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 economic and social settings, understanding their behavior as social agents becomes necessary. In this work, we examine scenarios where they can choose to cooperate in undesirable ways, i.e., collude. 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 (1) direct seller communication increases collusive tendencies; (2) propensity to collude varies across models; and (3) environmental pressures such as oversight and coercion influence collusive behavior. Our findings highlight important economic and ethical considerations for the deployment of LLM-based market agents and suggest potential regulatory approaches to mitigate collusive behaviors.