Cyber–Physical–Social Model of Community Resilience by Considering Critical Infrastructure Interdependencies

Abstract

Each year, several disasters occur, resulting in enormous human, infrastructural, and economic losses. To minimize losses and ensure an adequate emergency response, it is vital to prepare the community for greater shock absorption and recovery after an occurrence. This raises the concept of community resilience and also demands appropriate metrics and prediction models for improved preparedness and adaptability. While a community is impacted in three main ways during a disaster-namely social, physical, and cyber-there are currently no tools to model their interrelationship. Thus, this paper presents a multi-agent cyber-physical-social model of community resilience, taking into account the interconnection of power systems, emergency services, social communities, and cyberspace. To validate the model, we used data on two hurricanes (Irma and Harvey) collected from Twitter, GoogleTrends, FEMA, power utilities, CNN, and Snopes (a fact-checking organization). We also describe methods for quantifying social metrics such as the level of anxiety, risk perception, and cooperation using social sensing, natural language processing, and text mining tools. We examine the suggested paradigm through three different case studies: 1) hurricanes Irma and Harvey; 2) a group of nine agents; and 3) a society comprised of six distinct communities. According to the results, cooperation can positively change individual behavior. Relationships within a community are so crucial that a smaller population with greater empathy may be more resilient. Similar dynamic changes in social characteristics occur when two empathetic communities share resources after a disaster.