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WIREs Cogn Sci
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Decision making under risk and uncertainty

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Abstract 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

Cumulative prospect theory's value and weighting functions.

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Generic neural network representation of a decision problem. Each of three choice options is described by three attributes. An attention node determines which attribute(s) is/are processed at each time step and thus embodies decision weight. Links between attributes and options represent the value of each option on the corresponding attribute. All solid connections are assumed to be bidirectional for parallel constraint satisfaction (PCS) models, and feedforward for leaky competing accumulator (LCA) and decision field theory (DFT). Inhibitory bidirectional connections among options (dashed lines) model competition among options in LCA, PCS, and DFT models. LCA and DFT assume an additional layer between options and attributes to compute differences.

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Decision field theory (DFT) representation of preference accumulation for two options. Preference P(t) accumulates for each option, shown as separate trajectories, over time t. At time , option B is preferred with a higher value of P(t); at time , preference is equal between the two options, after which option A is consistently preferred. Choice is determined when an option's trajectory reaches the threshold level of preference, . A decision maker with a threshold of θ 2 would thus select option A at time ; a more impulsive individual modeled by θ 1 would select option B at time .

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Economics > Individual Decision-Making
Psychology > Reasoning and Decision Making

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