As artificial intelligence increasingly automates decision-making in competitive markets, understanding the resulting dynamics and ensuring fair market mechanisms is essential. We investigate the multi-faceted decision-making of large language models (LLMs) in oligopolistic Cournot markets, showing that LLMs not only grasp complex market dynamics--demonstrating their potential as effective economic planning agents--but also engage in sustained tacit collusion, driving prices up to 200% above Nash equilibrium levels. Our analysis examines LLM behavior across three dimensions-(1) decision type, (2) opponent strategies, and (3) market composition--revealing how these factors may shape the competitiveness of LLM-based decision-makers. Furthermore, we show that regulating a few dominant agents by enforcing best-response strategies effectively disrupts collusion and helps restore competitive pricing. Our findings identify potential concerns associated with AI integration in competitive market environments and provide regulatory policy recommendations for the era of automation.

翻译：随着人工智能在竞争性市场中日益自动化决策过程，理解由此产生的动态并确保公平的市场机制至关重要。我们研究了大型语言模型（LLMs）在寡头垄断的古诺市场中的多维度决策行为，结果表明LLMs不仅能理解复杂的市场动态——展现了其作为有效经济规划代理的潜力——还会参与持续的隐性共谋，将价格推高至纳什均衡水平之上200%。我们的分析从三个维度考察了LLM的行为：（1）决策类型，（2）对手策略，以及（3）市场构成，揭示了这些因素如何影响基于LLM的决策者的竞争性。此外，我们证明通过对少数主导代理实施最佳响应策略进行监管，能有效打破共谋并帮助恢复竞争性定价。我们的研究发现了人工智能融入竞争性市场环境可能带来的潜在问题，并为自动化时代的监管政策提供了建议。