On the numerical approximation of minimax regret rules via fictitious play

Patrik Guggenberger; Jiaqi Huang

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arXiv · 2503.10932

On the numerical approximation of minimax regret rules via fictitious play

Patrik Guggenberger·Jiaqi Huang

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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.

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Patrik Guggenberger, Jiaqi Huang. 2025-03-13. On the numerical approximation of minimax regret rules via fictitious play. https://arxiv.org/abs/2503.10932

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