Machine-learning technologies are seeing increased deployment in real-world market scenarios. In this work, we explore the strategic behaviors of large language models (LLMs) when deployed as autonomous agents in multi-commodity markets, specifically within Cournot competition frameworks. We examine whether LLMs can independently engage in anti-competitive practices such as collusion or, more specifically, market division. Our findings demonstrate that LLMs can effectively monopolize specific commodities by dynamically adjusting their pricing and resource allocation strategies, thereby maximizing profitability without direct human input or explicit collusion commands. These results pose unique challenges and opportunities for businesses looking to integrate AI into strategic roles and for regulatory bodies tasked with maintaining fair and competitive markets. The study provides a foundation for further exploration into the ramifications of deferring high-stakes decisions to LLM-based agents.

翻译：机器学习技术正日益广泛地应用于现实市场场景。本研究探讨了将大型语言模型（LLMs）作为自主智能体部署于多商品市场（特别是古诺竞争框架）时的策略性行为。我们检验LLMs能否独立从事反竞争行为，例如合谋或更具体地，市场分割。研究发现，LLMs能够通过动态调整定价与资源配置策略，有效垄断特定商品，从而在无需人工直接干预或明确合谋指令的情况下实现利润最大化。这些结果为寻求将人工智能整合至战略角色的企业，以及负责维护公平竞争市场的监管机构，带来了独特的挑战与机遇。本研究为进一步探索将高风险决策权授予基于LLM的智能体所产生的影响奠定了基础。