Controlling complex policy problems: a multimethodological approach using system dynamics and network controllability

TL;DR

This paper introduces a multimethodological approach combining system dynamics and network controllability to identify leverage points in complex policy models, demonstrated on the World Dynamics model, to improve policy design.

Contribution

It presents a novel integration of system dynamics with network controllability to identify influential variables for policy intervention.

Findings

Controlling 53% of variables can steer the system to any state.

High-ranked variables significantly influence system behavior.

The approach effectively ranks variables by their control importance.

Abstract

Notwithstanding the usefulness of system dynamics in analyzing complex policy problems, policy design is far from straightforward and in many instances trial-and-error driven. To address this challenge, we propose to combine system dynamics with network controllability, an emerging field in network science, to facilitate the detection of effective leverage points in system dynamics models and thus to support the design of influential policies. We illustrate our approach by analyzing a classic system dynamics model: the World Dynamics model. We show that it is enough to control only 53% of the variables to steer the entire system to an arbitrary final state. We further rank all variables according to their importance in controlling the system and we validate our approach by showing that high ranked variables have a significantly larger impact on the system behavior compared to low ranked variables.