Key result
PB-CUDSA algorithm deterministically classifies system stability by measuring relative drift between successive states.
Why the study?
Early failure in biological, physical, and complex systems often occurs despite apparently normal structure or functionality, indicating underlying dynamical instability.
Introduces a unified deterministic algorithm for classifying dynamical stability across various complex systems.
Early failure in biological, physical, and complex systems often occurs despite apparently normal structure or functionality, indicating underlying dynamical instability. This document introduces a unified deterministic algorithm that combines Predictive Bioharmonics (PB) and the Complete Unified Drift Saturation Architecture (CUDSA) into a single framework for classifying dynamical stability versus instability. The PB–CUDSA algorithm evaluates system evolution by measuring relative drift between successive states and saturating drift channels into a coherent stability signal. It operates without probabilities, learning, tuning, optimization, or domain-specific assumptions, and deterministically classifies system states as coherent, marginal, unstable, or collapsing based solely on dynamical structure. The core algorithm is defined independent of application domain; fertility, disease, climate, mechanical systems, and other uses are non-limiting implementations of the same signal.
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Tess Fries (2026) studied this question. The PB-CUDSA algorithm deterministically classifies system states as coherent, marginal, unstable, or collapsing by measuring relative drift between successive states.
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