Tacit Algorithmic Collusion when Platforms Use Recommendation Systems

Algorithmic pricing has drawn significant attention from economists, legal scholars, and antitrust officials due to its growing use across various industries and evidence of their ability to tacitly collude. Additionally, platforms that connect producers with consumers are increasingly utilizing AI-based recommendation systems (RSs) to determine which products to display to which users. In this paper, we explore the potential for collusion between algorithms engaged in pricing competition on platforms, where both the pricing and recommendation systems rely on reinforcement learning (RL). This study demonstrates that firms employing algorithmic pricing can obtain anti-competitive outcomes even when acting on a platform using an AI-based RSs, although the collusive capacity is inhibited.