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CAREER: Systemic Risk and Strategic Formation in Stochastic Networks

职业：随机网络中的系统性风险和战略形成

基本信息

批准号：
1752326
负责人：
Agostino Capponi
金额：
$ 50万
依托单位：
Columbia University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-03-15 至 2024-02-29
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1752326&HistoricalAwards=false
关键词：
CAREER Systemic Risk Strategic Formation

项目摘要

This Faculty Early Career Development (CAREER) award will contribute to the advancement of national prosperity and economic welfare by studying efficient operations of complex network systems. Current supply chain networks, financial networks and other industries have complex interconnected structures, where the performance of one participant can affect the performance of the entire system. In such cases, systemic risk arises when the failure of one supplier or financial institution cascades through the network, which can be costly to overall welfare. This award supports a fundamental understanding of incentives that manage network vulnerabilities, mitigate systemic risk, and resolve failures, while allowing for profit-maximizing behavior of individual participants. The interdisciplinary nature of this research will create new channels of communications between academics, practitioners, and policy makers, leading to synergistic efficiencies in the design of effective policies. The accompanying educational plan aims to broaden STEM interest in stochastic networks through hands-on activities based on lab curricula and digital libraries, and to provide opportunities for underrepresented communities.This research will build a strategic decision-making framework encompassing a broad class of networks, and will develop techniques to provide timely solutions for systemic risk mitigation. The framework will model the reaction of agents in the network as their joint decision-making process adapts to shocks and failures, and account for agents' facing incomplete information on the state of the network. The proposed research fills an important gap in the network literature, which mostly consider agents that both mechanically follow pre-specified agreements and transparently observe the entire network. The analytical infrastructure leverages and extends state-of-the-art techniques from stochastic analysis, game theory, and risk management. The project will develop mathematical techniques to recover Nash equilibria in constrained, weighted, and directed stochastic networks through the solution of non-linear discontinuous functional fixed-point equations. It will produce numerical algorithms to approximate the sequential equilibria that arise when agents are uncertain about the state of the network, and update their higher-order beliefs on beliefs of other agents and their interactions. The performance assessments of the control policies will be informed by available data.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.

该学院早期职业发展奖将通过研究复杂网络系统的有效运作，为促进国家繁荣和经济福利做出贡献。当前的供应链网络、金融网络等行业具有复杂的互联结构，其中一个参与者的表现会影响整个系统的表现。在这种情况下，当一家供应商或金融机构的倒闭通过网络级联而来时，就会出现系统性风险，这可能会对整体福利造成高昂的代价。该奖项支持对管理网络漏洞、降低系统风险和解决故障，同时允许单个参与者的利润最大化行为的激励措施的基本理解。这项研究的跨学科性质将在学者、实践者和政策制定者之间创造新的沟通渠道，导致有效政策设计的协同效率。随附的教育计划旨在通过基于实验室课程和数字图书馆的实践活动，扩大STEM对随机网络的兴趣，并为代表性不足的社区提供机会。这项研究将建立一个涵盖广泛网络类别的战略决策框架，并将开发技术，为系统性风险缓解提供及时的解决方案。该框架将建模网络中的代理在其联合决策过程适应冲击和故障时的反应，并考虑代理所面临的关于网络状态的不完全信息。拟议的研究填补了网络文献中的一个重要空白，这些文献大多认为代理既机械地遵守预先指定的协议，又透明地观察整个网络。分析基础设施利用并扩展了来自随机分析、博弈论和风险管理的最先进技术。该项目将开发数学技术，通过求解非线性不连续泛函不动点方程，恢复约束、加权和有向随机网络中的纳什均衡。它将产生数值算法来逼近当代理对网络状态不确定时产生的序列均衡，并更新他们对其他代理及其交互的信念的高阶信念。控制政策的绩效评估将由可获得的数据提供信息。这一裁决反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。