Understanding algorithmic collusion with experience replay

Bingyan Han

Papers from arXiv.org

Abstract: In an infinitely repeated pricing game, pricing algorithms based on artificial intelligence (Q-learning) may consistently learn to charge supra-competitive prices even without communication. Although concerns on algorithmic collusion have arisen, little is known on underlying factors. In this work, we experimentally analyze the dynamics of algorithms with three variants of experience replay. Algorithmic collusion still has roots in human preferences. Randomizing experience yields prices close to the static Bertrand equilibrium and higher prices are easily restored by favoring the latest experience. Moreover, relative performance concerns also stabilize the collusion. Finally, we investigate the scenarios with heterogeneous agents and test robustness on various factors.

Date: 2021-02, Revised 2021-03
New Economics Papers: this item is included in nep-big and nep-gth
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Persistent link: https://EconPapers.repec.org/RePEc:arx:papers:2102.09139

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