利用大语言模型代理进行协调的流行病防控
Coordinated Pandemic Control with Large Language Model Agents as Policymaking Assistants
AI总结：
本文提出利用大语言模型多代理系统进行协调流行病防控，通过模拟和闭环过程减少感染和死亡率。
摘要：
有效的流行病防控需要在本质上相互依赖的行政区域之间及时且协调的政策制定。然而，人类驱动的响应往往碎片化且反应性，政策通常是在疫情爆发后才制定，仅在疫情加剧后才进行调整，这削弱了主动干预和全球流行病缓解的能力。为了解决这一挑战，我们提出了一种大型语言模型（LLM）多代理政策制定框架，以支持跨区域的协调和主动流行病防控。在我们的框架中，每个行政区域被分配一个LLM代理作为人工智能政策制定助手。该代理在考虑区域特定的流行病动态的同时，与其他代理沟通以考虑跨区域的相互依赖性。通过整合真实世界数据、流行病演变模拟器和结构化的跨代理通信，我们的框架使代理能够共同探索反事实干预场景，并通过闭环模拟过程合成协调的政策决策。我们使用美国各州2020年4月至12月的新冠数据以及真实世界移动记录和观察到的政策干预来验证所提出的框架。与现实世界流行病结果相比，我们的方法在个体州层面减少了累计感染和死亡人数分别高达63.7%和40.1%，在跨州汇总时分别减少了39.0%和27.0%。这些结果表明，LLM多代理系统可以通过协调的政策制定实现更有效的流行病防控。
英文摘要：
Effective pandemic control requires timely and coordinated policymaking across administrative regions that are intrinsically interdependent. However, human-driven responses are often fragmented and reactive, with policies formulated in isolation and adjusted only after outbreaks escalate, undermining proactive intervention and global pandemic mitigation. To address this challenge, here we propose a large language model (LLM) multi-agent policymaking framework that supports coordinated and proactive pandemic control across regions. Within our framework, each administrative region is assigned an LLM agent as an AI policymaking assistant. The agent reasons over region-specific epidemiological dynamics while communicating with other agents to account for cross-regional interdependencies. By integrating real-world data, a pandemic evolution simulator, and structured inter-agent communication, our framework enables agents to jointly explore counterfactual intervention scenarios and synthesize coordinated policy decisions through a closed-loop simulation process. We validate the proposed framework using state-level COVID-19 data from the United States between April and December 2020, together with real-world mobility records and observed policy interventions. Compared with real-world pandemic outcomes, our approach reduces cumulative infections and deaths by up to 63.7% and 40.1%, respectively, at the individual state level, and by 39.0% and 27.0%, respectively, when aggregated across states. These results demonstrate that LLM multi-agent systems can enable more effective pandemic control with coordinated policymaking...
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
