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Non-Bayesian Learning in Misspecied Models

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Abstract

Deviations from Bayesian updating are traditionally categorized as biases, errors, or fallacies, thus implying their inherent “sub-optimality.” We offer a more nuanced view. In learning problems with misspecified models, we show that some non-Bayesian updating can outperform Bayesian updating.

Suggested Citation

  • Sebastian Bervoets & Mathieu Faure & Ludovic Renou, 2025. "Non-Bayesian Learning in Misspecied Models," AMSE Working Papers 2513, Aix-Marseille School of Economics, France.
    • Handle: RePEc:aim:wpaimx:2513
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    References listed on IDEAS

    as
    1. Fudenberg, Drew & Romanyuk, Gleb & Strack, Philipp, 2017. "Active learning with a misspecified prior," Theoretical Economics, Econometric Society, vol. 12(3), September.
    2. 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.
    3. Esponda, Ignacio & Pouzo, Demian & Yamamoto, Yuichi, 2021. "Asymptotic behavior of Bayesian learners with misspecified models," Journal of Economic Theory, Elsevier, vol. 195(C).
    4. J. Aislinn Bohren & Daniel N. Hauser, 2021. "Learning With Heterogeneous Misspecified Models: Characterization and Robustness," Econometrica, Econometric Society, vol. 89(6), pages 3025-3077, November.
    5. Bohren, Aislinn & Hauser, Daniel, 2017. "Learning with Heterogeneous Misspecified Models: Characterization and Robustness," CEPR Discussion Papers 12036, C.E.P.R. Discussion Papers.
    Full references (including those not matched with items on IDEAS)

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    JEL classification:

    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design

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