Complex cognitive, emotional, and social processes shape human evacuations during natural disasters. Accurate modeling and understanding of human behavior in disasters or emergencies can greatly impact the evacuation process by informing more effective planning and resource allocation. However, collecting human data in these situations is very difficult, and existing computational evacuation models assume rational, homogeneous behavior, leading to unrealistic, overly optimistic predictions. To address this gap, we present a simulation framework of sequential human decision-making during an evacuation scenario, introducing cognitively grounded, persona-driven agents. Our framework models evacuation behavior in a grid-based urban environment that evolves over time, capturing fire and other hazards. Human agents are modeled as personas that make sequential decisions in response to environmental stimuli with cognition structured in three levels: high-level evacuation goals, mid-level route reasoning, and low-level navigation. Decision-making is driven by large language models (LLMs) coupled with a cognitive module and calibrated with empirical human evacuation data. We propose a dynamic, stimulus-driven disaster simulation framework that models human evacuation decision-making using persona-conditioned LLM agents and a cognitive hierarchy.

翻译：自然灾害中，复杂的认知、情感和社会过程塑造了人类的疏散行为。在灾难或紧急情况下，对人类行为的准确建模与理解可通过优化预案制定和资源分配极大地影响疏散过程。然而，在此类场景中收集人类数据极为困难，且现有计算疏散模型假设理性、同质化的行为，导致不真实且过度乐观的预测结果。针对这一不足，我们提出了一种疏散场景下人类连续决策的仿真框架，引入了基于认知基础的人格驱动智能体。该框架在随时间演变的网格化城市环境中模拟疏散行为，并捕捉火灾及其他危险因素。人类智能体被建模为具有人格特征的个体，其根据环境刺激做出连续决策，认知结构分为三个层级：高层疏散目标、中层路径推理和低层导航。决策过程由大语言模型驱动，并耦合认知模块，同时利用实证人类疏散数据进行校准。我们提出了一种动态的、刺激驱动的灾难仿真框架，采用人格条件化的大语言模型智能体与认知层级结构对人类疏散决策过程进行建模。