Antitrust & Competition Policy Blog

Editor: D. Daniel Sokol
University of Florida
Levin College of Law

Tuesday, April 7, 2020

Algorithmic Collusion: Supra-Competitive Prices via Independent Algorithms

Karsten Hansen, University of California, San Diego (UCSD), Kanishka Misra, University of California, San Diego (UCSD), Mallesh Pai, Rice University analyze Algorithmic Collusion: Supra-Competitive Prices via Independent Algorithms.

ABSTRACT: Motivated by their increasing prevalence, we study outcomes when competing sellers use machine learning algorithms to run real-time dynamic price experiments. These algorithms are often misspecified, ignoring the effect of factors outside their control, e.g. competitors' prices. We show that the long-run prices depend on the informational value (or signal to noise ratio) of price experiments: if low, the long-run prices are consistent with the static Nash equilibrium of the corresponding full information setting. However, if high, the long-run prices are supra-competitive---the full information joint-monopoly outcome is possible. We show this occurs via a novel channel: competitors' algorithms' prices end up running correlated experiments. Therefore, sellers' misspecified models overestimate own price sensitivity, resulting in higher prices. We discuss the implications on competition policy.

 

https://lawprofessors.typepad.com/antitrustprof_blog/2020/04/algorithmic-collusion-supra-competitive-prices-via-independent-algorithms.html

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