Reinforcement Learning, Collusion, and the Folk Theorem

Recent advancements in Machine Learning and Artificial Intelligence have driven the widespread adoption of learning algorithms across many domains, such as pricing, auctions, and advertising. However, there is a growing literature to show that these algorithms may learn to collude without explicit coordination or instruction, which presents significant challenges, both economic and regulatory(see Ezrachi (2016), Gautier et al. (2020), Cartea et al. (2022),Hartline et al. (2024) and references therein). The potential for collusion among learning agents was demonstrated in Calvano et al. (2020) through a pricing game, where agents employing learning algorithms consistently selected prices above competitive levels.