This article proposes a social simulation paradigm based on the GPT-3.5 large language model. It involves constructing Generative Agents that emulate human cognition, memory, and decision-making frameworks, along with establishing a virtual social system capable of stable operation and an insertion mechanism for standardized public events. The project focuses on simulating a township water pollution incident, enabling the comprehensive examination of a virtual government's response to a specific public administration event. Controlled variable experiments demonstrate that the stored memory in generative agents significantly influences both individual decision-making and social networks. The Generative Agent-Based Simulation System introduces a novel approach to social science and public administration research. Agents exhibit personalized customization, and public events are seamlessly incorporated through natural language processing. Its high flexibility and extensive social interaction render it highly applicable in social science investigations. The system effectively reduces the complexity associated with building intricate social simulations while enhancing its interpretability.

翻译：本文提出了一种基于GPT-3.5大语言模型的社会仿真范式。该方法构建了模拟人类认知、记忆和决策框架的生成式智能体，建立了可稳定运行的虚拟社会系统，并设计了标准化公共事件的插入机制。项目聚焦于模拟乡镇水污染事件，能够全面考察虚拟政府对特定公共管理事件的响应。控制变量实验表明，生成式智能体中存储的记忆对个体决策及社会网络均产生显著影响。该生成式智能体仿真系统为社会科学与公共管理研究引入了新路径：智能体具备个性化定制能力，公共事件可通过自然语言处理无缝嵌入。其高灵活性与广泛的社会交互特性使其在社会科学研究中具有高度适用性。该系统有效降低了构建复杂社会仿真的难度，同时增强了仿真的可解释性。