Dynamic Interventions for Networked Contagions

TL;DR

This paper develops a dynamic intervention framework for networked contagions, optimizing resource allocation to minimize defaults and shocks in financial and other interconnected systems using Markov Decision Processes.

Contribution

It introduces a novel dynamic intervention model based on the Eisenberg-Noe framework, with efficient algorithms for continuous and discrete interventions, applicable across various networked systems.

Findings

Optimal policies effectively reduce defaults and shocks.

Node centrality influences intervention strength.

Interventions exhibit fairness properties.

Abstract

We study the problem of designing dynamic intervention policies for minimizing networked defaults in financial networks. Formally, we consider a dynamic version of the celebrated Eisenberg-Noe model of financial network liabilities and use this to study the design of external intervention policies. Our controller has a fixed resource budget in each round and can use this to minimize the effect of demand/supply shocks in the network. We formulate the optimal intervention problem as a Markov Decision Process and show how we can leverage the problem structure to efficiently compute optimal intervention policies with continuous interventions and provide approximation algorithms for discrete interventions. Going beyond financial networks, we argue that our model captures dynamic network intervention in a much broader class of dynamic demand/supply settings with networked inter-dependencies. To demonstrate this, we apply our intervention algorithms to various application domains, including ridesharing, online transaction platforms, and financial networks with agent mobility. In each case, we study the relationship between node centrality and intervention strength, as well as the fairness properties of the optimal interventions.