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Naturalistic Decision Making: A Domain-Integrated Overview

Naturalistic decision making has emerged as a prominent paradigm for the investigation of decision making in many of the traditional human factors research domains, such as aviation, medicine, nuclear power, and command and control. Models that have arisen from Naturalistic Decision-Making (NDM) theory, such as Recognition-Primed Decision making (RPD; Klein, 1989), Image Theory (Beach & Mitchell, 1987), or Situation Assessment Theory (Noble, 1989), highlight the non-analytical nature of expert decision making in naturalistic environments. Characteristics of these environments include dynamic situations, expert decision makers, time pressure, situational ambiguity, inadequacy of if ... then rules to guide behavior, and often a high degree of risk associated with faulty decisions. Additionally, the focus in these models is on the up-front aspect of the decision event, i.e., on identification and definition of the problem rather than on the generation and evaluation of alternatives for solution. The processes these models describe are serial and highly iterative, and focus heavily on situation awareness and situation assessment, recognition of problems, and the emergence of solutions from experienced-based evaluations of the situation.