Multi-agent Modeling of Hazard-Household-Infrastructure Nexus for Equitable Resilience Assessment

TL;DR

This study develops a multi-agent simulation model to assess the hazard-infrastructure-household nexus, focusing on hurricane-induced power outages, integrating social equity, and validating with empirical data from Hurricane Harvey.

Contribution

The paper introduces a novel multi-agent model that combines hazard, infrastructure, and household dynamics for equitable resilience assessment.

Findings

Prioritizing vulnerable populations improves recovery outcomes.

Model effectively captures complex human-infrastructure-hazard interactions.

Enhances resilience planning by integrating social equity considerations.

Abstract

To enable integrating social equity considerations in infrastructure resilience assessments, this study created a new computational multi-agent simulation model which enables integrated assessment of hazard, infrastructure system, and household elements and their interactions. With a focus on hurricane-induced power outages, the model consists of three elements: 1) the hazard component simulates exposure of the community to a hurricane with varying intensity levels; 2) the physical infrastructure component simulates the power network and its probabilistic failures and restoration under different hazard scenarios; and 3) the households component captures the dynamic processes related to preparation, information seeking, and response actions of households facing hurricane-induced power outages. We used empirical data from household surveys in conjunction with theoretical decision-making models to abstract and simulate the underlying mechanisms affecting experienced hardship of households. The multi-agent simulation model was then tested in the context of Harris County, Texas, and verified and validated using empirical results from Hurricane Harvey in 2017. Then, the model was used to examine effects of different factors such as forewarning durations, social network types, and restoration and resource allocation strategies on reducing the societal impacts of service disruptions in an equitable manner. The results show that improving the restoration prioritization strategy to focus on vulnerable populations is an effective approach, especially during high-intensity events. The results show the capability of the proposed computational model for capturing the dynamic and complex interactions in the nexus of humans, hazards, and infrastructure systems to better integrate human-centric aspects in resilience planning and into assessment of infrastructure systems in disasters.