DP14372 Algorithmic Collusion: Supra-competitive Prices via Independent Algorithms
|Author(s):||Karsten Hansen, Kanishka Misra, Mallesh Pai|
|Publication Date:||January 2020|
|Keyword(s):||algorithmic pricing, bandit algorithms, Collusion, Misspecified models|
|Programme Areas:||Industrial Organization|
|Link to this Page:||cepr.org/active/publications/discussion_papers/dp.php?dpno=14372|
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.