Resource Allocation in a Financial Contagion Environment (via Zoom)

Abstract: The pandemic has spread uncertainty among financial entities that experience income shocks. A policy framework is stimulus checks, i.e. cash injections into the financial system so that consumption is stimulated, and contagion is averted. A cardinal question faced by policymakers is: Who gets the subsidies? A common pattern in these scenarios is that when somebody's income is below a certain threshold or satisfies some criterion then the household is entitled to a stimulus check of fixed value which can depend on the entity's features. However, such rules are limited by ignoring contagion effects through the financial network. If a business defaults that may translate to job loss for the employees who in their turn may not be able to pay their own debts, potentially creating a sequence of defaults. Our work studies resource allocations under financial contagion, through the lens of approximation algorithms and fairness. In closing, we test our methods with real-world and semi-artificial data and compare them to heuristics.
In the sequel, we generalize the static framework to the dynamic setting in which liabilities that are not paid at a certain round accumulate to the next round, as an MDP. We study fractional and (approximate) discrete bailout allocation scenarios. Our framework is vastly applicable to a variety of domains: Specifically, in any problem that corresponds to a supply and demand network that evolves over time for which the nodes that cannot meet their demand have to split it proportionally and the planner wants to allocate resources can be captured by this framework. Applications beyond financial transaction networks include ridesharing, high-performance computing, ad placement, influence maximization, financial transaction networks on the Web, etc. Finally, we extend the notions of fairness to the dynamic setting.
This talk includes joint work with Jon Kleinberg and Sid Banerjee.
Bio: Marios is a 3rd-year Ph.D. Candidate in the Computer Science Department at Cornell University advised by Prof. Jon Kleinberg. His interests span theoretical and practical aspects of information networks. So far, his work has evolved around statistical network models, hypergraphs, and financial contagion. Marios has also worked at Twitter Cortex where he did research in scalable graph machine learning methods.