Homogeneous Algorithms Can Reduce Competition in Personalized Pricing

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

Keywords: Algorithmic Pricing, Personalized Pricing, Collusion, Competition, Outcome Homogenization, Algorithmic Monoculture, Antitrust, Tacit Collusion

TL;DR: Correlated algorithms can reduce competition and result in higher prices, posing antitrust implications

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 (1) higher correlation diminishes consumer welfare and (2) 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.

Primary Area: Social and economic aspects of machine learning (e.g., fairness, interpretability, human-AI interaction, privacy, safety, strategic behavior)

Submission Number: 17041