Non-Bayesian Learning in Misspecified Models
Theoretical Economics
2025-10-06 v3 Statistics Theory
Statistics Theory
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.
Cite
@article{arxiv.2503.18024,
title = {Non-Bayesian Learning in Misspecified Models},
author = {Sebastian Bervoets and Mathieu Faure and Ludovic Renou},
journal= {arXiv preprint arXiv:2503.18024},
year = {2025}
}