On the numerical approximation of minimax regret rules via fictitious play
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866917956867325952 |
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| author | Guggenberger, Patrik Huang, Jiaqi |
| author_facet | Guggenberger, Patrik Huang, Jiaqi |
| contents | Finding numerical approximations to minimax regret treatment rules is of key interest. To do so when potential outcomes are in {0,1} we discretize the action space of nature and apply a variant of Robinson's (1951) algorithm for iterative solutions for finite two-person zero sum games. Our approach avoids the need to evaluate regret of each treatment rule in each iteration. When potential outcomes are in [0,1] we apply the so-called coarsening approach. We consider a policymaker choosing between two treatments after observing data with unequal sample sizes per treatment and the case of testing several innovations against the status quo. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_10932 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | On the numerical approximation of minimax regret rules via fictitious play Guggenberger, Patrik Huang, Jiaqi Econometrics Finding numerical approximations to minimax regret treatment rules is of key interest. To do so when potential outcomes are in {0,1} we discretize the action space of nature and apply a variant of Robinson's (1951) algorithm for iterative solutions for finite two-person zero sum games. Our approach avoids the need to evaluate regret of each treatment rule in each iteration. When potential outcomes are in [0,1] we apply the so-called coarsening approach. We consider a policymaker choosing between two treatments after observing data with unequal sample sizes per treatment and the case of testing several innovations against the status quo. |
| title | On the numerical approximation of minimax regret rules via fictitious play |
| topic | Econometrics |
| url | https://arxiv.org/abs/2503.10932 |