CRISP Type 2: Interdependencies in Community Resilience (ICoR): A Simulation Framework

Regents Of The University Of Michigan - Ann Arbor, Ann Arbor MI

Investigators

Abstract

Research in natural hazards engineering, and, more broadly, disaster science, seeks to develop a science behind mitigating the effects of natural hazards. However, this research is being done by a multitude of highly specialized disciplines, each dedicated to handling a subset of the overall challenge. There is now an urgent need for researchers across disciplines to collaborate, so that the research done is holistic in nature, so as to find comprehensive, complete solutions to the problems in disaster science. Computation is widely used in disaster-science research across all the disciplines. Thus computational modeling may be used as a common language to link the disciplines. This project's planned integrative, computational platform will serve as this link. Users will be able to connect individual computational models and simulations from multiple disciplines to the platform and simultaneously run them to explore the complex interactions that take place between the different systems of society during and after natural hazard disasters. The ability to seamlessly interface with other models with minimal effort will foster entirely new collaborations between researchers who do not traditionally work together, enabling new studies within the natural hazards engineering and disaster science fields, leading to new contributions. Specifically in this project, new understanding will result of the complex interactions that take place between policy, casualty rates and community resilience. This will help policy makers determine what policy changes are needed in order to significantly influence a community's level of resilience to natural disasters. This project will also contribute to a better-skilled workforce. Students who will work on this project will attain a truly multi-disciplinary education at the intersection of civil engineering, social science and computer science. The unique skills that these students will acquire will allow them to make significant contributions to the future of natural hazards engineering and disaster science and position them as thought leaders in these fields. Thus, this project serves both the NSF's science mission as well as its mission to develop a science-aware workforce. Extreme natural hazards, such as earthquakes and hurricanes, can trigger intricate inter-dependencies between the critical infrastructure systems of society, including the built environment (e.g., buildings and bridges), elements of social organization (e.g., social power and cohesion), and institutional arrangements (e.g., policies, politics, economics, and disaster mitigation). By employing an established set of standards for software interoperability, a simulation framework will be developed to allow researchers from different natural hazards research sub-fields to link their models together to study the effects of infrastructure interdependencies on community resilience. These interdependencies are complex and dynamic; e.g. in a hurricane, each building of the community shelters people while being a potential target of and source for wind-borne missiles. The interdependencies have not been adequately studied in the past because of the broadly interdisciplinary nature of the problem and the lack of tools to study them in an integrated manner. This project will address this issue. In addition, community resilience will be assessed in terms of the interactions that arise between infrastructure robustness, social organization, and policy. Infrastructure robustness directly influences casualty rates. Casualty rates are a direct function of social organization, and while they depend on the policies in effect prior to the event, they also influence future policy. By applying the tools developed in this research to seismic and hurricane scenarios as case studies, interactions between policies (especially as they have evolved over the past decades), cost, casualty rates, and community resilience will be modeled with the objective of seeking new insights into their complex interactions. The studies will address the extent to which policy changes need to be implemented to significantly influence a community's level of resilience. Quantifying these values will allow the most cost-effective changes to be pin-pointed and therefore help to direct future changes in policy targeting resilience. They will also allow the disciplined study of emergence in the complex community resilience problem, an interdisciplinary topic recognized as extremely important to all branches of science.