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When Does Reward Maximization Lead to Matching Law?

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  • Yutaka Sakai
  • Tomoki Fukai

Abstract

What kind of strategies subjects follow in various behavioral circumstances has been a central issue in decision making. In particular, which behavioral strategy, maximizing or matching, is more fundamental to animal's decision behavior has been a matter of debate. Here, we prove that any algorithm to achieve the stationary condition for maximizing the average reward should lead to matching when it ignores the dependence of the expected outcome on subject's past choices. We may term this strategy of partial reward maximization “matching strategy”. Then, this strategy is applied to the case where the subject's decision system updates the information for making a decision. Such information includes subject's past actions or sensory stimuli, and the internal storage of this information is often called “state variables”. We demonstrate that the matching strategy provides an easy way to maximize reward when combined with the exploration of the state variables that correctly represent the crucial information for reward maximization. Our results reveal for the first time how a strategy to achieve matching behavior is beneficial to reward maximization, achieving a novel insight into the relationship between maximizing and matching.

Suggested Citation

  • Yutaka Sakai & Tomoki Fukai, 2008. "When Does Reward Maximization Lead to Matching Law?," PLOS ONE, Public Library of Science, vol. 3(11), pages 1-7, November.
  • Handle: RePEc:plo:pone00:0003795
    DOI: 10.1371/journal.pone.0003795
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    Cited by:

    1. Zhenbo Cheng & Jingying Gao & Leilei Zhang & Gang Xiao & Hongjing Mao, 2018. "Strategies using recent feedback lead to matching or maximising behaviours," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 13(2), pages 212-216, March.
    2. Hidetoshi Yamaji & Masatoshi Gotoh & Yoshinori Yamakawa, 2016. "Additional Information Increases Uncertainty in the Securities Market: Using both Laboratory and fMRI Experiments," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 425-451, October.
    3. Daniel E Acuña & Paul Schrater, 2010. "Structure Learning in Human Sequential Decision-Making," PLOS Computational Biology, Public Library of Science, vol. 6(12), pages 1-12, December.
    4. repec:cup:judgdm:v:13:y:2018:i:2:p:212-216 is not listed on IDEAS

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