  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.
