Conflict as Phase Transition: A Dynamical Systems Theory of Escalation in Coupled Organizational Networks
Bibliographic record
Abstract
Title: Conflict as Phase Transition: A Dynamical Systems Theory of Escalation in Coupled Organizational Networks Authors: Boris Kriger¹² ¹ Information Physics Institute, Gosport, Hampshire, United Kingdom ² Institute of Integrative and Interdisciplinary Research, Toronto, Canada Description: This paper proposes that conflict escalation is not a property of individuals but a phase transition in coupled networks—occurring when the spectral radius of interpersonal coupling exceeds aggregate decay. Drawing on nonlinear dynamics and network science, we formalize organizational conflict as a transmissible quantity propagating through social structures, mathematically analogous to epidemic dynamics. The framework generates a central prediction: in certain network configurations, escalation becomes structurally inevitable regardless of who initiates. This removes moral personalization from conflict analysis and redirects attention to structural conditions. We derive testable predictions, propose empirical validation comparing network properties against individual personality traits, and specify quantitative falsification criteria. If network coupling predicts escalation better than personality variables, this challenges four decades of individualist organizational psychology and suggests that intervention should target structures rather than persons. The core equation treats conflict activation as governed by four terms: decay, violation response, resource modulation, and network coupling—composing established mechanisms into a system where their interaction produces emergent phase transitions not predictable from any mechanism alone. Keywords: phase transition, network dynamics, conflict escalation, spectral radius, coupled systems, organizational behavior, dynamical systems, nonlinear dynamics, threshold models
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".