Optimal Intervention in Economic Networks using Influence Maximization Methods

TL;DR

This paper studies optimal intervention strategies in economic networks, revealing computational hardness and proposing approximation methods, with practical applications in stress testing and importance sampling.

Contribution

It extends influence maximization techniques to economic networks, proving NP-hardness and developing approximation algorithms for intervention problems.

Findings

Optimal intervention is NP-hard and hard to approximate.

Randomized thresholds enable submodular optimization approaches.

Influence maximization algorithms facilitate efficient stress testing.

Abstract

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.