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（智能体奥林匹克），一个基于大语言模型（LLM）智能体的系统仿真框架，专为博弈论研究设计。Alympics构建了研究复杂博弈问题的通用平台，通过提供可控环境模拟LLM智能体的人类式战略互动，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，我们聚焦稀缺生存资源的多轮拍卖这一战略博弈场景，深入探索Alympics的应用。本研究展示了该框架在定性与定量分析博弈要素、策略及结果方面的能力。此外，我们还对战略决策场景中的LLM智能体进行了全面的人类评估与深度评测。研究结果不仅拓展了我们对LLM智能体模拟人类战略行为能力的认知，更凸显了其在推动博弈论知识发展方面的潜力，从而深化了对博弈论的理解，并为基于LLM智能体的战略决策领域研究提供了有力支持。相关代码、提示词及全部资源已开源至https://github.com/microsoft/Alympics。