使用认知合理的强化学习对流行病学网络中的口罩佩戴行为进行建模
Masking Behaviors in Epidemiological Networks with Cognitively-plausible Reinforcement Learning
AI总结：
本文提出融合认知架构与流行病学模拟的基于智能体模型，通过效用函数刻画口罩佩戴决策，并系统分析行为参数与社会网络结构对感染传播的影响。
摘要：
COVID-19疫情凸显了人类行为在影响传染病传播方面的关键作用，以及建立能够捕捉这一复杂动态的模型的必要性。我们提出了一个基于智能体的模型，将疾病传播的流行病学模拟与驱动个体口罩佩戴决策的认知架构相结合。智能体根据一个效用函数决定是否佩戴口罩，该函数对同伴从众、个人风险容忍度和口罩佩戴不适等因素进行加权。通过系统地改变行为模型参数和社会网络结构开展实验，我们展示了适应性决策如何与网络连接模式相互作用，从而影响人群层面的感染结果。该模型提供了一个灵活的计算框架，有助于理解口罩强制令等行为干预如何在具有不同社会结构的社区中对疾病传播产生差异化影响。研究结果强调了在流行病学模型中整合真实人类决策过程的重要性，以便在公共卫生危机期间为政策决策提供依据。
英文摘要：
The COVID-19 pandemic highlighted the critical role of human behavior in influencing infectious disease transmission and the need for models capturing this complex dynamic. We present an agent-based model integrating an epidemiological simulation of disease spread with a cognitive architecture driving individual mask-wearing decisions. Agents decide whether to mask based on a utility function weighting factors like peer conformity, personal risk tolerance, and mask-wearing discomfort. By conducting experiments systematically varying behavioral model parameters and social network structures, we demonstrate how adaptive decision-making interacts with network connectivity patterns to impact population-level infection outcomes. The model provides a flexible computational framework for gaining insights into how behavioral interventions like mask mandates may differentially influence disease spread across communities with diverse social structures. Findings highlight the importance of integrating realistic human decision processes in epidemiological models to inform policy decisions during public health crises.
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。
