Robust Intervention in Networks

TL;DR

This paper develops a robust optimization framework for intervention strategies in networks under uncertainty, providing a duality-based characterization of worst-case scenarios and analyzing the trade-offs involved.

Contribution

It introduces a novel duality approach to characterize optimal robust interventions and identifies the worst-case network structure with a rank-1 property, advancing strategic decision-making in uncertain networks.

Findings

Worst-case network has a rank-1 structure.

Robust intervention strategies depend on uncertainty levels.

Trade-offs exist between influence maximization and risk mitigation.

Abstract

In economic settings such as learning, social behavior, and financial contagion, agents interact through interdependent networks. This paper examines how a decision maker (DM) can design an optimal intervention strategy under network uncertainty, modeled as a zero-sum game against an adversarial ``Nature'' that reconfigures the network within an uncertainty set. Using duality, we characterize the DM's unique robust intervention and identify the worst-case network structure, which exhibits a rank-1 property, concentrating risk along the intervention strategy. We analyze the costs of robustness, distinguishing between global and local uncertainty, and examine the role of higher-order uncertainties in shaping intervention outcomes. Our findings highlight key trade-offs between maximizing influence and mitigating uncertainty, offering insights into robust decision-making. This framework has applications in policy design, economic regulation, and strategic interventions in dynamic networks, ensuring their resilience against uncertainty in network structures.