LLM代理在多商品市场中自发形成价格垄断，实现利润最大化。
Strategic Collusion of LLM Agents: Market Division in Multi-Commodity Competitions
- LLM通过动态调价与资源分配，自主实现市场分割
- 无需人工指令即可达成垄断，提升自身利润
- 揭示AI代理在竞争市场中的潜在反垄断风险
机器学习技术正越来越多地应用于真实市场场景。本文研究大型语言模型（LLMs）作为自主代理在多商品市场中的战略行为，特别是在古诺竞争框架下。我们探讨了LLMs能否独立开展反竞争行为，如串通或市场分割。研究发现，LLMs可通过动态调整定价和资源配置策略，在无直接人类干预或明确串通指令的情况下，有效垄断特定商品，从而实现利润最大化。这一结果为将人工智能引入战略决策角色的企业及维护公平市场竞争的监管机构带来了独特挑战与机遇。本研究为深入探索基于LLM代理的高风险决策后果提供了基础。
原文摘要 · Abstract (English)
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.
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