Complex Operational Decision Making in Networked Systems of Humans and Machines A Multidisciplinary Approach (2014) / Chapter Skim
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Chapter 3 Human Elements of Team Decision Making
Chapter 3 Human Elements of Team Decision Making
Pages 23-36
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From page 23... ...
This chapter focuses on several elements that affect human teams, such as decision analysis, trust, memory, and accounting for human error; it ends with a brief discussion on task allocation. DECISION ANALYSIS Humans rely on their fast intuitive decision making capabilities in many situations but, when decisions are complex and the stakes are high, a slower, more deliberative process based on decision theory and decision analysis is worth using instead.
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From page 24... ...
Performing sensitivity analysis, seeing how the choices and their values change when these parameters are perturbed, can identify which parts of the model are less robust, where additional information gathering would be most valuable and improve the quality of the decisions. Graphical models, such as Bayesian belief networks and influence diagrams, have been valuable tools for building, communicating, learning, and analyzing models among decision makers, experts, analysts, and machines.2 They also help identify which sources of information might be relevant for particular decisions.
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From page 25... ...
Thus, in a human team, someone with enhanced mathematical or statistical expertise would normally be assigned to tasks requiring this kind of knowledge and skill, but during the course of the activity, if some other aspect requires more aid, these people would shift to help. Similarly, if the mathematical or statistical workload rose too high, less qualified workers would assist, ideally doing lower-level assignments that match their abilities.
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From page 26... ...
A-11. "Flight crew missed multiple cues before San Francisco crash, board says."
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To do so, computer-based participants require algorithms and heuristics that are able to reason about the information's importance and significance at a given time to a given individual, and are able to receive and display information about the basis for its recommended action. In addition, if a computer can teach or assist a human trainee so that the novice can perform at a higher level, the machine will have gained some trust.
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From page 28... ...
Decisions about what authority to delegate to an automated element are weighty. The committee considered the extreme end of the military context -- whether any circumstance would warrant conferral upon a machine the ability to "pull a trigger" with a human "outside the loop." The response to that issue depends heavily upon the degree of human trust in automation that has accumulated through observation of the machine's behavior 12 See, for example, how IBM's Watson applies confidence levels to its answers on the television show Jeopardy on February 16, 2011.
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14 In addition, our understanding of human memory systems is undergoing rapid change. Human memory is a powerful pattern matcher, capable of finding information from prior experiences that are analogous to the current experience.
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Even though we do not know the underlying architecture of human processing and decision making, there is considerable helpful observational evidence about the resulting behavior, to help us see which kinds of situations lend themselves to decisions being made rapidly and efficiently, situations that lead to poor decisions, and the strengths, weaknesses, and biases of the process. A highly over-simplified model that helps put much of the behavioral observations in perspective simply asserts that conscious processes are relatively slow, serial, and limited in the amount of information that can be maintained in an active state, especially in relation to timestressed decision making.
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From page 31... ...
Research reveals, for example, that it is possible to track, measure and model human attention in real-time with relevant stimuli.15 This work is confirmed by human psychophysiological studies,16 and these models can be implemented in systems to help provide the "nudge" that a human observer might need to dislodge from prior expectations. As models are both improved by neuroscience studies and in turn used to improve performance of humans, we expect this area to be widely implemented in visual detection tasks.
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From page 32... ...
The decision theory perspective is a helpful way to think about how to deal with inevitable errors. In that context, the best action is a function of the current situation, the actions available now, the estimated probabilities of possible outcomes of the actions, and the estimated utilities of each outcome.
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At the other extreme, teams could ignore every apparent anomaly and rationally explain each one. Because of their ability to store large bodies of precursor information and sift through it to find patterns, computers might be suited to assisting humans at identifying potentially problematic patterns.
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Humans and computation have different strengths in what they accomplish and there are several aspects of human decision making that can benefit from computer-aided systems, such as cognition, recognition of errors in judgment and task allocation. Similarly, there are several aspects of computer processing that can benefit from human guidance, such as prioritization, dealing with unusual or unexpected situations, understanding social and cultural context, and taking environmental and contextual information into account.
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From page 35... ...
20 With this perspective, one aims to understand the potential for joint collaboration between computational systems and people and to determine the design criteria and strategies needed to ensure that this is a real collaboration, where each contributes their best strengths and where communication among team members, including between people and machines, are always in the appropriate language and interactive form. A key challenge is to make sure design honors the need to address human characteristics, as opposed to today's interaction, which typically is dictated by the needs of the machine.
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