the optimization metric is not the same as that being implicitly held by the user operating the TDA, and/or if the TDA design assumptions are being violated by the actual scenario of use, then the TDA advice is unlikely to be optimal in any sense of the word. • Consideration should be given to other factors in the design of the TDA besides optimality in some predefined solution space. For example, robustness of the proposed solution104 may be much more important than optimality if the oper- ating context is fraught with uncertainty. Likewise, if the user cannot understand the solution logic (“Why did it suggest that???”), a simpler but less optimizing technique may be more appropriate. Some decisions may be better supported by explanatory capabilities that enable the user to trace the TDA’s “reasoning.” In addition, effective visualization modes must be developed. Map-based aids are the most popular, but some situations call for totally novel representations (e.g., influence analysis may call on social network visualization, logistics planning on Gantt charts, etc.). Finally, ease of training on how to use the new TDA 105 and its ease of integration into the existing operations will both be strong determinants of technology adoption. 103 Alan R. Washburn and Moshe Kress. 2009. Combat Modeling in the International Series in Operations Research and Management Science, Springer, New York. 104 That is, the sensitivity of the solution payoff to unpredictable or uncontrollable variations in the solution space. 105 With today’s “20-something” users expecting to need no training at all in view of their consumer- electronic experiences, significant “usability” issues need to be addressed by future TDA developers.

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75 SCIENTIFIC BASIS AND ENGINEERING APPROACHES 3.3.4 Human-Computer Interaction: Displays and Controls Although the above considerations for successful TDA design and deploy - ment are broad, general, and certainly not exhaustive, there is an extensive, pre - scriptive, and empirically validated body of specific knowledge that exists under the rubric of what is called human-computer interaction, or HCI. The Association for Computing Machinery defines human-computer interaction as “a discipline concerned with the design, evaluation and implementation of interactive comput - ing systems for human use and with the study of major phenomena surrounding them.”106 Many of the science and technology HCI “products” come in the form of best practices by HCI designers and evaluators (e.g., the guidelines noted above). Many more, however, are summarized in formalized guidelines, textbooks, and handbooks,107,108,109 which cover topics that range from the “shallow,” interfaced- focused topics of how to deal with, in the present case, the TDA interface between human and computer (regarding displays and controls; see below), to the “deep,” under-the-hood topics dealing, on the computer side, with issues like opacity of operation, trustworthiness of the computations, and so on, and on the human side with issues like the operator’s skill level, that person’s mental model of the TDA, and so on. Interface displays have primarily focused on visual modality, and display guidance has ranged from very early work in the 1940s on the design of good displays for the aircraft cockpit,110,111 to work in the 1990s focusing on the development of a consistent design framework for visualizing different classes of information,112,113 to current efforts for displaying high-dimensional data sets with complex relationships between data entities. In this last category, a relevant 106 See http://old.sigchi.org/cdg/cdg2.html#2_11. Accessed December 3, 2011. 107 Andrew Sears and Julie A. Jacko (eds.). 2008. The Human-Computer Interaction Handbook: Fundamentals, Evolving Technologies and Emerging Applications, 2d Ed., CRC Press, New York. 108 Christopher D. Wickens, John D. Lee, Yili Liu, and Sallie E. Gordon Becker. 2004. An Introduction to Human Factors Engineering, 2d ed., Pearson Prentice Hall, Upper Saddle River, N.J. 109 See also the Association for Computing Machinery Special Interest Group on Human-Computer Interaction Bibliography: Human-Computer Interaction Resources for links to more than 65,000 related publications. Available at http://hcibib.org. Accessed December 3, 2011. 110 L.F.E. Coombs, 1990, The Aircraft Cockpit: From Stick-and-String to Fly-by-Wire, Patrick Stephens Limited, Wellingborough; see also L.F.E. Coombs, 2005, Control in the Sky: The Evolution and History of The Aircraft Cockpit, Pen and Sword Books Limited, Barnsley, U.K. 111 Mary L. Cummings and Greg L. Zacharias. 2010. “Aircraft Pilot and Operator Interfaces,” in Richard Blockley and Wei Shyy (eds.), Encyclopedia of Aerospace Engineering, Vol. 8, Wiley, Hoboken, N.J. 112 Sig Mejdal, Michael E. McCauley, and Dennis B. Beringer. 2001. Human Factors Design Guidelines for Multifunction Displays, DOT/FAA/AM-01/17, Office of Aerospace Medicine, Washington, D.C. 113 Ben Schneiderman. 1996. “The Eyes Have It: A Task by Data Type Taxonomy for Information Visualizations,” Proceedings of IEEE Symposium on Visual Languages, Boulder, Colo.

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76 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS concern in today’s hybrid environment is the presentation of complex information associated with a large social network, consisting of multiple categories of entities (nodes) connected by multiple types of relationships (links). Algorithmic-centric approaches often take the tack of reducing node and link complexity, computing simple social network analysis measures114 such as node centrality, and displaying abstracted two-dimensional representations of the networks with their associated measures. In contrast, visualization-centric approaches attempt to maintain the full network complexity, and present it in its full richness by means of innovative information-coding schemes (color, luminosity, size, animation, etc.). Examples of this “algorithmic-averse” approach, in which the human does the network parameter extraction, can be found at many web sites.115 Finally, it is important to note that, in the right operational context, the visual modality may not be the best way to display information (hence, auditory alarms), and other modalities should be considered. Indeed, there is a push toward multimodality displays (combined visual, auditory, haptic, etc.) in certain cases, and there are emerging guidelines for their use and design.116 The development of interface controls does not have as rich a history as that of the display side, except perhaps in highly constrained environments like the aircraft cockpit. In the aircraft cockpit, manual controls have evolved from crude direct linkages from hands and feet to the control surfaces, to exquisitely complex fly-by-wire hand controllers augmented by dozens of on-stick switches and but - tons, some dedicated to controlling the functionality of others. 117 Transition of interface controls to the ground-based warfighter has happened at a considerably slower pace, but it is happening. As discussed just a few years ago: These technologies include spatial auditory displays, skinbased haptic and tactile displays, and automatic speech recognition (ASR) voice input controls. When used by themselves or collectively, displays involving more than one sensory modality (also known as multimodal displays) can enhance soldier safety [and effectiveness] in a wide variety of applications.118 Potential clearly exists for improving the controls side of the interface, especially in the demanding environments faced by today’s Marines. Right now, 114 David Knoke and Song Yang. 2008. Social Network Analysis, 2d ed., Sage Publications, Thousand Oaks, Calif. 115 F or example, http://socialmediatrader.com/10-amazing-visualizations-of-social-networks. Acessed December 3, 2011. 116 Leah M. Reeves, Jennifer Lai, James A. Larson, Sharon Oviatt, T.S. Balaji, Stéphanie Buisine, Penny Collings, Phil Cohen, Ben Kraal, Jean-Claude Martin, Michael McTear, T.V. Raman, Kay M. Stanney, Hui Su, and QianYing Wang. 2004. “Guidelines for Multimodal User Interface Design,” Communications of the Association for Computing Machinery 47(1):57-59. 117 See, for example, the F-22 controls description, at http://www.f22fighter.com/cockpit.htm#Hands- On%20Throttle%20and%20Stick%20%28HOTAS%29. Accessed December 3, 2011. 118 Ellen C. Haas. 2007. “Emerging Multimodal Technology,” Professional Safety, December. Available at http://www.asse.org. Accessed December 3, 2011.

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77 SCIENTIFIC BASIS AND ENGINEERING APPROACHES the commercial world is leading the development, replacing the mouse/cursor paradigm that it introduced 30 years ago with direct visual manipulation afforded by touch-sensitive screens and multitouch gestures like “pinch” and “swipe,” 119 and, most recently, by a voice-recognition technology, Siri, introduced by Apple on its iPhone 4S.120 Significant potential exists in improving the control side of TDAs in the next several years—not only by an improvement in the effectiveness of the operator’s control of the TDA and the visualization of its data, but also by a qualitative change in the nature of the interaction. This change, in effect, would move the operator from a “batch” mode of directing algorithmic processing of a given data stream toward a “real-time” mode of interaction whereby the computer and operator mutually inform and interact with each other to arrive at a “solution” to the tactical problem at hand. Improving the control side of the interaction is critical to making this happen. 3.3.5 Physiological Monitoring and Augmented Cognition 3.3.5.1 Physiological Monitoring As summarized in Chapters 1 and 2, decision making by the small unit leader is executed under extremely challenging physiological states and stresses, includ - ing sleep deprivation, fatigue, anxiety, and fear. All of these stressors can have an effect on basic cognitive capacities such as sustained attention. 121 In addition, the maximal tolerated stress will vary with different individuals.122 Decision making performance could vary both among different individuals and at different times for the same individual. The most prevalent and widely studied stressor in battlefield operations is sleep deprivation or disruption that destructively impacts on the restorative properties of sleep. For example, research has shown that long periods of sleep deprivation (40 hours of sleeplessness) have a profound effect on the ability of a sharpshooter to select out one hostile target as being differ- ent from four neutral or friendly targets with minimal effects on single-target marksmanship performance.123 Other studies have shown that sleep deprivation also impacts moral decision making tasks. After 53 hours of sleep deprivation, 119 See http://www.apple.com/macosx/whats-new/gestures.html. Accessed December 3, 2011. 120 See http://www.apple.com/iphone/features/siri.html. Accessed December 3, 2011. 121 Peter A. Hancock and Joel S. Warm. 1989. “A Dynamic Model of Stress and Sustained Attention,” Human Factors 31(5):519-537. 122 Peter A. Hancock and James L. Szalma. 2008. Performance Under Stress (Human Factors in Defense), Ashgate Publishing, Surrey, U.K. 123 National Research Council. 2009. Opportunities in Neuroscience for Future Army Applications, The National Academies Press, Washington, D.C., p. 53.

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78 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS there is impairment in the ability to integrate emotion and cognition information to guide moral judgments.124 Biological markers, or biomarkers, provide an empirical approach to assess - ing physiological states of the decision maker independent of that person’s deci - sion making performance.125 Several biomarkers can potentially indicate that a small unit leader is at risk for degraded behavioral performance. These biomarkers include peripheral measures of the autonomic nervous system, including changes in the electrical properties of skin (galvanic skin response), changes in the contrac - tion of the heart (reduction of the cardiac QT interval), and increases in pulse and respiratory rate, as shown in Table 3.1. Changes in the central nervous system can be measured by electroencephalography (EEG). There can be a reduction in the alpha (10 Hz) wave of the power spectrum of background activity and reductions in evoked electrical responses to stimuli, such as a reduced positive wave near the visual areas (the P300). Blood levels of essential hormones and proteins (such as serum cortisol and acute-phase serum protein levels) can also change with physi - ologic stress. Each of these biomarkers measures effects over different timescales. Some are relatively transient (measured in minutes) in their association with performance degradation, others have a somewhat longer half-life (measured in hours to days) associated with loss of performance, and yet others are long-lasting indicators (measured in weeks or longer) of performance shift. Unfortunately, common interventions such as prescribed or limited work- shift duration that are employed in other professions, including aviation and medicine, are not readily applicable to hybrid warfare. The motivation for using physiologic monitoring is that a dynamic measure of stress or mental state could be used to introduce restorative interventions adaptively, contingent on the task or context. The biomarkers could potentially be used to establish boundary con - ditions or reasonable physiological states in which decision making is reliable. One outcome of this research would be the measurement of a set of biomarkers in the deployed leader, providing a mechanism to anticipate potential positive and negative responses to threat and thereby to allow mitigation of undesired states, such as poor decision making. 3.3.5.2 Augmented Cognition As noted above, decision making is critically dependent on an ability to integrate the available evidence. Prior knowledge, experience, the level of uncer- tainty, and the rate at which new information is acquired during an operation are 124 William D.S. Kilgore, Desiree B. Kilgore, Lisa M. Day, Gary H. Kamimori, and Thomas J. Balkin. 2007. “The Effects of 53 Hours of Sleep Deprivation on Moral Judgments,” SLEEP 30(3):345-352. 125 Additional discussion of the relation of biomarkers to stress and decision making outcomes is presented in Appendix F.

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79 SCIENTIFIC BASIS AND ENGINEERING APPROACHES TABLE 3.1 Biomarkers, Stress Indicators, and Device Requirements for Stressors on the Battlefield Biomarkers Stress Indicators Device Requirements Cardiac QT interval Decreased QT interval with Portable electrocardiogram— increased stress two leads with personal digital assistant (PDA) Pulse and respiratory rates Increased rates with increased Arm and chest electrodes with stress PDA Reduction in slow wave with Reduction in slow wave Electrodes placed to occipital eyes closed associated with fatigue or region with PDA attention loss Change in P300 of visual Marked change associated Small light emitter on glasses evoked potential with fatigue or attention loss with two electrode leads and PDA Cortisol and acute-phase Marked changes with stress Transdermal measure proteins some of the critical factors influencing how evidence is integrated when a person is making a choice.126 In addition, time-varying internal “states” of the operator, which may be associated with workload, performance anxiety, task stress, and a number of other factors, have been hypothesized to affect information integration and decision making task performance.127,128 Accordingly, the Defense Advanced Research Projects Agency (DARPA) initiated the Augmented Cognition program in 2001,129 which at its inception was an investigation of the feasibility of using psychophysiological measures of cognitive activity to guide the behavior of human–computer interfaces. The goal is to increase the effectiveness of system operators by managing the information presented to them and the tasks assigned to them based on the available cognitive capacity of the operator.130 126 Philippe Domenech and Jean-Claude Dreher, 2010, “Decision Threshold Modulation in the Human Brain,” Journal of Neuroscience 30:14305-14317; Emily R. Stern, Richard Gonzalez, Robert C. Welsh, and Steven F. Taylor, 2010, “Updating Beliefs for a Decision: Neural Correlates of Uncertainty and Underconfidence,” Journal of Neuroscience 30:8032-8041. 127 See http://www.augmentedcognition.org/history.htm. Accessed December 3, 2011. 128 Peter A. Hancock and Joel S. Warm, 1989, “A Dynamic Model of Stress and Sustained Attention,” Human Factors 31(5):519-537; also Peter A. Hancock and James L. Szalma, 2008, Performance Under Stress (Human Factors in Defense), Ashgate Publishing, Surrey, U.K. 129 Leah M. Reeves, Dylan D. Schmorrow, and Kay M. Stanney. 2007. Augmented Cognition and Cognitive State Assessment Technology—Near-Term, Mid-Term, and Long-Term Research Objectives, Springer, New York. 130 Mark St. John, David A. Kobus, and Dylan Schmorrow. 2004. “Overview of the DARPA Augmented Cognition, Technical Integration Experiment,” International Journal of Human–Computer

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80 IMPROVING THE DECISION MAKING ABILITIES OF SMALL UNIT LEADERS At the time, the program focused on the evaluation of 20 psychophysiological measures from 11 different research groups, including functional Near Infrared imaging, continuous and event-related electrical encephalography, pupil dilation, mouse pressure, body posture, heart rate, and galvanic skin response.131 The first phase of the program demonstrated “a great potential for a number of psychophysiological gauges to sensitively and consistently detect changes in cognitive activity during a relatively complex command and control-type task.” It was thought at DARPA that, with sufficient development, several of the sensors could be brought out of the laboratory into the field. The second phase of the pro - gram has moved into incorporating these measures into prototypes of operational systems to further demonstrate the utility of measuring cognitive activity as a basis for augmenting that activity. The Office of Naval Research, the Army Research Office, and the Army Research Laboratory are continuing with components of the Augmented Cogni - tion program that DARPA started, to facilitate information integration, accelerate learning, and increase workload capacity.132 Emerging research also suggests the potential for fMRI, EEG, and magnetoencephalography (MEG) methods to identify processes that support abstract decision making, including creative think- ing.133 Because of low cost and potential portability, increasing emphasis is given to EEG solutions to augment cognition. Methods involving brain mapping could also lead to the design of behavioral, immersive, or adaptive training algorithms that are applicable to small unit leaders.134 3.4 SUMMARY AND FINDING This report deals with the conduct of enhanced company operations in hybrid environments that are complex, contingent, and variably bounded. It calls for understanding and aiding difficult levels of decision making that span tactical operations, coordination, logistics, cross-cultural negotiation, and more. Deci - Interaction 17(2):131-149. 131 Mark St. John, David A. Kobus, and Dylan Schmorrow. 2004. “Overview of the DARPA Augmented Cognition, Technical Integration Experiment,” International Journal of Human–Computer Interaction 17(2):131-149. 132 LCDR Joseph Cohn, USN, “Some Thoughts on Improving the Decision Making Abilities of Small Unit Leaders,” presentation to the committee, Washington, D.C., September 28, 2010; COL Steven Chandler, USA, “Human Dimension: Optimizing Individual Performance for More Effective Small Units,” presentation to the committee, Washington, D.C., September 28, 2010. 133 LCDR Joseph Cohn, USN, “Some Thoughts on Improving the Decision Making Abilities of Small Unit Leaders,” presentation to the committee, Washington, D.C., September 28, 2010. 134 Raja Parasuraman, James Christensen, and Scott Grafton (eds.). 2012. “Neuroergonomics: The Human Brain in Action and at Work,” NeuroImage 59(1):1-153.

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81 SCIENTIFIC BASIS AND ENGINEERING APPROACHES sions in that setting are made on a collection of many variables, not just one, and often involve trade-offs based on context, mission, and judgment. Both science and engineering provide a basis for insights that can improve the decision making abilities of small unit leaders. This chapter has reviewed selected traditional and evolving approaches to cognitive psychology and cogni - tive neuroscience as the scientific basis for decision making. It has also discussed the roles that information integration, tactical decision aiding, and physiological monitoring can play in engineering support for decision making. The chapter closes with the committee’s last finding: FINDING 7: Established and emerging research in human cognition and deci- sion making is highly relevant to developing approaches and systems that sup - port small unit decision making. Cognitive psychology can provide significant guidance in developing technologies that support the decision maker, including approaches to information integration, tactical decision aids, and physiological monitoring and augmented cognition. However, technologies that do not incor- porate human-centered design methods—such as those of cognitive systems engineering—may not generate useful and usable in-theater decision aids for the small unit leader. Lastly, the emerging field of cognitive neuroscience may have significant potential for developing the understanding of the fundamental neuro - physiological mechanisms underlying human decision making. Although research in this area is very new, over the next few decades it may generate a fundamental paradigm change in scientific approaches to understanding human perception, sensemaking, and decision making.