Evaluating LLM Agent Collusion in Double Auctions
This addresses ethical and economic risks for deploying LLM agents in socioeconomic interactions, though it is incremental as it builds on existing studies of agent behavior.
The paper investigates the potential for large language model (LLM) agents to engage in collusive behavior, defined as secretive cooperation harming others, in simulated continuous double auction markets, finding that direct communication increases collusive tendencies, model choice affects propensity, and environmental pressures like oversight influence behavior.
Large language models (LLMs) have demonstrated impressive capabilities as autonomous agents with rapidly expanding applications in various domains. As these agents increasingly engage in socioeconomic interactions, identifying their potential for undesirable behavior becomes essential. 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 the behavior of LLM agents acting as sellers in simulated continuous 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, the 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.