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Convergence to collusion in algorithmic pricing

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  • Kevin Michael Frick

Abstract

Artificial intelligence algorithms are increasingly used by firms to set prices. Previous research shows that they can exhibit collusive behaviour, but how quickly they can do so has so far remained an open question. I show that a modern deep reinforcement learning model deployed to price goods in a repeated oligopolistic competition game with continuous prices converges to a collusive outcome in an amount of time that matches empirical observations, under reasonable assumptions on the length of a time step. This model shows cooperative behaviour supported by reward-punishment schemes that discourage deviations.

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  • Kevin Michael Frick, 2026. "Convergence to collusion in algorithmic pricing," Papers 2604.15825, arXiv.org.
    • Handle: RePEc:arx:papers:2604.15825
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    References listed on IDEAS

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