CAREER: Policy-Infrastructure-Community Interdependencies: The Next Frontiers in Dynamic Networks

Vanderbilt University, Nashville TN

Investigators

Abstract

Disasters result from the combined effect of hazards and vulnerability of humans and infrastructures. This Faculty Early Career Development (CAREER) grant seeks to understand the interactions between people and infrastructures in response to hazards. These interactions often lead to unexpected outcomes during and in the aftermath of disasters. The failure of an infrastructure system can directly or indirectly affect other infrastructures and result in crippling effects in socio-economic sectors. In addition, a lack of consideration of social and organizational aspects in infrastructure management can lead to unintended consequences that harm communities. As such, these interactions are uncertain, and they change over time in response to hazards, development, and technology. If we can capture the dynamics of such inter-dependencies, we can proactively intervene to prevent cascading failures, reduce losses, and improve recovery. The objectives of this project are to (i) develop the next generation of dynamic networks to model the uncertain inter-dependencies between policy, infrastructure, and the community, (ii) train future engineering practitioners and researchers through community engagement and human-centered research projects, and (iii) increase public awareness of disaster preparedness through media, arts, and Girl Scouts activities. This project aligns with NSF’s mission to promote the progress of science and to advance the national welfare. Specifically, the outcome of this integrated research and education will transform policy making to ensure the resilience and sustainability of communities under short- and long-term risks. The research approach is founded in statistical network models which provide a probabilistic estimation of future network structures based on partial observation of the structure or historical data. These models provide an approach to encode latent parameters that govern network structures, offering advantages for inference and prediction. This project will develop a new class of statistical network models that accommodates the dynamics of multiple networks. The models will be informed by stakeholders and integrated with empirical approaches and modeling techniques such as decision analysis and Bayesian methods to capture human behavior during disasters. Incorporation of vertex and edge covariates will be accomplished using statistical learning methods for predictive analytics. These new models will be implemented using data-driven scenarios of two cities with increased urbanization and natural hazards to advance knowledge on the diversity of sustainability and resilience assessments in different contexts. Algorithms for network predictive analytics will be developed to quantify sustainable resilience indicators. Immersive educational opportunities through interdisciplinary research projects and international experiences will engage students with communities and enhance their learning experience while contributing to research and human-centered solutions to disasters. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.