A Dynamic Model for Systemic Risk in Networks Subject to Contagion

受传染网络系统性风险的动态模型

• 批准号: 1462495
• 负责人: David Yao
• 金额: $ 30.29万
• 依托单位: Columbia University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-07-01 至 2018-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1462495&HistoricalAwards=false
• 关键词: Dynamic; Model; Systemic; Risk; Networks

The contagion effect in a network may arise from the collective activities of the parties involved in the network as a whole, as well as from the interactions among parties who are directly connected to each other. These two mutually enhancing channels of interconnectedness can lead to cascading network failures and catastrophe with serious societal consequences. Examples of networked systems subject to contagion include traffic, telecommunication and financial networks. In some environments, the network-level effect can spread the contagion must faster and wider than the diffusion through neighboring nodes. Instances of contagion may cause functional collapse of such networks. This project will seek to mitigate the damaging effects of contagion via the use of a dynamic model to discern the effects of contagion from these two channels. This research is expected to lead to new decision tools that may be used by policy makers and risk management professionals to address what-if questions, to design stress tests for individual decision-making units, to monitor critical elements in a network, and to set up firewalls to prevent or limit cascading failures.Contagion dynamics in interconnected networks will be modeled as a high dimensional dynamic complementarity problem, also known as Skorohod problem. An algorithm that solves the Skorohod problem will generate all possible failure times over any given horizon, along with the evolution dynamics of the network state. These results will inform the development of new risk measures for clustering of failures and contagion concentration. Additional research objectives are: (1) conducting sensitivity analysis for the failure-clustering and contagion measures, formulating a robust optimization model to construct the network configuration and calibrating the model with data; (2) incorporating stochastic shocks to state variables to study the state- and time-dependent dynamics of failures and contagion; and (3) investigating resource control schemes to mitigate contagion, along with utility-maximizing objectives and fairness constraints. The project will extend the research frontier of stochastic networks to generate new approaches to modeling and analyzing systemic risk in networks, and new knowledge in understanding the contagion dynamics of failures.

网络中的传染效应可能来自于整个网络中参与方的集体活动，也可能来自于直接相连的各方之间的互动。这两个相互增强的互联渠道可能会导致级联网络故障和灾难，从而造成严重的社会后果。容易受到传染的网络系统的例子包括交通、电信和金融网络。 在某些环境中，网络级效应可以比通过相邻节点的扩散更快、更广泛地传播传染病。 传染的情况可能会导致此类网络的功能崩溃。该项目将寻求通过使用动态模型来辨别这两个渠道的传染影响，从而减轻传染的破坏性影响。这项研究预计将带来新的决策工具，政策制定者和风险管理专业人员可以使用这些工具来解决假设问题，为各个决策单元设计压力测试，监控网络中的关键要素，并设置建立防火墙以防止或限制级联故障。互连网络中的传染动态将被建模为高维动态互补问题，也称为 Skorohod 问题。解决 Skorohod 问题的算法将生成任何给定范围内所有可能的故障时间，以及网络状态的演化动态。这些结果将为针对故障集群和传染集中的新风险措施的开发提供信息。其他研究目标是：（1）对故障聚类和传染措施进行敏感性分析，制定稳健的优化模型来构建网络配置并用数据校准模型； (2) 将随机冲击纳入状态变量，以研究故障和蔓延的状态和时间相关动态； (3) 研究资源控制计划以减轻传染，以及效用最大化目标和公平约束。该项目将扩展随机网络的研究前沿，以产生建模和分析网络系统风险的新方法，以及理解故障传染动力学的新知识。