2025/02/20 by Ye, Zexin · 1 citation
#FOS: Economics and business #General Economics (econ.GN)
paper · doi:10.48550/arxiv.2502.15084
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 δ, consistent with Rotemberg and Saloner (1986). When δ is high, they learn procyclical pricing, charging higher prices in higher demand states. In contrast, at low δ, 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.