Apparently irrational choice as optimal sequential decision making

Haiyang Chen, Hyung Jin Chang, Andrew Howes

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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Abstract

In this paper, we propose a normative approach to modeling apparent human irrational decision making (cognitive biases) that makes use of inherently rational computational mechanisms. We view preferential choice tasks as sequential decision making problems and formulate them as Partially Observable Markov Decision Processes (POMDPs). The resulting sequential decision model learns what information to gather about which options, whether to calculate option values or make comparisons between options and when to make a choice. We apply the model to choice problems where con- text is known to influence human choice, an effect that has been taken as evidence that human cognition is irrational. Our results show that the new model approximates a bounded optimal cognitive policy and makes quantitative predictions that correspond well to evidence about human choice. Furthermore, the model learns to use context to help infer which option has a maximum expected value while taking into ac- count computational cost and cognitive limits. In addition, it predicts when, and explains why, people stop evidence accumulation and make a decision. We argue that the model pro- vides evidence that apparent human irrationalities are emergent consequences of learning processes that prefer higher value (rational) policies.
Original languageEnglish
Title of host publicationAAAI'21 Proceedings of the Thirty-fifth AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Pages792-800
Number of pages9
ISBN (Print)9781577358664
Publication statusPublished - 18 May 2021
Event35th AAAI Conference on Artificial Intelligence - Vancouver, Canada
Duration: 2 Feb 20219 Feb 2021
Conference number: 35
https://aaai.org/Conferences/AAAI-21/

Publication series

NameProceedings of the AAAI Conference on Artificial Intelligence
PublisherAAAI Press
Number1
Volume35
ISSN (Print)2159-5399
ISSN (Electronic)2374-3468

Conference

Conference35th AAAI Conference on Artificial Intelligence
Abbreviated titleAAAI-21
Country/TerritoryCanada
CityVancouver
Period2/02/219/02/21
Internet address

Keywords

  • Simulating Humans

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