Antitrust Law and Coordination Through Al-Based Pricing Technologies

Abstract

Price is the core element of commercial transactions and an important parameter of competition. One of antitrust law’s aims is to ensure that market prices form under the laws of supply and demand, and not after the whims of monopolists or cartelists. Innovations in computer and data science have brought about pricing technologies that rely on advanced analytics or machine learning (ML) techniques, which could strengthen existing bargaining power disparities in part by supporting price coordination among competitors.

Existing research establishes a theoretical framework for competitive harm through coordination, showing that pricing technologies can lead to near-cartel price levels while avoiding anti-cartel prohibitions. This contribution builds on that framework, taking into account up to date empirical, game-theoretic, and computer science literature on pricing technologies to produce a taxonomy of those technologies. We then employ a comparative approach to identify the legal effects of various pricing technologies at a more granular level under EU and US antitrust law. The contribution supports greater understanding between economists and policy- makers regarding the analysis and treatment of AI-based pricing technologies.

Keywords: artificial intelligence, antitrust, competition, coordination