We discuss the emerging new opportunity for building feedback-rich computational models of social systems using generative artificial intelligence. Referred to as Generative Agent-Based Models (GABMs), such individual-level models utilize large language models such as ChatGPT to represent human decision-making in social settings. We provide a GABM case in which human behavior can be incorporated in simulation models by coupling a mechanistic model of human interactions with a pre-trained large language model. This is achieved by introducing a simple GABM of social norm diffusion in an organization. For educational purposes, the model is intentionally kept simple. We examine a wide range of scenarios and the sensitivity of the results to several changes in the prompt. We hope the article and the model serve as a guide for building useful diffusion models that include realistic human reasoning and decision-making.

翻译：我们探讨了利用生成式人工智能构建反馈丰富的社会系统计算模型的新兴机遇。这类被称为生成式基于智能体模型（GABMs）的个体层面模型，借助ChatGPT等大型语言模型来模拟社会情境中的人类决策过程。我们提供了一个GABM案例，通过将人类互动机理模型与预训练大型语言模型相结合，可在仿真模型中融入人类行为。该案例通过构建组织内社会规范扩散的简单GABM实现。出于教学目的，模型被刻意保持简洁。我们考察了广泛场景及提示词若干变化对结果的敏感性。希望本文及模型能为构建包含真实人类推理与决策过程的有用扩散模型提供指导。