Rationalizable Irrationalities of Choice

被引:17
作者
Dayan, Peter [1 ]
机构
[1] UCL, Gatsby Computat Neurosci Unit, London WC1N 3AR, England
关键词
Reinforcement learning; Bounded rationality; Model-based; Model-free; Pavlovian; Pruning; Noisy decision-making; NUCLEUS-ACCUMBENS SHELL; WORKING-MEMORY; PROBABILISTIC INFERENCE; PREDICTION ERRORS; DECISION-MAKING; REINFORCEMENT; REWARD; MODEL; DOPAMINE; STRIATUM;
D O I
10.1111/tops.12082
中图分类号
B84 [心理学];
学科分类号
04 ; 0402 ;
摘要
Although seemingly irrational choice abounds, the rules governing these mis-steps that might provide hints about the factors limiting normative behavior are unclear. We consider three experimental tasks, which probe different aspects of non-normative choice under uncertainty. We argue for systematic statistical, algorithmic, and implementational sources of irrationality, including incomplete evaluation of long-run future utilities, Pavlovian actions, and habits, together with computational and statistical noise and uncertainty. We suggest structural and functional adaptations that minimize their maladaptive effects.
引用
收藏
页码:204 / 228
页数:25
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