Gigerenzer,G.
Todd,P.M.

(create) In the literature that emphasizes human rationality, the laws of statistics (such as Bayes's rule) are proposed as the normative means toward given ends. These normative computations are sometimes said to describe the "computational level" of cognition (a term borrowed from Marr, 1982), while "at the algorithmic level, the relevant cognitive processes operate via a set of heuristic tricks...rather than with explicit probabilistic calculations" (Chater & Oaksford, 2008, p. 8). In this rational view of the mind, a heuristic is a quick-and-dirty cognitive shortcut or approximation to an optimization process that is too difficult for the mind to execute. In this chapter, we will argue for the possibility of a normative study of heuristics. The normative part comes from exploring the ecological rationality of heuristics; it complements the descriptive part, the study of the adaptive toolbox. The term "ecological" signals that the yardstick for rationality is some measure of success in the external world, instead of some measure of internal consistency, as in most traditional theories of rationality. To make our point, we introduce in the next section two distinctions: process models versus as-if models; and problems for which optimization is feasible or not. In short, our argument is that the study of heuristics can be normative through answering questions of what particular heuristic process one should use to succeed in a given environment, and through considering the ecological rationality of heuristics in situations where optimization is not feasible. (PsycINFO Database Record (c) 2012 APA, all rights reserved)

Gigerenzer, G., & Todd, P. M. (2012). Ecological rationality: The normative study of heuristics. In P. M. Todd, G. Gigerenzer & the ABC Research Group, Ecological rationality: Intelligence in the world (pp. 487-497). New York: Oxford University Press.

Publication year	2012
Document type:	In book
Publication status:	Published
External URL:
Categories:
Keywords:	*computational modeling*ecological psychology*heuristics*rationalitydecision makingheuristic modelingstatistics