Robust Algorithmic Collusion

Nicolas Eschenbaum, Filip Mellgren and Philipp Zahn

Papers from arXiv.org

Abstract: This paper develops a formal framework to assess policies of learning algorithms in economic games. We investigate whether reinforcement-learning agents with collusive pricing policies can successfully extrapolate collusive behavior from training to the market. We find that in testing environments collusion consistently breaks down. Instead, we observe static Nash play. We then show that restricting algorithms' strategy space can make algorithmic collusion robust, because it limits overfitting to rival strategies. Our findings suggest that policy-makers should focus on firm behavior aimed at coordinating algorithm design in order to make collusive policies robust.

Date: 2022-01, Revised 2022-01
New Economics Papers: this item is included in nep-big, nep-cmp, nep-com, nep-gth, nep-ind and nep-reg
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