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April 8, 20260 citationsOpen Access

A Universal Coherence Functional Across Complex Systems: Empirical Evidence from Seven Independent Domains

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AFAlexandre Fogel-Reinert

Key Points

  • This research aims to introduce a universal coherence functional to classify complex systems across various domains.
  • Developed a multiscale coherence functional incorporating multiple metrics.
  • Applied the framework to seven independent datasets from diverse fields like cardiology and genomics.
  • Classified system regimes based on coherence and other derived metrics.
  • Achieved high predictive accuracy in cardiac electrophysiology with AUC=0.961.
  • Identified distinct catastrophe types, including dissociation and homogenization.
  • Demonstrated meaningful coherence across various complex systems, indicating a shared framework.

Abstract

We introduce CΨ, a multiscale coherence functional combining Kuramoto synchrony, mean pairwise correlation, and MST compactness into a single fixed operator. Together with cross-scale tension TΨ and internal fragility SΨ, the framework defines a two-dimensional state space classifying complex system regimes. Applied without modification to seven empirical datasets: cardiac electrophysiology (AUC=0.961, MIT-BIH, n=29; AUC=0.713, Chapman, n=13,257), sleep EEG (F=449.2, n=22,233), financial markets (AUC=0.594–0.762 vs VIX=0.418, 25 years), music (F=43.6), genomics (t=95.3), and seismology. Three mechanistically distinct catastrophe types identified: Type A dissociation, Plein Sync homogenization, and Fragmentation. All code and data publicly available.

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Alexandre Fogel-Reinert (2026) studied this question.

synapsesocial.com/papers/69d5f10974eaea4b11a7a8fdhttps://doi.org/10.5281/zenodo.19444147
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