Epidemic Modeling with Generative Agents
This addresses the problem of improving epidemic modeling for public health by introducing a new paradigm to represent human reasoning and decision-making, though it is a new paradigm rather than incremental.
The study tackled the challenge of incorporating human behavior into epidemic models by using generative AI agents that make individual decisions via large language models, resulting in agents that mimic real-world behaviors like quarantining and successfully flatten the epidemic curve.
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.