Less Than Meets the Eye : Simultaneous Experiments as a Source of Algorithmic Seeming Collusion

This article explains the supra-competitive profits observed in Calvano et al.(2020) and subsequent literature. We refute the idea that the phenomenon arises from collusive mechanisms, where each agent exerts the threat of punishment. Instead, our analysis reveals that the high prices and apparent punishment schemes result from simultaneous experimentation and the learning inertia inherent in most reinforcement learning techniques. Additionally, we demonstrate that such seeming collusion can arise in memoryless environments and much faster than previously thought. Our findings challenge the policy implications of prior research and invite new, simpler approaches to address the issue of algorithmic supra-competitive pricing