How Market Volatility Shapes Algorithmic Collusion: A Comparative Analysis of Learning-Based Pricing Algorithms

The rapid diffusion of autonomous pricing algorithms has reshaped competitive dynamics in digital marketplaces, raising important economic and policy questions about their potential for collusive behavior. A substantial body of research demonstrates that reinforcement-learning (RL) agents can autonomously coordinate on supracompetitive outcomes even in the absence of explicit communication. Foundational contributions--including the work in [1]--show that algorithmic agents may systematically learn tacitly collusive strategies across multiple market structures, with Q-learning in particular generating prices above competitive levels in Logit, Hotelling, and linear demand environments. These concerns are reinforced by seminal work such as [2], which demonstrates that simple Q-learning agents reliably sustain collusion through structured punishment and reward cycles in repeated pricing games, as well as by [3], who document how algorithmic systems may generate sudden price spikes in response to high-impact, low-probability events (HILP), unintentionally coordinating on elevated prices. The study of [4] establishes a robust empirical and computational foundation demonstrating that pricing algorithms may autonomously learn to collude. A complementary line of research focuses specifically on Q-learning's capacity to learn collusive equilibria, as documented in papers [2], [5], and [6]. These findings are consistent with the theoretical properties of Q-learning established by [7], who show that the algorithm incrementally learns long-run discounted value-maximizing strategies in sequential decision problems. More recent studies further reveal that deep reinforcement-learning (deep RL) algorithms--including DDQN and SAC--may also display collusive tendencies. For instance, [8] documents that modern RL systems can coordinate on higher-than-competitive prices under a variety of market configurations.