Deng & Hernandez on Algorithmic Pricing in Horizontal Merger Review

Ai Deng (Johns Hopkins University; Charles River Associates) and Cristián Hernández (NERA Economic Consulting) have posted “Algorithmic Pricing in Horizontal Merger Review: An Initial Assessment” on SSRN. Here is the abstract:

While the possibility of algorithmic price discrimination and algorithmic collusion has been extensively discussed in the global antitrust community in recent years, there has been much more limited discussion in the context of mergers. In this article, we aim to fill this gap by discussing some potential implications of algorithmic pricing on market definition, unilateral effects, coordinated effects, and remedies. Specifically, we discuss the following topics and related questions:

– Market definition. How to deal with algorithm-enhanced market/customer segmentation and how to identify relevant antitrust markets when prices are set by a “blackbox” algorithm.

– Unilateral effects. How to use merging parties’ pricing algorithms to conduct merger simulations and why there are important antitrust issues related to integrating merging parties’ pricing algorithms and their data.

– Coordinated effects. What some of the recent scholarship tells us about potentially coordinated effects in a merger context.

– Remedies. Why data compatibility and collusion risk are important considerations when “divesting” merging parties’ pricing algorithm.