ABSTRACT
Naturalistic decision-making (NDM) theory emphasizes the effectiveness and evolution of decision making in real-life settings (e.g., Gigerenzer & Goldstein, 1996; Gigerenzer et al., 1999; Klein, Orasanu, Calderwood, & Zsambok, 1993; Lipshitz, Klein, Orasanu, & Salas, 2000; Zsambok & Klein, 1997). Many NDM researchers argue that human performance in the natu ralistic setting is often quite good even when performance on similar tasks in the laboratory is poor. The difference is often explained as the result of unfamiliar and unrealistic laboratory settings rather than cognitive "errors and biases." The naturalistic argument is at least implicitly dynamic and evolutionary: High-performing decision-making heuristics may arise and persist in a population through both learning and selection. At the same time, the evolutionary argument helps explain why the heuristics people use may not work well outside of the specific context in which they evolved. A heuristic may depend, for its effectiveness, on unique features of the particular setting; if that setting remains sufficiently stable, that heuris tic may propagate and evolve to high efficacy for that environment even if it is ineffective in other settings, such as a laboratory task that has the same underlying logical structure but is otherwise unfamiliar.
