Convergence to collusion in algorithmic pricing

TL;DR

This paper demonstrates that deep reinforcement learning algorithms can rapidly converge to collusive pricing strategies in oligopolistic markets, aligning with real-world observations.

Contribution

It provides a model showing how AI algorithms can quickly develop collusive behavior through reward-punishment mechanisms in repeated pricing games.

Findings

Deep RL models converge to collusion in realistic timeframes.

Collusive outcomes are supported by reward-punishment schemes.

Model aligns with empirical observations of collusion timing.

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.