The SHRF Instrument Panel: A Dimensionless Constraint Framework for Diagnosing Compression and Expansion in Coupled Human Systems

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Authors: Neil Clive Tuckwell
Cited by: 0

Abstract

The SHRF Instrument Panel is a constraint-based diagnostic framework designed to detect and classify compression and expansion states in coupled human, institutional, and informational systems. The model reduces complex behavioural dynamics to five dimensionless control ratios derived from classical resonance and control theory: Quality factor (Q), Coupling-to-Loss ratio (C), Signal-to-Noise ratio (SNR), Load ratio (L), and Phase Margin (PM).Rather than proposing new ontological structures or ideological interpretations, the framework functions as an operational measurement layer. It interprets polarisation, escalation, and binary collapse as emergent consequences of bandwidth overload and stability margin reduction rather than moral or ideological failure.The instrument is scale-agnostic and adaptable across domains including conversational analysis, institutional decision environments, media ecosystems, and networked communication systems. Its validity depends on empirical calibration of defined proxies and predictive testing under measurable perturbation.The SHRF Instrument Panel is intentionally minimal. It prioritises falsifiability, dimensional reduction, and constraint consistency over descriptive expansion. It is not a theory of consciousness, cosmology, or morality. It is a diagnostic architectures. Keywords Resonance dynamics; Constraint systems; Control theory; Dimensionless ratios; Load ratio; Signal-to-noise; Phase margin; Coupling; System stability; Polarisation dynamics; Bandwidth limitation; Feedback systems; SHRF; Cognitive load; Institutional stability; Nonlinear dynamics

Fields of Study & Subjects

Complex Systems and Dynamics Complex Systems and Decision Making Chaos Complexity and Education

Publication details

Journal / source
Zenodo (CERN European Organization for Nuclear Research)
Publication date
11 February 2026
Language
EN
Document type
Preprint