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计算复杂性与人类决策

Computational Complexity and Human Decision-Making.

机构信息

Brain, Mind & Markets Laboratory, Department of Finance, The University of Melbourne, Melbourne, VIC 3010, Australia; Florey Institute of Neuroscience and Mental Health, Melbourne, VIC 3010, Australia.

Brain, Mind & Markets Laboratory, Department of Finance, The University of Melbourne, Melbourne, VIC 3010, Australia.

出版信息

Trends Cogn Sci. 2017 Dec;21(12):917-929. doi: 10.1016/j.tics.2017.09.005.

Abstract

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.

摘要

合理性原则假定决策者总是选择最适合他们的行动。它是大多数现代决策理论的基础。该原则没有考虑到找到最佳选择的难度。在这里,我们提出计算复杂性理论 (CCT) 为定义和量化决策的难度提供了一个框架。我们回顾了表明人类决策受到计算复杂性影响的证据。在此基础上,我们认为,大多数决策模型和元认知在计算上都是难以处理的。为了具有合理性,未来的决策理论将需要考虑到实施理论所隐含的计算所需的资源,以及生物学对决策者施加的资源限制。

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