Non-Bayesian Learning in Misspecified Models
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , |
|---|---|
| Format: | Preprint |
| Publié: |
2025
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916986227785728 |
|---|---|
| author | Bervoets, Sebastian Faure, Mathieu Renou, Ludovic |
| author_facet | Bervoets, Sebastian Faure, Mathieu Renou, Ludovic |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_18024 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Non-Bayesian Learning in Misspecified Models Bervoets, Sebastian Faure, Mathieu Renou, Ludovic Theoretical Economics Statistics Theory 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. |
| title | Non-Bayesian Learning in Misspecified Models |
| topic | Theoretical Economics Statistics Theory |
| url | https://arxiv.org/abs/2503.18024 |