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取决于对有形基础设施系统造成的直接损害，也取决于社区能够实施的灾后恢复进程。这一复苏进程取决于金融部门（如私人银行、保险和再保险公司）是否有投资。因此，这些财政和有形基础设施之间必须建立适当的关系，以便为迅速和有效地恢复有形系统提供财政先决条件。另一方面，这些极端事件也是金融网络的压力源，如果金融和有形基础设施之间的关系不能以可持续、有弹性的方式建立起来，就可能导致崩溃。该项目旨在更深入地了解金融和物质基础设施之间的相互关系及其对这些相互关联的系统的复原力的影响。这一认识的提高将最终导致对决策者以及金融和有形基础设施管理人员的支持，以改善对极端事件的准备和反应能力，这一项目的目标将通过将有形基础设施风险评估和金融网络建模领域的现有研究与分析大型互联系统的计算技术相结合来实现。模型和方法的系统工程可靠性分析受到极端事件，马尔可夫链模型的系统演变和恢复后的重大中断，和网络模型的运输和供应基础设施将用于描述的物理基础设施。金融基础设施和这些机构之间的合同关系，以及物理和金融部门之间的合同关系将使用金融网络分析技术，结合最近的结果，在困境传播和渐进式金融崩溃。将这些结合到一个共同的异构网络模型中，用于互连的物理和金融基础设施，将在一套潜在的灾害情景下进行弹性分析。由于相关的计算挑战将建模的物理和金融资产的大型系统的响应在灾害恢复过程中，代理模型的网络扩散分析，使用图形理论的方法进行分析，将开发和校准，允许大规模系统的有效分析。最后，利用上述弹性分析，物理-金融网络的图形结构，代表这些实体之间的合同关系，将被优化，以最大限度地提高所产生的系统在极端灾害事件下的弹性。这种网络优化的结果将有助于决策者确定哪些合同结构和政策最能支持和提高互联系统的复原力。总体而言，该项目将导致从工程，金融和网络理论相结合的跨学科角度更好地了解灾害，脆弱性和融资的相互作用，在极端事件发生后的社区恢复。在计算上，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.