جدول المحتويات: :: Library Catalog

محفوظ في:

التفاصيل البيبلوغرافية
المؤلف الرئيسي:	Gheuens, Jacqueline
التنسيق:	Recurso digital
اللغة:	الإنجليزية
منشور في:	Zenodo 2026
الموضوعات:	information geometry Fisher-Rao metric Entropy Dynamics Weyl Geometry Scale-Covariant systems non-Markovian dynamics Recursive systems control theory Hesper framework Counter-sheer stabilization informational manifold entropy shear tensor Weyl-covariant tensor Recursive agency recursion pressure Scale transition dynamics cognitive modeling neurodivergence modeling cognitive load information processing systems adaptive feedback control hierarchical cognition complex adaptive systems informational curvature entropic fields geometric dynamics tensor-based simulation dynamical systems theory dark energy Unification pathway through dark energy
الوصول للمادة أونلاين:	https://doi.org/10.5281/zenodo.19686908
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

جدول المحتويات:

Model 181 (“Hesper”) presents a deterministic, non-Markovian framework for the stabilization of high-entropy dynamical systems using a unified information-geometric and control-theoretic approach. The model formalizes system evolution as trajectories over a differentiable informational manifold equipped with a Fisher–Rao metric, where entropy defines local information density and shear tensors characterize directional instability in disorder flow. The central construct, the Hesper tensor, is defined as a Weyl-covariant, scale-sensitive counter-shear field that dynamically responds to entropy gradients and anisotropic distortions. Acting as an adaptive feedback controller, Hesper introduces a stabilizing flow that counteracts destabilizing entropy-driven dynamics while preserving structural coherence across scales. The framework integrates four principal components: <ol> <li> Information Geometry — representing system states as points on a metric manifold governed by entropy fields. </li> <li> Weyl Geometry — enabling local scale covariance through a dilation field governing metric variation. </li> <li> Recursive Dynamics — incorporating non-Markovian evolution dependent on recursion depth and accumulated informational pressure. </li> <li> Control Theory — formalizing Hesper as a hierarchical feedback mechanism that senses entropy and shear and applies counteracting stabilization. </li> </ol> System evolution follows: <ul> <li> Natural entropy-driven flow, </li> <li> Recursive agency (Model 157), </li> <li> Scale modulation via recursion pressure (Model 179), </li> <li> Geometric stabilization via the Hesper tensor. </li> </ul> A formal stability criterion is defined: ∥H(x)∥≥∥σ(x)∥\|H(x)\| \ge \|\sigma(x)\|∥H(x)∥≥∥σ(x)∥ ensuring that stabilizing counter-shear exceeds destabilizing disorder flow. Violation of this condition results in scale transitions or topology reduction, preserving system integrity under extreme informational load. The framework is simulation-ready, expressed in tensorial form with Weyl-covariant derivatives, and is applicable across domains including: <ul> <li> Cognitive systems and neurodivergent processing regimes, </li> <li> Complex adaptive systems, </li> <li> Information-theoretic physics and entropy-driven dynamics, </li> <li> Hierarchical control architectures. </li> </ul> Model 181 contributes a novel synthesis of information geometry and feedback control, extending prior work on entropy-based cognition and geometric complexity by introducing an explicit, scale-aware stabilization mechanism.

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