Homogeneous Algorithms Can Reduce Competition in Personalized Pricing

Nathanael Jo, Ashia C. Wilson, Kathleen Creel, Manish Raghavan

Algorithmic Pricing Personalized Pricing Collusion Competition Outcome Homogenization Algorithmic Monoculture Antitrust Tacit Collusion

⋅ NeurIPS ⋅ Poster ⋅OpenReview

Abstract

Firms' algorithm development practices are often homogeneous Whether firms train algorithms on similar data or rely on similar pre-trained models, the result is correlated predictions.

In the context of personalized pricing, correlated algorithms can be viewed as a means to collude among competing firms, but whether or not this conduct is legal depends on the mechanisms of achieving collusion.

We investigate the precise mechanisms through a formal game-theoretic model. Indeed, we find that

higher correlation diminishes consumer welfare
as consumers become more price sensitive, firms are increasingly incentivized to compromise on the accuracy of their predictions in exchange for coordination.

We demonstrate our theoretical results in a stylized empirical study where two firms compete using personalized pricing algorithms. Our results demonstrate a new mechanism for achieving collusion through correlation, which allows us to analyze its legal implications. Correlation through algorithms is a new frontier of anti-competitive behavior that is largely unconsidered by US antitrust law.