Non-Bayesian Learning in Misspecified Models

Sebastian Bervoets (), Mathieu Faure () and Ludovic Renou
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Sebastian Bervoets: AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique
Mathieu Faure: AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique
Ludovic Renou: QMUL - Queen Mary University of London

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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.

Keywords: learning; Bayesian; consistency (search for similar items in EconPapers)
Date: 2025-09-30
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