Title: Breaking Algorithmic Collusion in Human-AI Ecosystems
Primary area: learning theory
Keywords: Algorithmic Collusion, learning in games

Abstract: The growing adoption of AI agents is giving rise to ecosystems where these agents interact not only with each other but also with humans. We study such mixed ecosystems in the context of repeated pricing games, modeling AI agents as playing equilibrium strategies. We then analyze defections, where a human manually performs the task instead of their AI agent, thereby replacing an equilibrium player with a no-regret strategy. Motivated by how populations of AI agents can sustain supracompetitive prices, we ask whether high prices persist under such defections. Our main finding is that even a single human defection can destabilize collusion and drive down prices, while multiple defections push prices close to competitive levels. We further show how the nature of collusion changes under defection-aware AI agents and under greedy (follow-the-leader) defection strategies. Taken together, our results characterize when algorithmic collusion is fragile—and when it persists—in mixed ecosystems of AI agents and humans.

TL;DR: Even a single human defection in a repeated pricing game destabilizes algorithmic collusion—driving prices down from supracompetitive levels to Θ(ln N / N)—highlighting the fragility of collusion in mixed human-AI ecosystems