Simulating Social Behavior of LLM-Based Autonomous Negotiator Agents in a Game-Theoretical Framework Using Multi-Agent Systems

초록

Simulation is a widely used approach for evaluating system performance, robustness, and potential issues during design and testing. Large Language Models (LLMs) have recently shown strong potential in autonomous agent systems, including negotiation tasks-a core aspect of commerce. This paper evaluates LLM-based autonomous negotiator agents (LANAs) in a buyer-seller bargaining game to assess their decision-making and reasoning. We simulate interactions between agents embodying contrasting social behaviors: (a) Cunning vs. Kind, and (b) Greedy vs. Generous. By analyzing both the game outcomes and the agents' internal reasoning, we find that LLMs can effectively simulate distinct social behaviors in both dialogue and decision-making. Our results offer insights into how social traits affect negotiation dynamics, emphasizing the importance of clear policy design to ensure fairness and reliability in LANA-based systems.

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