The plausibility transition model for sensemaking.

When people make plausibility judgments about an assertion, an event, or a piece of evidence, they are gauging whether it makes sense that the event could transpire as it did. Therefore, we can treat plausibility judgments as a part of sensemaking. In this paper, we review the research literature, presenting the different ways that plausibility has been defined and measured. Then we describe the naturalistic research that allowed us to model how plausibility judgments are engaged during the sensemaking process. The model is based on an analysis of 23 cases in which people tried to make sense of complex situations. The model describes the user's attempts to construct a narrative as a state transition string, relying on plausibility judgments for each transition point. The model has implications for measurement and for training.

Measuring Resilience.

OBJECTIVE: As human factors and ergonomics (HF/E) moves to embrace a greater systems perspective concerning human-machine technologies, new and emergent properties, such as resilience, have arisen. Our objective here is to promote discussion as to how to measure this latter, complex phenomenon. BACKGROUND: Resilience is now a much-referenced goal for technology and work system design. It subsumes the new movement of resilience engineering. As part of a broader systems approach to HF/E, this concept requires both a definitive specification and an associated measurement methodology. Such an effort epitomizes our present work. METHOD: Using rational analytic and synthetic methods, we offer an approach to the measurement of resilience capacity. RESULTS: We explicate how our proposed approach can be employed to compare resilience across multiple systems and domains, and emphasize avenues for its future development and validation. CONCLUSION: Emerging concerns for the promise and potential of resilience and associated concepts, such as adaptability, are highlighted. Arguments skeptical of these emerging dimensions must be met with quantitative answers; we advance one approach here. APPLICATION: Robust and validated measures of resilience will enable coherent and rational discussions of complex emergent properties in macrocognitive system science.

Influencing preferences for different types of causal explanation of complex events.

OBJECTIVE: We examined preferences for different forms of causal explanations for indeterminate situations. Background: Klein and Hoffman distinguished several forms of causal explanations for indeterminate, complex situations: single-cause explanations, lists of causes, and explanations that interrelate several causes. What governs our preferences for single-cause (simple) versus multiple- cause (complex) explanations? METHOD: In three experiments, we examined the effect of target audience, explanatory context, participant nationality, and explanation type. All participants were college students. Participants were given two scenarios, one regarding the U.S. economic collapse in 2007 to 2008 and the other about the sudden success of the U.S. military in Iraq in 2007. The participants were asked to assess various types of causal explanations for each of the scenarios, with reference to one or more purposes or audience for the explanations. RESULTS: Participants preferred simple explanations for presentation to less sophisticated audiences. Malaysian students of Chinese ethnicity preferred complex explanations more than did American students. The form of presentation made a difference: Participants preferred complex to simple explanations when given a chance to compare the two, but the preference for simple explanations increased when there was no chance for compari- son, and the difference between Americans and Malaysians disappeared. CONCLUSIONS: Preferences for explanation forms can vary with the context and with the audience, and they depend on the nature of the alternatives that are provided. APPLICATION: Guidance for decision-aiding technology and training systems that provide explanations need to involve consideration of the form and depth of the accounts provided as well as the intended audience.

Human factors contributions to knowledge elicitation.

OBJECTIVE: The objective of this article is to lay out contributions of human factors to knowledge elicitation (KE) methodology. BACKGROUND: The background is historical, dating to about 1985, and involves the convergence of expert systems with applied psychology and cognitive psychology. METHOD: The method is a literature review, focusing on past issues of Human Factors. RESULTS: Human factors researchers have contributed significantly to KE methodology. However, KE methodology "belongs to" a number of communities of practice and has applications that transcend individual disciplines. CONCLUSION: Knowledge elicitation, thought of as a kind of cognitive task analysis, grows in importance with the increasing use of information technology to form complex sociotechnical work systems and the increasing importance of expertise to knowledge-based organizations. APPLICATION: I discuss some open issues for further research and methodological investigation.