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Non-Bayesian Learning in Misspecified Models

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  • Sebastian Bervoets
  • Mathieu Faure
  • Ludovic Renou

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. We demonstrate that, in learning problems with misspecified models, non-Bayesian updating can outperform Bayesian updating.

Suggested Citation

  • Sebastian Bervoets & Mathieu Faure & Ludovic Renou, 2025. "Non-Bayesian Learning in Misspecified Models," Papers 2503.18024, arXiv.org, revised Apr 2025.
    • Handle: RePEc:arx:papers:2503.18024
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    References listed on IDEAS

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    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).
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