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 |
JEL(s): | |
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.