We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). In oligopoly settings, LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits. Variation in seemingly innocuous phrases in LLM instructions ("prompts") substantially influence the degree of supracompetitive pricing. We develop novel techniques for behavioral analysis of LLMs and use them to uncover price-war concerns as a contributing factor. Our results extend to auction settings. Our findings uncover unique challenges to any future regulation of LLM-based pricing agents, and AI-based pricing agents more broadly.

翻译：我们基于大型语言模型（LLM）开展了算法定价智能体的实验研究。在寡头垄断市场环境中，基于LLM的定价智能体能够快速自主地达成超竞争水平的价格与利润。LLM指令（"提示词"）中看似无害的短语变化会显著影响超竞争定价的程度。我们开发了用于LLM行为分析的新技术，并借此揭示了价格战担忧是促成该现象的重要因素。我们的研究结果在拍卖场景中同样成立。本研究发现揭示了未来对基于LLM的定价智能体乃至更广泛的基于AI的定价智能体进行监管时将面临独特挑战。