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

极端事件对社会的影响不仅是对物理基础设施系统造成的直接破坏的函数，也是一个社区能够实施的事件后恢复过程的函数。这一恢复过程取决于金融部门（如私人银行、保险和再保险公司）是否提供投资。因此，这些财政和物质基础设施之间的适当关系是必要的，以便为迅速和有效地恢复物质系统提供财政先决条件。另一方面，这些极端事件也代表了金融网络的压力源，如果金融和物理基础设施之间的关系不能以可持续的、有弹性的方式建立起来，就可能导致崩溃。该项目旨在深入了解金融和物理基础设施之间的耦合关系，以及它们对这些相互关联的系统的恢复能力的影响。这种更好的理解最终将导致对政策制定者以及金融和实体基础设施管理人员的支持，以改进对极端事件的准备和响应。该项目的目标将通过将物理基础设施风险评估和金融网络建模领域的现有研究与大型互联系统分析的计算技术相结合来实现。极端事件下系统工程可靠性分析的模型和方法，重大破坏后系统演化和恢复的马尔可夫链模型，以及运输和供应基础设施的网络模型将用于描述物理基础设施。金融基础设施和这些机构之间以及实体和金融部门之间的契约关系将使用金融网络分析技术进行建模，并结合最近在危机传播和渐进金融崩溃方面的结果。将这些组合成一个用于互联物理和金融基础设施的通用异构网络模型，将在一系列潜在危险情景下进行弹性分析。由于在灾害恢复过程中对大型物理和金融资产系统的响应进行建模的计算挑战，将开发和校准网络扩散分析的替代模型，使用图理论方法进行分析，从而允许对大型系统进行有效分析。最后，利用上述弹性分析，将优化物理-金融网络的图形结构，代表这些实体之间的契约关系，以便在极端灾害事件下最大限度地提高系统的弹性。这种网络优化的结果将有助于政策制定者确定哪种合同结构和政策最能支持和提高互联系统的弹性。总体而言，该项目将从跨学科的角度，结合工程、金融和网络理论，更好地理解极端事件社区灾后恢复过程中危害、脆弱性和融资之间的相互作用。计算上，该框架将允许分析大型系统，包括随机（或不确定）异构网络结构。

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.