对传染病爆发期间的个人决策进行建模对于理解行为动态和为有效的公共卫生干预措施提供信息至关重要。先前的工作表明，大型语言模型可以通过根据人口统计提示和情境上下文生成代理决策来模拟现实的人类行为。在此基础上，我们建立了一个基于空间的,基于代理的模拟框架，该框架将法学硕士生成的有关自我报告的流感样疾病的决策集成到基于人口普查的综合代理群体中。位置被视为核心特征: 代理被分配到城市内的空间单元, 使用现实世界的人口普查数据捕获不同人口群体的空间分布，并实现地理上多样化的行为建模。我们实施并比较了三种决策场景,独立推理,家庭影响力,和消息框架,，并模拟旧金山和亚特兰大的自我报告结果。结果显示，收入和教育是报告率变化, 的主要驱动因素，地理, LLM 模型选择, 和消息框架的影响较小但一致。我们的框架生成综合数据，捕获社会和地理异质性,，支持空间流行病学建模和偏见意识行为分析。
Modelling individual decision-making during infectious disease outbreaks is crucial for understanding behavioural dynamics and informing effective public health interventions. Prior work has shown that large language models can simulate realistic human behaviour by generating agent decisions based on demographic prompts and situational context. We build on this foundation with a spatially grounded, agent-based simulation framework that integrates LLM-generated decisions about self-reported influenza-like illness into a census-based synthetic population of agents. Location is treated as a central feature: agents are assigned to spatial units within cities, capturing the spatial distributions of different demographic groups using real-world census data and enabling geographically diverse behavioural modelling. We implement and compare three decision scenarios, independent reasoning, household influence, and message framing, and simulate self-reporting outcomes in San Francisco and Atlanta. Results reveal that income and education are the dominant drivers of reporting rate variation, with smaller but consistent effects from geography, LLM model choice, and message framing. Our framework generates synthetic data that captures both social and geographic heterogeneity, supporting spatial epidemiological modelling and bias-aware behavioural analysis.
科目:人工智能(cs.AI)
Subjects: Artificial Intelligence (cs.AI)