AI agents are increasingly deployed as quasi-autonomous systems for specialized tasks, yet their potential as computational models of decision-making remains underexplored. We develop a generative AI agent to study repetitive policy decisions during an epidemic, embedding the agent, prompted to act as a city mayor, within a simulated SEIR environment. Each week, the agent receives updated epidemiological information, evaluates the evolving situation, and sets business restriction levels. The agent is equipped with a dynamic memory that weights past events by recency and is evaluated in both single- and ensemble-agent settings across environments of varying complexity. Across scenarios, the agent exhibits human-like reactive behavior, tightening restrictions in response to rising cases and relaxing them as risk declines. Crucially, providing the agent with brief systems-level knowledge of epidemic dynamics, highlighting feedbacks between disease spread and behavioral responses, substantially improves decision quality and stability. The results illustrate how theory-informed prompting can shape emergent policy behavior in AI agents. These findings demonstrate that generative AI agents, when situated in structured environments and guided by minimal domain theory, can serve as powerful computational models for studying decision-making and policy design in complex social systems.

翻译：人工智能代理正日益作为准自主系统部署于专业任务中，但其作为决策计算模型的潜力仍未得到充分探索。我们开发了一个生成式人工智能代理，用于研究流行病期间的重复性政策决策。该代理被设定为扮演市长角色，并嵌入模拟的SEIR环境中。每周，代理接收更新的流行病学信息，评估不断变化的局势，并设定商业限制级别。该代理配备了动态记忆模块，可根据时效性对过往事件进行加权处理，并在不同复杂度的环境中，通过单代理与集成代理两种设置进行评估。在所有模拟场景中，该代理均表现出类人的反应行为：在病例上升时加强限制，在风险下降时放松管控。关键的是，当向代理提供关于流行病动态的简要系统级知识（着重强调疾病传播与行为响应之间的反馈机制）时，其决策质量与稳定性得到显著提升。研究结果表明，通过理论引导的提示可以塑造人工智能代理中涌现的政策行为。这些发现证明，生成式人工智能代理在结构化环境中运行并接受最少领域理论指导时，能够作为强大的计算模型，用于研究复杂社会系统中的决策制定与政策设计。