Governance_Control

This paper formalizes the operational control layer of the Deficit-Fractal Governance (DFG) framework for adaptive multi-agent systems operating near criticality. We introduce a governance state representation, threshold-based intervention control laws, and a minimum-intervention protocol designed to reduce controller activity over time while preserving system stability. A key contribution is the formal identification of the Dependency Trap, in which excessive intervention degrades self-correction capacity and produces post-withdrawal fragility. The framework establishes operational conditions under which governance disengagement remains safe, linking instability measurement, control action, and withdrawal stability within a unified feedback architecture.

Authors

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-02-24
DOI
https://doi.org/10.5281/zenodo.18754316
Primary Topic
Distributed Control Multi-Agent Systems
Type
article
Field-Weighted Citation Impact
0.00
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Governance_Control

Bin Seol
Zenodo (CERN European Organization for Nuclear Research)
Distributed Control Multi-Agent Systems
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Governance_Control

Bin Seol
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Abstract

This paper formalizes the operational control layer of the Deficit-Fractal Governance (DFG) framework for adaptive multi-agent systems operating near criticality. We introduce a governance state representation, threshold-based intervention control laws, and a minimum-intervention protocol designed to reduce controller activity over time while preserving system stability. A key contribution is the formal identification of the Dependency Trap, in which excessive intervention degrades self-correction capacity and produces post-withdrawal fragility. The framework establishes operational conditions under which governance disengagement remains safe, linking instability measurement, control action, and withdrawal stability within a unified feedback architecture.

Zenodo (CERN European Organization for Nuclear Research)
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Distributed Control Multi-Agent Systems
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