The final frontier for simulation is the accurate representation of complex, real-world social systems. While agent-based modeling (ABM) seeks to study the behavior and interactions of agents within a larger system, it is unable to faithfully capture the full complexity of human-driven behavior. Large language models (LLMs), like ChatGPT, have emerged as a potential solution to this bottleneck by enabling researchers to explore human-driven interactions in previously unimaginable ways. Our research investigates simulations of human interactions using LLMs. Through prompt engineering, inspired by Park et al. (2023), we present two simulations of believable proxies of human behavior: a two-agent negotiation and a six-agent murder mystery game.

翻译：仿真的最终前沿是对复杂真实世界社会系统的准确表示。尽管基于智能体的建模（ABM）旨在研究智能体在更大系统中的行为与交互，但它无法忠实地捕捉人类驱动行为的全部复杂性。大型语言模型（LLMs），如ChatGPT，已作为一种潜在解决方案出现，能够以前所未有的方式使研究人员探索人类驱动的交互，从而突破这一瓶颈。我们的研究利用大型语言模型调查人类交互的仿真。受Park等人（2023）启发，通过提示工程，我们展示了两个可模拟人类行为可信代理的仿真案例：一个双智能体谈判场景和一个六智能体谋杀谜题游戏。