On the numerical approximation of minimax regret rules via fictitious play
Econometrics
2025-03-17 v1
Abstract
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.
Keywords
Cite
@article{arxiv.2503.10932,
title = {On the numerical approximation of minimax regret rules via fictitious play},
author = {Patrik Guggenberger and Jiaqi Huang},
journal= {arXiv preprint arXiv:2503.10932},
year = {2025}
}