Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets
This addresses algorithmic price collusion concerns for regulators and market participants, but it is incremental as it builds on existing Q-learning methods.
The study investigated how AI agents using Q-learning engage in tacit collusion in two-sided markets, finding that they achieve higher collusion levels than Bertrand competition, with increased network externalities enhancing collusion and user heterogeneity reducing it.
Algorithmic price collusion facilitated by artificial intelligence (AI) algorithms raises significant concerns. We examine how AI agents using Q-learning engage in tacit collusion in two-sided markets. Our experiments reveal that AI-driven platforms achieve higher collusion levels compared to Bertrand competition. Increased network externalities significantly enhance collusion, suggesting AI algorithms exploit them to maximize profits. Higher user heterogeneity or greater utility from outside options generally reduce collusion, while higher discount rates increase it. Tacit collusion remains feasible even at low discount rates. To mitigate collusive behavior and inform potential regulatory measures, we propose incorporating a penalty term in the Q-learning algorithm.