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

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.

Figures (9)

• Figure 1: Illustration of interactions between households in the model. The payoff applied depends on the decisions of the two interacting parties.
• Figure 2: Evacuation rates in a. a randomised highest-degree scenario and b. a fixed highest-degree scenario. Each point is the average of 5 simulations, and the error band has a width of 2 standard deviations. Vertical lines indicate the fractions of nodes corresponding to the degree change, ranging from degree 9 to degree 3: 0.04%, 0.42%, 3.78%, 19.26%, 56.98%, 98.2%, and 98.8%. These thresholds mark the points at which agents with specific degrees begin to receive prioritised treatment.
• Figure 3: Evacuation rates in a) a randomised lowest-degree scenario and b) a fixed lowest-degree scenario. In contrast to the highest-degree scenarios, prioritised treatment was initiated from the lowest-degree nodes, ranging from degree 3 to degree 9: 1.2%, 1.8%, 43.02%, 80.74%, 96.22%, 99.58%, and 99.96%. The results indicate that low-degree nodes make a negligible contribution to the evacuation rates.
• Figure 4: The degree distribution of the small-world network used in the simulations.
• Figure 5: Comparison of results around the transition between 50% and 50.4% prioritisation(Plot a), and between 56.6% and 57% prioritisation(Plot b) in the 0% incentive values. Plots c) and d) show five curves with the same priority values (50% and 56.6%). This figure shows that different results occurred around the thresholds at both different and the same priority levels.