2026/04/07 by Bernd von Mallinckrodt · 1 voice
Environmental Science · Physics and Astronomy · #Ecosystem dynamics and resilience #Chaos control and synchronization #Complex Systems and Dynamics
paper · doi:10.5281/zenodo.19449071
openalex publication_date 2026/04/07 · openalex created_date 2026/04/08 · openalex updated_date 2026/07/01
This conceptual interview introduces the Compression–Response Transition Index (CRTI) as a mechanism-specific diagnostic framework for early warning signals in complex systems. In contrast to classical indicators such as variance and autocorrelation, the CRTI framework focuses on structural compression, defined as the concentration of variance into fewer effective dimensions of a system’s state space. This phenomenon is quantified via Φ, derived from the eigenvalue spectrum of the covariance matrix and interpretable as an effective rank measure. CRTI combines structural capacity (Φ) with a dynamical response proxy (R) into the composite index T = R / Φ. The framework is designed to detect scenarios in which systems lose internal degrees of freedom prior to observable changes in amplitude-based indicators. The interview format is used intentionally to clarify core concepts, address common criticisms (including reparameterization, identifiability, and noise assumptions), and define the domain of validity, particularly for fold-type critical transitions under constrained noise conditions. Rather than proposing a universal law, CRTI is positioned as a calibrated measurement instrument that complements existing early warning approaches by capturing structural degradation that may remain invisible to traditional metrics. This document serves as a conceptual bridge between formal mathematical exposition and applied interpretation, providing an accessible entry point for interdisciplinary evaluation and future empirical validation. CRTI, early warning signals, structural compression, effective rank, spectral entropy, covariance structure, fold bifurcation, critical transitions, system collapse, resilience, complex systems, dimensionality reduction