Algorithmic Collusion under Observed Demand Shocks

Zexin Ye

Papers from arXiv.org

Abstract: This paper examines how the observability of demand shocks influences pricing patterns and market outcomes when firms delegate pricing decisions to Q-learning algorithms. Simulations show that demand observability induces Q-learning agents to adapt prices to demand fluctuations, giving rise to distinctive demand-contingent pricing patterns across the discount factor $\delta$, consistent with Rotemberg and Saloner (1986). When $\delta$ is high, they learn procyclical pricing, charging higher prices in higher demand states. In contrast, at low $\delta$, they lower prices during booms and raise them during downturns, exhibiting countercyclical pricing. Q-learning agents also autonomously sustain supracompetitive profits, indicating that demand observability does not hinder algorithmic collusion. I further explore how the information available to algorithms shapes their learned pricing behavior. Overall, the results suggest that, through pure trial and error, Q-learning algorithms internalize both the stronger deviation incentives during booms and the trade-off between short-term gains and long-term continuation values governed by the discount factor, thereby reproducing the cyclicality of pricing patterns predicted by collusion theory.

Date: 2025-02, Revised 2025-12
New Economics Papers: this item is included in nep-ain, nep-com, nep-cta, nep-ind and nep-reg
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