CAREER: Systemic Risk and Strategic Formation in Stochastic Networks

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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.