This study offers a new paradigm of individual-level modeling to address the grand challenge of incorporating human behavior in epidemic models. Using generative artificial intelligence in an agent-based epidemic model, each agent is empowered to make its own reasonings and decisions via connecting to a large language model such as ChatGPT. Through various simulation experiments, we present compelling evidence that generative agents mimic real-world behaviors such as quarantining when sick and self-isolation when cases rise. Collectively, the agents demonstrate patterns akin to multiple waves observed in recent pandemics followed by an endemic period. Moreover, the agents successfully flatten the epidemic curve. This study creates potential to improve dynamic system modeling by offering a way to represent human brain, reasoning, and decision making.

翻译：本研究提出了一种个体层面建模的新范式，旨在应对将人类行为纳入流行病模型的重大挑战。通过在基于智能体的流行病模型中运用生成式人工智能，每个智能体能够通过连接至大型语言模型（如ChatGPT）自主进行推理与决策。通过多种仿真实验，我们提供了有力证据，表明生成式智能体能够模拟真实世界的行为，例如患病时自我隔离以及病例增加时主动回避。整体而言，这些智能体展现出的模式与近年观察到的多次疫情波及其后的地方性流行阶段相似。此外，智能体成功实现了疫情曲线的平缓化。本研究通过提供一种表征人类思维、推理与决策过程的途径，为改进动态系统建模创造了可能。