Generative Agent-Based Modeling: Unveiling Social System Dynamics
  through Coupling Mechanistic Models with Generative Artificial Intelligence
Navid Ghaffarzadegan Aritra Majumdar Ross Williams Niyousha Hosseinichimeh
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.