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| Format: | Recurso digital |
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Zenodo
2026
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| Online Access: | https://doi.org/10.5281/zenodo.19390958 |
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Table of Contents:
- <p>Recent advances in monitoring biological dynamical systems have demonstrated that regime transitions and systemic instability can be detected early using reduced-dimensional representations, such as Information-Driven Cognitive Entropy (ICE) state spaces. However, translating these predictive diagnostics into active stabilization strategies remains a fundamental challenge. In this work, we introduce a conservative closed-loop intervention mapping framework. Rather than proposing specific mechanistic control laws or clinical therapies, we formulate a structured control-theoretic approach to construct candidate intervention vectors in a reduced state space. Bydefining “Isostasis” as a reference attractor, we establish a mathematically explicit and experimentally falsifiable pathway to test whether stabilizing input–response mappings exist across physiological systems. The framework does not assume controllability or therapeutic efficacy, but instead provides a minimal structure for evaluating intervention consistency under controlled conditions. If empirically validated, this approach may provide a computational foundation for translating abstract control inputs into physically realizable intervention signals, supporting future biofeedback and controlled stabilization environments.</p>