Frontiers: Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms
Karsten T. Hansen, Kanishka Misra, Mallesh M. Pai
Marketing Science · 2021 · Volume 40 · Issue 1
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, for example, 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 that this occurs via a novel channel: competitors’ algorithms’ prices end up running correlated experiments. Therefore, sellers’ misspecified models overestimate the own price sensitivity, resulting in higher prices. We discuss the implications on competition policy.
Keywords
algorithmic pricing, collusion, behavioral game theory
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