用大模型提升疫情模拟中个体决策的实时适应能力
LLM-powered reasoning in agent-based modeling
- 将大模型嵌入基于代理的模拟，动态预测个体行为
- 在盐湖县新冠模拟中实现对政策响应的实时调整
- 适合政策模拟与公共健康建模的研究者
基于代理的建模（ABM）可模拟数百万个体及其互动，适用于政策制定。但传统ABM依赖静态先验，难以适应实时变化。本研究提出一种可扩展的混合代理与语言驱动流行病（HALE）建模框架，利用大语言模型（LLMs）预测人类决策。以盐湖县新冠传播为例，验证了该框架在模拟中的有效性，实现了对个体行为动态响应的建模。
原文摘要 · Abstract (English)
Agent-based modeling (ABM) has the capability to model millions of individuals and their interactions, which is useful for policy making. However, ABMs have traditionally relied on static prior, which prevents the models from adapting to real-time changes. Our research provides a novel approach to addressing this information gap. Large language models (LLMs) offer new opportunities to predict human decision-making. Here, we introduce a scalable Hybrid Agent-based and Language-driven Epidemic (HALE) modeling framework that leverages LLMs to predict human decision-making in an ABM simulation. As a proof-of-concept, we use HALE to simulate COVID-19 and its effects in Salt Lake County, UT.
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