Ezra’s Round Table / Systems Seminar: Andreea Minca (Cornell)
Cascading Risks and Sensitivity in Economic Networks
Agents in complex economic networks face intrinsic uncertainty regarding global network structure. As real networks are large and complex, even small network uncertainties can lead to huge uncertainties about the market values and risks (i.e., high parameter sensitivity) of firms and organizations in the face of network cascades. This raises the question of how organizations, regulators, or investors can utilize network models to assess the risks at the organization level despite imperfect information. We derive a solution to this challenge and a new unifying perspective. We apply perturbation theory based on conditioning to quantify the sensitivity of node values to uncertainty in network parameters in the presence of nonlinear cascade effects.
Our main result is a new efficient algorithm to bound uncertainty in node values in models with nonlinear cascade effects given uncertainty ranges in parameters. We illustrate the application of this algorithm on a real-world network. Our applied case study demonstrates the efficiency of our methods in uncovering valuable information into the risks faced by numerous nodes in the network, even in the presence of significant sensitivity challenges posed by network cascade models.
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Bio:
Andreea Minca is a professor in the School of Operations Research and Information Engineering at Cornell University. She holds degrees from Sorbonne University (Ph.D. in applied mathematics) and Ecole Polytechnique (Diplome de l’Ecole Polytechnique). In recognition of “her fundamental research contributions to the understanding of financial instability, quantifying and managing systemic risk, and the control of interbank contagion”, Andreea received the 2016 SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize. Andreea is also a recipient of the NSF CAREER Award (2017), a Research Fellow of the Global Association of Risk Professionals (GARP) (2014), and an AXA Research Fund Awardee (2020)
