Powered by OpenAIRE graph
Found an issue? Give us feedback
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/ ZENODOarrow_drop_down
image/svg+xml art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos Open Access logo, converted into svg, designed by PLoS. This version with transparent background. http://commons.wikimedia.org/wiki/File:Open_Access_logo_PLoS_white.svg art designer at PLoS, modified by Wikipedia users Nina, Beao, JakobVoss, and AnonMoos http://www.plos.org/
ZENODO
Other literature type . 2026
License: CC BY
Data sources: ZENODO
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
ZENODO
Research . 2026
License: CC BY
Data sources: Datacite
versions View all 2 versions
addClaim

The Intervention Paradox: Why Direct Governance Near Criticality Fails — A Coarse-Grained Potential Theory

Authors: Seol, Bin;

The Intervention Paradox: Why Direct Governance Near Criticality Fails — A Coarse-Grained Potential Theory

Abstract

Well-intentioned interventions frequently worsen the crises they seek to prevent: financial rescues can amplify systemic risk; organizational restructurings trigger departure cascades; ecological management interventions push ecosystems past tipping points. This empirical regularity — observed across management science, ecology, and financial economics under names such as the change paradox, intervention-induced tipping, and signaling-induced contagion — has lacked a unified structural explanation. This paper provides that unifying mechanism within a coarse-grained potential model. Systems operating near self-tuned criticality (Paper I of the DFG Trilogy) reside in shallow potential wells V(X) = −AX² + B_pot·X⁴ whose barrier height vanishes at the critical manifold: ΔV ~ |ξ| → 0 as ξ → 0, since A ~ |ξ|^{1/2}. Direct interventions above a magnitude threshold act as energy injections enabling Kramers escape over the barrier — triggering the very cascade they aimed to prevent. Theorem 1 (Intervention Paradox): Above D_threshold, the probability of collapse increases monotonically with intervention magnitude within the model. The stochastic operational threshold D_threshold = 2·sqrt(A·B_pot)·G(X_c)·σ_η shrinks as D_threshold ~ |ξ|^{1/4} → 0 as the system approaches criticality — meaning the safe intervention window collapses precisely when crises appear most imminent. Two distinct threshold formulas are derived from the same Kramers escape framework under different noise-scaling assumptions: D_geom = A²/(4B_pot·|X_c|) is the barrier-elimination criterion in the ungated zero-noise limit (G = 1); D_threshold is the stochastic operational threshold incorporating noise amplification through the logistic gating function G(X_c) = 1/2. D_threshold is interpreted as a phenomenological scaling ansatz — a rigorous Fokker-Planck derivation under non-conservative forcing is identified as future work. Theorem 2 (Buffer-First Principle): Buffer depth increases are monotonically stabilizing within the coarse-grained model class for all Δb > 0: d(ΔV)/d(Δb) > 0. Buffer investment unconditionally deepens the potential well and expands the safe intervention window, making it categorically superior to direct state forcing near criticality. Recommended Three-Phase Governance Protocol (Corollary of Theorems 1–2): (1) maximize buffer depth b first, (2) apply bounded direct intervention ||U|| ≤ D_threshold, (3) transition to structural steering if ξ persists. The rationale follows directly from Theorems 1 and 2; the protocol is not formally proved optimal. Key scaling relations near criticality (A ~ |ξ|^{1/2}, mean-field): Barrier height: ΔV ~ |ξ| → 0 Well minimum: X_c ~ |ξ|^{1/4} → 0 Geometric threshold: D_geom ~ |ξ|^{3/4} → 0 Stochastic threshold: D_threshold ~ |ξ|^{1/4} → 0 (D_threshold > σ_η². Near criticality (ΔV → 0), the system enters a crossover regime where classical Kramers asymptotics break down; the analysis is therefore a scaling argument indicating that intervention-induced barrier reduction accelerates transitions, not an exact escape-rate formula. The qualitative conclusion D_threshold → 0 as ξ → 0 is robust to this limitation. The effective potential V_eff(X) = V(X) − G(X)·U·X is a first-order approximation near the well minimum; U is not a conservative force. The Buffer-First unconditional claim holds within the model class only. The order parameter X is related to the critical coordinate ξ of Paper I through a smooth reparameterization X = f(ξ) with f(0) = 0, representing the normal-form reduction of the coordination dynamics near the bifurcation point and formalizing the connection between the GGT critical coordinate and the double-well potential structure. This paper is Series Paper III of the Deficit-Fractal Governance (DFG) Framework. Together with Paper I (self-tuned criticality) and Paper II (cascade universality), it completes the trilogy: critical coordination systems simultaneously generate universal power-law cascades, restrict safe intervention magnitude to zero at criticality, and require logarithmic hierarchy depth — three structural consequences of the same coordination balance Γ ≈ Γ_c.

Keywords

structural governance, near-criticality, double-well potential, Kramers escape, gating function, tipping point, potential well geometry, safe intervention threshold, critical systems, intervention paradox, cascade prevention, DFG framework, buffer-first principle, stochastic escape, governance failure

  • BIP!
    Impact byBIP!
    selected citations
    These citations are derived from selected sources.
    This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    0
    popularity
    This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
    Average
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
    Average
    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Average
Powered by OpenAIRE graph
Found an issue? Give us feedback
selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average