This paper introduces Alympics, a platform that leverages Large Language Model (LLM) agents to facilitate investigations in game theory. By employing LLMs and autonomous agents to simulate human behavior and enable multi-agent collaborations, we can construct realistic and dynamic models of human interactions for game theory hypothesis formulating and testing. To demonstrate this, we present and implement a survival game involving unequal competition for limited resources. Through manipulation of resource availability and agent personalities, we observe how different agents engage in the competition and adapt their strategies. The use of LLM agents in game theory research offers significant advantages, including simulating realistic behavior, providing a controlled, scalable, and reproducible environment. Our work highlights the potential of LLM agents in enhancing the understanding of strategic decision-making within complex socioeconomic contexts. All codes are available at https://github.com/microsoft/Alympics

翻译：本文介绍Alympics平台，该平台利用大型语言模型（LLM）智能体推动博弈论研究。通过采用LLM与自主智能体模拟人类行为并实现多智能体协作，我们能够构建真实且动态的人类互动模型，用于博弈论假设的提出与验证。为验证该平台的有效性，我们设计并实现了一个涉及有限资源不平等竞争的生存博弈实验。通过调控资源可用性与智能体性格特征，我们观察到不同智能体如何参与竞争并调整其策略。将LLM智能体应用于博弈论研究具有显著优势，包括模拟真实行为、提供可控、可扩展且可复现的实验环境。本工作凸显了LLM智能体在增强对复杂社会经济情境中战略决策机制理解的潜力。所有代码已开源：https://github.com/microsoft/Alympics