Dynamic Interventions for Networked Contagions

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 constant-factor approximations with 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 a wide variety of 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 fairness properties of the optimal interventions.