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Decision making under risk and uncertainty
Author(s) -
Johnson Joseph G.,
Busemeyer Jerome R.
Publication year - 2010
Publication title -
wiley interdisciplinary reviews: cognitive science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.526
H-Index - 49
eISSN - 1939-5086
pISSN - 1939-5078
DOI - 10.1002/wcs.76
Subject(s) - normative , axiom , computer science , set (abstract data type) , cognition , representation (politics) , management science , decision theory , preference , focus (optics) , computational model , cognitive science , artificial intelligence , psychology , epistemology , mathematics , philosophy , statistics , physics , geometry , optics , neuroscience , politics , political science , law , economics , programming language
Decision making is studied from a number of different theoretical approaches. Normative theories focus on how to make the best decisions by deriving algebraic representations of preference from idealized behavioral axioms. Descriptive theories adopt this algebraic representation, but incorporate known limitations of human behavior. Computational approaches start from a different set of assumptions altogether, focusing instead on the underlying cognitive and emotional processes that result in the selection of one option over the other. This review comprehensively but concisely describes and contrasts three approaches in terms of their theoretical assumptions and their ability to account for behavioral and neurophysiological evidence from experimental research. Although each approach contributes substantially to our understanding of human decision making, we argue that the computational approach is more fruitful and parsimonious for describing and predicting choices in both laboratory and applied settings and for understanding the neurophysiological substrates of decision making. Copyright © 2010 John Wiley & Sons, Ltd. This article is categorized under: Economics > Individual Decision-Making Psychology > Reasoning and Decision Making