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 will be made public soon.

翻译：本文介绍Alympics，一个利用大语言模型智能体促进博弈论研究的平台。通过运用LLM和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交互模型，用于博弈论假设的提出与检验。为验证这一方法，我们提出并实现了一个涉及有限资源不平等竞争的生存博弈。通过操控资源可用性与智能体人格特质，我们观察不同智能体如何参与竞争并调整策略。在博弈论研究中使用LLM智能体具有显著优势，包括模拟真实行为、提供可控、可扩展及可复现的实验环境。本研究揭示了LLM智能体在增强对复杂社会经济情境中策略性决策理解方面的潜力。所有代码将很快公开。