Algorithmic Pricing – A Black Box for Antitrust Analysis

The conversation around and study of the use of algorithms in pricing and other competitively sensitive decisions remains vibrant and is increasingly well-informed. Early theoretical work paved the way for government studies and more recently – and most interestingly – experimental and real-world empirical studies. At the same time, technology continues to advance, and with it the varieties and sophistication of software deployed. The law does not seem to have kept pace. Examples of enforcement to date are against pure cartel agreements that happen to have pricing algorithms as a tool for implementation. The most likely harms from deployment of pricing algorithms, increased capacity for optimal tacitly collusive outcomes, is unlikely to violate the law in any developed antitrust system. More speculative harms, including actual algorithmic collusion, seem to be equally outside of the realm of antitrust. And all of these considerations arise against a backdrop of efficiency considerations that while apparent seem to be under-theorized and under-studied. We outline findings on algorithmic pricing in theoretical and empirical research, how they interact with existing legal rules, and suggest promising areas for future study and policy development.

By Max Huffman & Dr. Maria José Schmidt-Kessen^[1]

I. INTRODUCTION

Beginning with an important paper by Salil Mehra,^[2] the last six years has seen animated conversation and a growing body of literature by academic