Algorithmic Collusion under Observed Demand Shocks

Abstract

The growing reliance on AI-driven pricing algorithms has raised antitrust concerns, particularly that third-party use of nonpublic competitor data may facilitate information sharing and undermine market competition. This paper examines a setting in which firms receive demand information from a common third party and incorporate it into their pricing algorithms. Specifically, I analyze the pricing behavior of Q-learning agents when demand shocks are observable. The simulation results show that at high discount factors, agents consistently charge constant supracompetitive prices across demand states, resulting in price rigidity. This pricing pattern is sustained through collusive strategies that effectively punish deviations and feature a discontinuous switch from punishment back to cooperation. At medium discount factors, however, agents lower prices during booms to reduce incentives to deviate and raise them during downturns, resulting in countercyclical pricing consistent with Rotemberg and Saloner (1986). The findings highlight both the pricing adaptability of Q-learning to fluctuating market demand and the robustness of algorithmic collusion. Compared to settings with unobserved demand shocks, demand observability reduces firm profits and enhances consumer surplus, suggesting that access to market information under algorithmic pricing does not necessarily harm consumers.

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Reproductions