Non-Bayesian Learning in Misspecied Models

Sebastian Bervoets (), Mathieu Faure () and Ludovic Renou ()
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Sebastian Bervoets: Aix-Marseille Univ., CNRS, AMSE, Marseille, France, https://www.amse-aixmarseille.fr/en/members/bervoets
Mathieu Faure: Aix-Marseille Univ., CNRS, AMSE, Marseille, France, https://www.amse-aixmarseille.fr/en/members/faure
Ludovic Renou: ASU, QMUL and CEPR

No 2513, AMSE Working Papers from Aix-Marseille School of Economics, France

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.

Keywords: learning; Bayesian; consistency (search for similar items in EconPapers)
JEL-codes: C73 D82 (search for similar items in EconPapers)
Pages: 40 pages
Date: 2025-09
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Persistent link: https://EconPapers.repec.org/RePEc:aim:wpaimx:2513

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