Network control by a constrained external agent as a continuous optimization problem
This work addresses the policy challenge of governing real-world socioeconomic networks, though it appears incremental as it combines existing methods from different fields.
The authors tackled the problem of optimizing interventions for controlling socioeconomic networks under real-world constraints, integrating deep-learning optimization tools with network science to characterize corporate network vulnerabilities to sensitive takeovers.
Social science studies dealing with control in networks typically resort to heuristics or describing the static control distribution. Optimal policies, however, require interventions that optimize control over a socioeconomic network subject to real-world constraints. We integrate optimisation tools from deep-learning with network science into a framework that is able to optimize such interventions in real-world networks. We demonstrate the framework in the context of corporate control, where it allows to characterize the vulnerability of strategically important corporate networks to sensitive takeovers, an important contemporaneous policy challenge. The framework produces insights that are relevant for governing real-world socioeconomic networks, and opens up new research avenues for improving our understanding and control of such complex systems.