Journal of Cognitive Engineering and Decision Making

[This is an edited version of the original, unpublished 1985 study that identified recognition-primed decision making, with a new commentary added.] The objective of this study was to examine the way in which decisions are made by highly proficient personnel, under conditions of extreme time pressure, and in environments where the consequences of the decisions could affect lives and property. The domain of fire-fighting was selected, and the research focused on the decisions made by fire ground commanders (FGCs). Interviews were conducted with 26 experienced FGCs (mean experience of 23 years). Each interview covered a critical incident that was nonroutine and that demanded expertise. A total of 156 decision points were probed in this way. In less than 12% of them was there any evidence of simultaneous comparisons and relative evaluation of two or more options. In over 80% of the decision points, the strategy was for the FGCs to use their experience to directly identify the situation as typical of a standard prototype and to identify a course of action as typical for that prototype. In this way, the FGCs handled decision points without any need to consider more than one option. A recognition-primed decision (RPD) model was synthesized from these data, which emphasized the use of recognition rather than calculation or analysis for rapid decision making.

Naturalistic decision-making studies of intelligence analysis have generally focused on information search, collection, and synthesis processes, deemphasizing the initial “problem formulation” phase, in which analysts interpret and contextualize the information request to determine which information to collect. We present the results of two studies focusing on this phase. In the first study, we performed a cognitive task analysis via semistructured interviews with 22 active-duty U.S. Army intelligence analysts to uncover factors that arise in operational environments that complicate problem formulation. The factors discovered (e.g., vague and/or overly narrow intelligence requests) led to a second study probing 6 active-duty U.S. Army intelligence analysts' cognitive strategies with a “think-aloud” protocol as they interpreted and evaluated representative information requests. The study revealed that analysts actively interpret and contextualize an information request. The analysts reframed and broadened the request so that they could respond meaningfully to the underlying intent, then used contextual cues and metainformation to determine the most useful collectors and how effectively the request could be answered in the time allotted. We discuss these results and their implications for both the cognitive modeling of intelligence analysis and the development of training and decision aids for more effective framing and contextualization of information requests.

The field of naturalistic decision making (NDM) assumes a “cold” cognitive model in that nonemotional, valence-neutral cues and information are predicted to influence decision making in identifiable ways. Judgment and decision-making research over the past 10 to 15 years, however, has greatly enhanced knowledge of the ways in which affect that is present at the time of decision making influences how people make decisions—specifically, how they process information, how they respond to risk, and which outcomes they prefer. The purpose of this article is to review relevant aspects of the literature on affect and decision making and to present the argument that NDM researchers need to be cognizant of the potential impact of affect on decision processes to adequately describe and predict expert decision making.

Over the last 20 years, both naturalistic decision making and fast and frugal heuristics programs have radically broken with mainstream decision science, moving beyond the confines of artificial tasks and safe academic laboratories. We document commonalities of these programs and discuss ways in which a synthesis could contribute to a more relevant, precise, predictive, and effective decision science. We begin by reviewing the common roots and philosophies of the two programs, such as their respect for the capable decision maker and their acknowledgment of the importance of task ecology. We then identify four specific areas of synergetic potential, including ecological rationality and metacognition. Our review culminates in a case study of naturalistic heuristics based on a particular class of fast and frugal heuristics. These fast and frugal trees provide examples of effective, well-specified decision-making algorithms applied in a naturalistic domain: emergency medical diagnosis. By leveraging the strengths of each program, we point out some of the ways in which more sustainable progress can be fostered on issues that matter the most—for example, decisions that save and transform lives.