ALYMPICS：LLM智能体遇见博弈论——探索AI智能体的战略决策
ALYMPICS: LLM Agents Meet Game Theory -- Exploring Strategic Decision-Making with AI Agents
AI总结：
本文提出Alympics仿真框架，利用LLM智能体研究博弈论问题，并通过水资源分配挑战验证其在战略决策分析中的有效性。
摘要：
本文介绍了Alympics（Agents的奥林匹克），一个利用大语言模型（LLM）智能体进行博弈论研究的系统化仿真框架。Alympics创建了一个多功能平台，用于研究复杂的博弈论问题，通过提供受控环境来模拟LLM智能体的人类式战略交互，弥合了理论博弈论与实证研究之间的鸿沟。在我们的试点案例研究“水资源分配挑战”中，我们通过一个聚焦于稀缺生存资源多轮拍卖的具有挑战性的战略博弈来探索Alympics。该研究展示了该框架在定性和定量分析博弈决定因素、策略和结果方面的能力。此外，我们对LLM智能体在战略决策场景中的表现进行了全面的人工评估和深入评价。我们的发现不仅拓展了对LLM智能体模拟人类战略行为能力的理解，还突显了它们在推进博弈论知识方面的潜力，从而丰富我们对博弈论的理解，并赋能利用LLM智能体在战略决策领域的进一步研究。代码、提示词及所有相关资源可在https://github.com/microsoft/Alympics获取。
英文摘要：
This paper introduces Alympics (Olympics for Agents), a systematic simulation framework utilizing Large Language Model (LLM) agents for game theory research. Alympics creates a versatile platform for studying complex game theory problems, bridging the gap between theoretical game theory and empirical investigations by providing a controlled environment for simulating human-like strategic interactions with LLM agents. In our pilot case study, the "Water Allocation Challenge," we explore Alympics through a challenging strategic game focused on the multi-round auction on scarce survival resources. This study demonstrates the framework's ability to qualitatively and quantitatively analyze game determinants, strategies, and outcomes. Additionally, we conduct a comprehensive human assessment and an in-depth evaluation of LLM agents in strategic decision-making scenarios. Our findings not only expand the understanding of LLM agents' proficiency in emulating human strategic behavior but also highlight their potential in advancing game theory knowledge, thereby enriching our understanding of both game theory and empowering further research into strategic decision-making domains with LLM agents. Codes, prompts, and all related resources are available at https://github.com/microsoft/Alympics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
