Multi-Agent Based Stochastic Dynamical Model to Measure Community Resilience

Abstract

Emergency services and utilities need appropriate planning tools to analyze and improve infrastructure and community resilience to disasters. Recognized as a key metric of community resilience is the social well-being of a community during a disaster, which is made up of mental and physical social health.Other factors influencing community resilience directly or indirectly are emotional health, emergency services, and the availability of critical infrastructures services, such as food, agriculture, water, transportation, electric power, and communications system. It turns out that in computational social science literature dealing with community resilience, the role of these critical infrastructures along with some important social characteristics is not considered. To address these weaknesses, we develop a new multi-agent based stochastic dynamical model, standardized by overview, design concepts, details, and decision (ODD+D) protocol and derived from neuro-science, psychological and social sciences, to measure community resilience in terms of mental and physical well-being. Using this model, we analyze the micro-macro level dependence between the emergency services and power systems and social characteristics such as fear, risk perception, informationseeking behaviour, cooperation, flexibility, empathy, and experience, in an artificial society. Furthermore, we simulate this model in two case studies and show that a high level of flexibility, experience, and cooperation enhances community resilience. Implications for both theory and practice are discussed.

Measuring Community Resilience During the COVID-19 Based on Community Wellbeing and Resource Distribution

“…Community resilience (CR) refers to the ability of a social system to absorb the impact of the stress and cope with threats and adapt to post-event situations by reorganizing, changing, or learning to cope with the threat from the disasters [15,16] . This definition is well aligned with the general concept of system resilience in terms of its fault tolerance (i.e., functioning under threats or errors), adaptability (i.e., adapting to disruptions), and recoverability (i.e., recovering quickly from the disrupted situations) [14] . Community resilience has been measured based on various types of metrics [17][18][19] .…”

“…We measure the community functionality in terms of community wellbeing and resource distribution. Figure 2 represents the community functionality, CF(t), with time t. We define community resilience based on the concept of system resilience [14] , consisting of absorption (i.e., fault tolerance), adaptability, and recoverability. We interpret the time until a community does not function as the time period for absorption, namely TFA (i.e., time from t 0 to t 1 ).…”

Computational social science in smart power systems: Reliability, resilience, and restoration

“…Plus, the term cyber refers to the interdependent network of information systems infrastructures, which includes the Internet, social media, telecommunications networks, computer systems, and embedded processors and controllers. In the literature, social scientists and researchers in computational social science advocate the use of an artificial society to model social systems' collective behaviour, the interaction between agents and human response [15][16][17]. The artificial society can be used for virtual experiments via agent-based modelling and simulations, which is an appropriate and promising method widely accepted by researchers addressing problems in sociology, complex systems, emergence, and evolutionary programming [18].…”