Understanding evacuation decision-making behaviour is one of the key components for designing disaster mitigation policies. This study investigates how communications between household agents in a community influence self-evacuation decisions. We develop an agent-based model that simulates household agents' decisions to evacuate or stay. These agents interact within the framework of evolutionary game theory, effectively competing for limited shared resources, which include property recovery funds and coordination services. We explore four scenarios that model different prioritisations of access to government-provided incentives. We discover that the impact of the incentive diminishes both with increasing funding value and the household agent prioritisation, indicating that there is an optimal level of government support beyond which further increases become impractical. Furthermore, the overall evacuation rate depends on the structure of the underlying social network, showing discontinuous jumps when the prioritisation moves across the node degree. We identify the so-called "community influencers", prioritisation of whom significantly increases the overall evacuation rate. In contrast, prioritising household agents with low connectivity may actually impede collective evacuation. These findings demonstrate the importance of social connectivity between household agents. The results of this study are useful for designing optimal government policies to incentivise and prioritise community evacuation under limited resources.

翻译：理解疏散决策行为是设计灾害缓解政策的关键组成部分之一。本研究探讨社区中家庭智能体之间的沟通如何影响自主疏散决策。我们开发了一个基于智能体的模型，用于模拟家庭智能体选择疏散或留驻的决策过程。这些智能体在演化博弈论的框架下进行互动，有效竞争有限的共享资源，包括财产恢复资金和协调服务。我们模拟了四种不同政府激励获取优先级分配的情景。研究发现，激励措施的影响会随着资金规模的增加和家庭智能体优先级的提升而减弱，这表明存在一个政府支持的最优水平，超过该水平后进一步增加支持将变得不切实际。此外，整体疏散率取决于底层社会网络的结构，当优先级分配跨越节点度阈值时会出现不连续跃变。我们识别出所谓的"社区影响者"，优先考虑这类个体能显著提升整体疏散率。相反，优先考虑连接度低的家庭智能体反而可能阻碍集体疏散。这些发现证明了家庭智能体间社会连接的重要性。本研究结果可为设计最优政府政策提供参考，以在有限资源条件下有效激励和优化社区疏散的优先级分配。