  We consider optimal intervention in the Elliott-Golub-Jackson network model
\cite{jackson14} and we show that it can be transformed into an influence
maximization-like form, 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
cannot be approximated 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.
