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Entropic Resonance Principle: A Unified Informational Framework for Persistence

Submitted:

22 December 2025

Posted:

26 December 2025

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Abstract
This paper introduces the Entropic Resonance Principle (ERP) as an informational framework for investigating how organized systems persist across physical, biological, cognitive, and engineered domains. ERP advances the hypothesis that stability is associated not with resistance to entropy, but with a regulated co-variation between coherence (R) and entropy (H), schematically expressed by an approximate proportionality of the form dR/dH ≈ λ. A specific candidate value for the dimensionless resonance parameter λ, motivated within a minimal self-similar renewal model, is examined as a conjectural organizing quantity rather than as an established constant. This proportionality admits both a flux formulation and a variational formulation, dR – λ dH ≈ 0, which together characterize persistent regimes in an informational state space without modifying underlying microphysical laws.
The paper develops the conceptual and mathematical structure of ERP, examines its ontological motivations, and situates it within existing work on coherence, entropy, and non-equilibrium organization. It further outlines strategies for empirically engaging the framework, including methods for estimating effective coherence–entropy slopes under coarse-graining and for assessing their stability across systems and scales. ERP is presented as the nucleus of a research programme whose empirical adequacy remains an open question. If future studies reveal constrained coherence–entropy relations recurring across domains, ERP may point toward a previously unrecognized structural regularity underlying persistence; if not, it nevertheless provides a precise framework for analyzing how coherence and entropy jointly shape organized behavior.
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Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.
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