  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.
