Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms

Mallesh Pai and Karsten Hansen

No 14372, CEPR Discussion Papers from C.E.P.R. Discussion Papers

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.

Keywords: Algorithmic pricing; Collusion; Misspecified models; Bandit algorithms (search for similar items in EconPapers)
Date: 2020-01
New Economics Papers: this item is included in nep-big, nep-cmp and nep-com
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Citations: View citations in EconPapers (15)

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