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Evolutionary justifications for non-Bayesian beliefs

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  • Zhang, Hanzhe

Abstract

This paper suggests that the evolutionarily optimal belief of an agent’s intrinsic reproductive ability is systematically different from the posterior belief obtained by the perfect Bayesian updating. In particular, the optimal belief depends on how risk-averse the agent is. Although the perfect Bayesian updating remains evolutionarily optimal for a risk-neutral agent, it is not for any other. Specifically, the belief is always positively biased for a risk-averse agent, and the more risk-averse an agent is, the more positively biased the optimally updated belief is. Such biased beliefs align with experimental findings and also offer an alternative explanation to the empirical puzzle that people across the population appear overconfident by consistently overestimating their personal hereditary traits.

Suggested Citation

  • Zhang, Hanzhe, 2013. "Evolutionary justifications for non-Bayesian beliefs," Economics Letters, Elsevier, vol. 121(2), pages 198-201.
    • Handle: RePEc:eee:ecolet:v:121:y:2013:i:2:p:198-201
    DOI: 10.1016/j.econlet.2013.08.016
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    References listed on IDEAS

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    1. Alvaro Sandroni, 2000. "Do Markets Favor Agents Able to Make Accurate Predicitions?," Econometrica, Econometric Society, vol. 68(6), pages 1303-1342, November.
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    3. Larry G. Epstein, 2006. "An Axiomatic Model of Non-Bayesian Updating," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 73(2), pages 413-436.
    4. David Eil & Justin M. Rao, 2011. "The Good News-Bad News Effect: Asymmetric Processing of Objective Information about Yourself," American Economic Journal: Microeconomics, American Economic Association, vol. 3(2), pages 114-138, May.
    5. , G. & , & ,, 2008. "Non-Bayesian updating: A theoretical framework," Theoretical Economics, Econometric Society, vol. 3(2), June.
    6. Ali Jadbabaie & Alvaro Sandroni & Alireza Tahbaz-Salehi, 2010. "Non-Bayesian Social Learning, Second Version," PIER Working Paper Archive 10-005, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 01 Feb 2010.
    7. Botond Köszegi, 2006. "Ego Utility, Overconfidence, and Task Choice," Journal of the European Economic Association, MIT Press, vol. 4(4), pages 673-707, June.
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    Cited by:

    1. Herold, Florian & Netzer, Nick, 2023. "Second-best probability weighting," Games and Economic Behavior, Elsevier, vol. 138(C), pages 112-125.
    2. Takanori Ida & Ryo Okui, 2019. "Can information alleviate overconfidence? A randomized experiment on financial market predictions," Working Paper Series no126, Institute of Economic Research, Seoul National University.
    3. Mitsunobu MIYAKE, 2024. "On the multiplicative law of subjective probability," TERG Discussion Papers 481, Graduate School of Economics and Management, Tohoku University.
    4. Wu, Jiabin & Zhang, Hanzhe, 2021. "Preference evolution in different matching markets," European Economic Review, Elsevier, vol. 137(C).

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    More about this item

    Keywords

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    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D83 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Search; Learning; Information and Knowledge; Communication; Belief; Unawareness

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