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.

利用生成式人工智能建立反馈丰富的社会系统计算模型，通过生成代理基于模型（GABMs），将人类决策过程在社会环境中进行模拟，并探讨了社会规范扩散的一种简单 GABM 模型，以及该模型在不同情境下的敏感性和重要性。