Relationship Between Controllability Scoring and Optimal Experimental Design

TL;DR

This paper reveals a structural link between controllability scores in networked systems and optimal experimental design, showing how classical control measures relate to OED criteria and uncovering long-horizon effects.

Contribution

It establishes a formal connection between controllability scoring and OED, highlighting differences in optimization properties and long-term behavior in network control.

Findings

Controllability scores decompose additively across nodes.

VCS and AECS correspond to D- and A-optimality in OED.

Long-horizon effects cause source-like nodes to be downweighted.

Abstract

Controllability scores provide control-theoretic centrality measures that quantify the relative importance of state nodes in networked dynamical systems. We establish a structural connection between finite-time controllability scoring and approximate optimal experimental design (OED): the finite-time controllability Gramian decomposes additively across nodes, yielding an affine matrix model of the same form as the information-matrix model in OED. This yields a direct correspondence between the volumetric controllability score (VCS) and D-optimality, and between the average energy controllability score (AECS) and A-optimality, implying that the classical D/A invariance gap has a direct analogue in controllability scoring. By contrast, we point out that controllability scoring generically admits a unique optimizer, unlike approximate-OED formulations. Finally, we uncover a long-horizon phenomenon with no OED counterpart: source-like state nodes without a negative self-loop can be increasingly downweighted by AECS as the horizon grows. Two numerical examples corroborate this long-horizon downweighting behavior.