CRISP Type 1/Collaborative Research: A Computational Approach for Integrated Network Resilience Analysis Under Extreme Events for Financial and Physical Infrastructures

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The effects of extreme events on society are not only a function of the immediate damage induced on the physical infrastructure system, but also of the post-event recovery process that a community is able to implement. This recovery process depends on the availability of investments from the financial sector (e.g. by private banks, insurance, and re-insurance companies). Appropriate relationships between these financial and physical infrastructures are therefore necessary to provide the financial pre-conditions for rapid and efficient restoration of the physical system. On the other hand, these extreme events also represent stressors for the financial network, and can cause collapses if the relationships between financial and physical infrastructures are not established in a sustainable, resilient manner. This project aims at developing a deeper understanding of the relationships coupling the financial and physical infrastructures and their effect on the resilience of these interconnected systems. This improved understanding will ultimately lead to support for policy makers and for financial and physical infrastructure managers in order to improve preparedness for and responsiveness to extreme events.The aims of this project will be accomplished through the integration of existing research in the fields of physical infrastructure risk assessment and financial network modeling with computational techniques for the analysis of large interconnected systems. Models and methods for engineering reliability analysis of systems subjected to extreme events, Markov chain models for system evolution and recovery following major disruption, and network models of transportation and supply infrastructures will be used for describing the physical infrastructures. Financial infrastructures and the contractual relationships among these institutions and between physical and financial sectors will be modeled using financial network analysis techniques, incorporating recent results in distress propagation and progressive financial collapse. Combining these into a common heterogeneous network model for interconnected physical and financial infrastructures, resilience analysis will be conducted under a suite of potential hazard scenarios. Because of the computational challenges associated will modeling the responses of large systems of physical and financial assets during the hazard recovery process, surrogate models for network diffusion analysis, analyzed using graph-theoretical approaches, will be developed and calibrated, allowing for efficient analysis of large-scale systems. Finally, making use of the above resilience analysis, the graphical structure of the physical-financial network, representing the contractual relationships between these entities, will be optimized in order to maximize the resilience of the resulting system under the extreme hazard event. The results of this network optimization will be useful to policy makers in determining which contractual structures and policies best support and improve the resilience of the interconnected system. Overall, this project will result in a better understanding of the interactions of hazard, vulnerability, and financing in the post-event recovery of communities exposed to extreme events from an interdisciplinary perspective combining engineering, finance, and network theory. Computationally, the framework will allow for analysis of large systems, including random (or uncertain) heterogeneous network structures.