Computational Complexity and Human Decision-Making

被引:72
|
作者
Bossaerts, Peter [1 ,2 ]
Murawski, Carsten [1 ]
机构
[1] Univ Melbourne, Dept Finance, Brain Mind & Markets Lab, Melbourne, Vic 3010, Australia
[2] Florey Inst Neurosci & Mental Hlth, Melbourne, Vic 3010, Australia
关键词
WORKING-MEMORY CAPACITY; REVEALED PREFERENCE; COGNITIVE CONTROL; RATIONAL CHOICE; SELF-REGULATION; CAB DRIVERS; UTILITY; BEHAVIOR; REWARD; MODEL;
D O I
10.1016/j.tics.2017.09.005
中图分类号
B84 [心理学]; C [社会科学总论]; Q98 [人类学];
学科分类号
03 ; 0303 ; 030303 ; 04 ; 0402 ;
摘要
The rationality principle postulates that decision-makers always choose the best action available to them. It underlies most modern theories of decision-making. The principle does not take into account the difficulty of finding the best option. Here, we propose that computational complexity theory (CCT) provides a framework for defining and quantifying the difficulty of decisions. We review evidence showing that human decision-making is affected by computational complexity. Building on this evidence, we argue that most models of decision-making, and metacognition, are intractable from a computational perspective. To be plausible, future theories of decision-making will need to take into account both the resources required for implementing the computations implied by the theory, and the resource constraints imposed on the decision-maker by biology.
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页码:917 / 929
页数:13
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