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.

翻译：本文介绍Alympics（面向智能体的奥林匹克），一个利用大语言模型智能体进行博弈论研究的系统性仿真框架。Alympics为研究复杂博弈论问题创建了通用平台，通过提供受控环境模拟LLM智能体的人类战略互动，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，我们通过聚焦稀缺生存资源多轮拍卖的战略博弈对Alympics进行了探索。该研究展示了框架对博弈决定因素、策略和结果进行定性与定量分析的能力。此外，我们在战略决策场景中对LLM智能体进行了全面的人类评估与深入评测。研究结果不仅拓展了对LLM智能体模拟人类战略行为能力的理解，还凸显了其在推进博弈论知识方面的潜力，从而既丰富了我们对博弈论的认识，又推动了利用LLM智能体在战略决策领域的深入研究。代码、提示词及相关资源均可在https://github.com/microsoft/Alympics获取。