A Diagnostic Framework for Structural Regimes in Majority-Minority Field Dynamics

Scalar hierarchy metrics conflate structurally distinct field configurations. Three configurations of a 100-node field — an intact minority cluster, a Lévy-driven transient spike, and a hub-forced bifurcation — each produce H≈0.33, yet differ mechanistically and require different interventions. We introduce a hierarchy vector (C, A, D) — Centralization, Asymmetry, Dominance — as a diagnostic framework for structural regimes in RCR (Recursion-Collapse-Recombination) dynamical systems. The central formal result (Proposition 1) is that no weighted linear scalar of (C, A, D) can replace the vector: every such scalar produces at least 43 structurally conflated timesteps across a 500-step trajectory, while H (the natural scalar) conflates 110. Complementing this, six pre-registered robustness claims hold across seeds, partition methods, parameter perturbations, and adversarial conditions. The C–A anti-correlation (r=−0.677±0.032) and D near-independence (MI<0.14) survive ±10% parameter perturbation and four partition methods (93–97% agreement). The analytical D ceiling (D=hub/(N·|R|_maj+hub)) matches observation within 7% — the result is predictability of the ceiling, not mere smallness of D. The framework is computable on any real-valued field R(t) without presupposing RCR architecture. Practitioners working with any continuous-valued ABM can apply (C, A, D) directly using the three formulas in Section 3, with no dependence on RCR architecture or parameter choices. Across a 500-step trajectory, three structural phases are identified algorithmically from (C, A, D) alone, with phase boundaries stable across 10 seeds. Core results are cross-validated under a second RCR architecture (Model 5) and, in the cross-model validation of Section 8, against Hegselmann–Krause, Deffuant, and noisy bounded confidence — confirming that (C, A, D) correctly identifies distinct structural regimes across architecturally unrelated continuous-opinion dynamics models, including noise-sustained minority persistence absent from scalar measures.

Authors

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2026-05-17
DOI
https://doi.org/10.5281/zenodo.20248741
Primary Topic
Opinion Dynamics and Social Influence
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A Diagnostic Framework for Structural Regimes in Majority-Minority Field Dynamics

Justin D. Gallant
Zenodo (CERN European Organization for Nuclear Research)
Opinion Dynamics and Social Influence
article

A Diagnostic Framework for Structural Regimes in Majority-Minority Field Dynamics

Justin D. Gallant
article en

Abstract

Scalar hierarchy metrics conflate structurally distinct field configurations. Three configurations of a 100-node field — an intact minority cluster, a Lévy-driven transient spike, and a hub-forced bifurcation — each produce H≈0.33, yet differ mechanistically and require different interventions. We introduce a hierarchy vector (C, A, D) — Centralization, Asymmetry, Dominance — as a diagnostic framework for structural regimes in RCR (Recursion-Collapse-Recombination) dynamical systems. The central formal result (Proposition 1) is that no weighted linear scalar of (C, A, D) can replace the vector: every such scalar produces at least 43 structurally conflated timesteps across a 500-step trajectory, while H (the natural scalar) conflates 110. Complementing this, six pre-registered robustness claims hold across seeds, partition methods, parameter perturbations, and adversarial conditions. The C–A anti-correlation (r=−0.677±0.032) and D near-independence (MI<0.14) survive ±10% parameter perturbation and four partition methods (93–97% agreement). The analytical D ceiling (D=hub/(N·|R|_maj+hub)) matches observation within 7% — the result is predictability of the ceiling, not mere smallness of D. The framework is computable on any real-valued field R(t) without presupposing RCR architecture. Practitioners working with any continuous-valued ABM can apply (C, A, D) directly using the three formulas in Section 3, with no dependence on RCR architecture or parameter choices. Across a 500-step trajectory, three structural phases are identified algorithmically from (C, A, D) alone, with phase boundaries stable across 10 seeds. Core results are cross-validated under a second RCR architecture (Model 5) and, in the cross-model validation of Section 8, against Hegselmann–Krause, Deffuant, and noisy bounded confidence — confirming that (C, A, D) correctly identifies distinct structural regimes across architecturally unrelated continuous-opinion dynamics models, including noise-sustained minority persistence absent from scalar measures.

Zenodo (CERN European Organization for Nuclear Research)
Institute for Doctoral Studies in the Visual Arts (US)
Openalex Percentile: Top 5%
Opinion Dynamics and Social Influence
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.