Effects of Property Recovery Incentives and Social Interaction on Self-Evacuation Decisions in Natural Disasters: An Agent-Based Modelling Approach

2026-02-23

AI Summary

This study investigates how individual household evacuation decisions during natural disasters are influenced by property recovery incentives and social interactions, aiming to optimize disaster response policies under limited resources. By integrating agent-based modeling (ABM) with evolutionary game theory, the authors develop a dynamic model of evacuation decision-making within community social networks, simulating information diffusion and strategic evolution under resource constraints. The findings reveal that the effectiveness of incentives exhibits an optimal threshold, evacuation rates undergo discontinuous jumps depending on network topology, and a novel class of “community influencers” is identified: prioritizing support for highly connected individuals significantly enhances collective evacuation efficiency, whereas prioritizing poorly connected individuals suppresses it. These insights provide quantitative foundations and optimization strategies for governments to design targeted and efficient evacuation policies.

Technology Category

• Game Theory and Economic Paradigms: Social Choice / Voting
• Multiagent Systems: Mechanism Design
• Humans and AI: Planning and Decision Support for Human-Machine Teams

Application Category

• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Social Networks and Social Media: Influence propagation, information diffusion, and the prediction on networks
• Web Mining and Content Analysis: Models for Web evolution

Abstract

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.

Problem

• evacuation decision-making
• property recovery incentives
• social interaction
• agent-based modelling
• disaster mitigation

Innovation

• agent-based modelling
• evolutionary game theory
• social network structure
• property recovery incentives
• community influencers