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This article proposes a social simulation paradigm based on the GPT-3.5 largelanguage model. It involves constructing Generative Agents that emulate humancognition, memory, and decision-making frameworks, along with establishing avirtual social system capable of stable operation and an insertion mechanismfor standardized public events. The project focuses on simulating a townshipwater pollution incident, enabling the comprehensive examination of a virtualgovernment's response to a specific public administration event. Controlledvariable experiments demonstrate that the stored memory in generative agentssignificantly influences both individual decision-making and social networks.  The Generative Agent-Based Simulation System introduces a novel approach tosocial science and public administration research. Agents exhibit personalizedcustomization, and public events are seamlessly incorporated through naturallanguage processing. Its high flexibility and extensive social interactionrender it highly applicable in social science investigations. The systemeffectively reduces the complexity associated with building intricate socialsimulations while enhancing its interpretability.

Simulating Public Administration Crisis: A Novel Generative Agent-Based Simulation System to Lower Technology Barriers in Social Science Research