Working Paper: CEPR ID: DP14372
Authors: Karsten Hansen; Kanishka Misra; Mallesh Pai
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
JEL Codes: K21; L41
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| signal-to-noise ratio (SNR) (Y40) | long-run prices indistinguishable from Nash equilibrium prices (D41) |
| high signal-to-noise ratio (SNR) (C58) | supracompetitive long-run prices (D41) |
| misspecified models (C50) | correlated pricing behavior among firms (L11) |
| competitors' correlated experiments (C90) | overestimation of own price sensitivity (D40) |
| overestimation of own price sensitivity (D40) | higher prices (D49) |
| independent algorithms (C69) | correlated pricing (G13) |
| misspecification of models (C50) | supracompetitive pricing outcomes (L11) |