GovernanceControl

This paper formalizes the operational control layer of the Deficit-Fractal Governance (DFG) framework for adaptive multi-agent systems operating near criticality. We define a governance state vector, construct a multi-resolution state estimator, and derive threshold-based intervention control laws under a minimum-intervention principle: the controller is designed to reduce its own activity over time. A central contribution is the Dependency Trap mechanism, in which well-intentioned intervention structurally degrades self-correction capacity, producing post-withdrawal fragility invisible to standard health metrics. A minimal dynamical model confirms the mechanism quantitatively: the analytically derived critical threshold SCC* = d/βᵣ is validated by simulation, and systems trained under excessive intervention produce 150+ collapses after governance withdrawal compared to zero under minimal intervention. Version 2. 0 introduces maturation-aware control by coupling intervention dynamics to closure depth Lc (t) from the companion Dynamic Closure theory. A regression shock experiment demonstrates that this coupling is qualitatively distinct from time-based decay: under transient perturbation, maturation-aware governance provides automatic state-contingent re-engagement (p < 0. 000001 vs. monotonic withdrawal), a capability no time-scheduled policy can replicate.