Stochastic Multi-Agent-Based Model to Measure Community Resilience-Part 2: Simulation Results

TL;DR

This paper uses a stochastic multi-agent simulation to analyze community resilience during natural disasters, highlighting the importance of cooperation, empathy, and information sharing in improving social well-being.

Contribution

It introduces a novel stochastic multi-agent framework to evaluate community resilience considering social, mental, and physical factors during disasters.

Findings

High cooperation improves individual behavior.

Empathy and smaller community size can enhance resilience.

Community dynamics significantly affect social well-being.

Abstract

In this paper we investigate the resiliency planning of interdependent electric power systems and emergency services. We investigate the effect of the level of empathy, cooperation, coordination, flexibility, and experience of individuals on their mental well-being. Furthermore, we explore the impact of the information that is provided by emergency services and the impact of the availability of electric energy on the physical, mental, and social well-being of individuals. For our simulations, we use a stochastic, multi-agent-based numerical framework that is reported in the companion paper for estimating the social well-being of a community when facing natural disasters such as hurricanes, floods, earthquakes, and tsunamis. The performance of the proposed method is assessed by measuring community resilience for a multitude of effects in the context of two case studies. These effects are analyzed for Gaussian social random characteristics. Each case study considers nine agents, namely, three areas of three communities each, yielding a total of six communities. The results show that a high level of cooperation can positively change individual behavior. In addition, the relationship among the individuals of a community is so vital that the society with less population and more empathy may be more resilient than the community with more population and less empathy.