Improving the Decision Making Abilities of Small Unit Leaders (2012) / Chapter Skim
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3 Scientific Basis and Engineering Approaches for Improving Small Unit Decision Making
3 Scientific Basis and Engineering Approaches for Improving Small Unit Decision Making
Pages 50-81
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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)
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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)
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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.
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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.
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3.1.1.2 Economic Model Becker contends that "all human behavior can be viewed as involving participants 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.
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"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.
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, 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.
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Marine Corps small unit leaders operate in the kind of complex, uncertain 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)
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1997. "Naturalistic Decision Making and the New Organizational Context," pp.
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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.
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The action learning approach includes four activities: (1) encountering changes in perceptions of the world (hearing)
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42 Over the past 20 years, investigations into how to improve team decision making have revealed several useful strategies.43 In most cases, successful team training involves exposing the team to realistic scenarios that represent the types of problems that it will encounter in the operational environment. Such scenarios, when appropriately designed and paired with effective feedback and debriefing mechanisms, help teams to develop the repertoire of instances necessary to support adaptive team performance.44 40 Janis A
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49 make it possible to learn how to avoid brittleness in the face of uncertainty and unanticipated variability. 3.1.4 Implications from Cognitive Psychology Cognitive psychology brings to bear time-tested knowledge based on scientific inquiry that can address each of the findings in Chapter 2 and support the committee's recommendations in Chapter 4.
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Cognitive psychology can continue to be used to discover and to learn about new challenges that small unit leaders face as the missions and role of the Marine Corps evolve. 3.2 COGNITIVE NEUROSCIENCE Following is a summary of recent work in cognitive neuroscience, which seeks to use insight from functional neuroanatomy to extend theoretical models
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The approach is only beginning to examine what cognitive factors might lead to successful decision making, with recent experiments drawing mainly on tasks involving economic exchange. Clearly, these are relatively con strained compared to the complex decision making environment of the small unit leader (e.g., adaptive decision making in the face of an adversary)
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3.2.2 Implications from Cognitive Neuroscience Cognitive processes used for estimating value and evaluating evidence are potential sources of individual variation that could explain differences in how people make decisions. This possibility has been supported by neuroscience research using fMRI experiments to study tasks that are specifically associated with risk taking,55 avoiding uncertainty,56 and expending effort.57 These studies are particularly important because they provide objective metrics of deci sion making that could -- the word could is emphasized here -- be applied in the assessment of small unit leader decision making "style" or "biases" (e.g., toward or away from risk)
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.60 It remains to be seen if these methods could be used to select for or assess the decision making performance of small unit leaders. 3.3 ENGINEERING APPROACHES TO SUPPORT DECISION MAKING There is a long history of the leveraging of methods from engineering to enhance decision making, through the development of what are called decision aids, and a complete review is beyond the scope of this report.61 Here, the focus is on five areas of opportunity that the committee considered to be the most relevant to the development of decision aids for small unit leaders: (1)
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3.3.1.1 Cognitive Task Analysis Researchers cannot expect decision makers to explain accurately why they have made decisions,64 and so researchers have developed methods to learn from experts. Cognitive task analysis65 is a set of methods, such as semi-structured interviews and observations, that are used to discover the cues and context that influence how people make decisions.
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essays,74 situational awareness (SA) entails "the perception of the critical 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."75 SA enables decision makers to assess the state of the world, important features in a scene, estimates for progress against plan, and adversaries' intentions.
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that employ novel optimization algorithms can help small unit leaders deploy static and dynamic sensor systems so as to maximize information gains. • Sensor signal processing to convert collected data into higher-order elements (objects, events, relationships)
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. However, two related NRC reports80,81 have recently suggested that methodologies such as expert systems82 and case-based reasoning83 might be used to integrate disparate elements into situational assessments to support small unit decision making in complex hybrid engagements.
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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.
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. 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.
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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.
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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.
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, 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.
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. 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.
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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.
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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 uncertainty, and the rate at which new information is acquired during an operation are 124 William D.S.
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2004. "Overview of the DARPA Augmented Cognition, Technical Integration Experiment," International Journal of Human–Computer
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: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.
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The chapter closes with the committee's last finding: FINDING 7: Established and emerging research in human cognition and decision 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.
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