Non-Bayesian Learning in Misspecified Models

Sebastian Bervoets, Mathieu Faure and Ludovic Renou

Papers from arXiv.org

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.

Date: 2025-03, Revised 2025-04
New Economics Papers: this item is included in nep-dcm and nep-mic
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