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Non-Bayesian Learning in Misspecified Models (2503.18024v2)
Published 23 Mar 2025 in econ.TH, math.ST, and stat.TH
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.
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