Evaluating LLM Agent Collusion in Double Auctions

Large language models (LLMs) have demonstrated impressive capabilities as autonomous agents with rapidly expanding applications in various domains. As these agents increasingly participate in social and economic settings, understanding their behavior in economic markets becomes necessary. In this work, we examine scenarios where they can choose to collude, defined as secretive cooperation that harms another party. To systematically study this, we investigate LLM agent behavior in continuous negotiations through simulated double auction markets. Through a series of controlled experiments, we analyze how parameters such as the ability to communicate, choice of model, and presence of environmental pressures affect the stability and emergence of seller collusion. We find that direct seller communication increases collusive tendencies, propensity to collude varies across models, and environmental pressures such as oversight and urgency from authority figures influence collusive behavior. Our findings highlight important economic and ethical considerations for the deployment of LLM-based market agents.