  We consider optimal intervention in the Elliott-Golub-Jackson network model
and show that it can be transformed into an influence maximization problem,
interpreted as the reverse of a default cascade. Our analysis of the optimal
intervention problem extends well-established targeting results to the economic
network setting, which requires additional theoretical steps. We prove several
results about optimal intervention: it is NP-hard and additionally hard to
approximate to a constant factor in polynomial time. In turn, we show that
randomizing failure thresholds leads to a version of the problem which is
monotone submodular, for which existing powerful approximations in polynomial
time can be applied. In addition to optimal intervention, we also show
practical consequences of our analysis to other economic network problems: (1)
it is computationally hard to calculate expected values in the economic
network, and (2) influence maximization algorithms can enable efficient
importance sampling and stress testing of large failure scenarios. We
illustrate our results on a network of firms connected through input-output
linkages inferred from the World Input Output Database.
