Artificial Intelligence and Algorithmic Price Collusion in Two-sided Markets

TL;DR

This paper investigates how AI algorithms, specifically Q-learning agents, facilitate tacit collusion in two-sided markets, highlighting factors that influence collusion levels and proposing a penalty-based mitigation approach.

Contribution

It demonstrates that AI-driven platforms can achieve higher collusion levels than traditional competition and introduces a penalty mechanism to reduce collusive behavior.

Findings

AI algorithms increase collusion compared to Bertrand competition.

Network externalities amplify collusion levels.

Penalty terms in Q-learning can mitigate collusive behavior.

Abstract

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.