What question did this study set out to answer?

This work aims to establish a theoretical framework that explains how biological systems maintain low-entropy states through network dynamics and cognitive allocation.

March 25, 2026Open Access

The Demand-State Network Theory of Low-Entropy Maintenance:From Four-Domain Emergence to Chain-Web Focusing (Version 1.0)

Puntos clave

This work aims to establish a theoretical framework that explains how biological systems maintain low-entropy states through network dynamics and cognitive allocation.
Introduced the Demand-State Ontology to define minimal functional units.
Developed a Chain-Web Topology integrating various chain structures into a network.
Created a Two-Stage Selective Focusing model for cognitive resource allocation.
Proposed Meta-ISS Backtracking for monitoring and network reconfiguration.
Formalized models for various psychological conditions as dysregulations of network dynamics.
Established the Demand State as a critical unit in biological systems.
Demonstrated the integration of delegation and parallel chains within network dynamics.
Modelled cognitive resource allocation through a structured two-stage process.
Introduced mechanisms for addressing failures in cognitive focus.
Outlined a comprehensive pathological taxonomy for emotional dysregulations.

Resumen

The Demand-State Network Theory of Low-Entropy Maintenance: From Four-Domain Emergence to Chain-Web Focusing (Version 1.0) This deposit presents the Quad-Domain Two-Track Network (QDTN) theory, a systematic theoretical framework explaining how biological systems maintain low-entropy states through selective activation of demand-state networks under cognitive economy constraints. Core Theoretical Contributions: • Demand-State Ontology: Establishes the Demand State (DS) as the minimal functional unit possessing domain property (physical/physiological/psychical/social), hub level (betweenness centrality), reality index, and state value. • Chain-Web Topology: Integrates delegation chains (vertical) and parallel chains (horizontal) into a dynamic network structure with cross-chain attention allocation algorithms. • Two-Stage Selective Focusing: Models cognitive resource allocation via Parallel Screening Pool (Stage I) and Focused Execution Pool (Stage II) with dynamic threshold regulation and Softmax competition. • Meta-ISS Backtracking: Introduces second-order monitoring mechanisms triggered post-focusing failures to enable network reconfiguration. • Complete Pathological Taxonomy: Provides formal models for anxiety, depression, procrastination, and addiction as dysregulations of focusing and network dynamics. Theoretical Foundations: Built upon Generalized Steady-State Maintenance (GSSM) and extending the Four-Domain Emergence and Dual-Track Transduction framework (V5.0), this work operationalizes value-state pluralism, temporal discounting, and cross-domain downward modulation within a unified mathematical framework. Applications: Cognitive science, psychology, behavioral economics, systems theory, and computational neuroscience. Version: 1.0 (Initial Release) 基于低熵维持的需求态网络理论:从四域涌现到链网聚焦(第一版) 本文建立四域-双轨-链网(QDTN)理论模型,系统阐释生物系统在认知经济性约束下通过选择性激活需求态网络以维持低熵状态的运作机制。核心理论贡献: • 需求态本体论:确立需求态为具备域属(物理/生理/心理/社会)、枢纽级(介数中心性)、真实度与值位的最小功能单元。 • 链网拓扑:将层级链(纵向)与平行链(横向)整合为动态网络结构,包含跨链注意力分配算法。 • 两阶段选择性聚焦:通过广域筛选池(第一阶段)与聚焦执行池(第二阶段)建模认知资源分配,含动态阈值调节与Softmax竞争。 • 元ISS回溯:引入第二阶段聚焦失效后触发的二阶监控机制,实现网络重构。 • 完整病理谱系:为焦虑、抑郁、拖延症和成瘾提供聚焦与网络动力学失调的形式化模型。理论基础: 基于广义稳态维持(GSSM),拓展《四域涌现与双轨转导》(Version 5.0)理论框架,在统一数学框架中形式化值位多元性、时间贴现与跨域向下调制。应用领域: 认知科学、心理学、行为经济学、系统理论与计算神经科学。版本: 1.0(初始发布)

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Cite This Study

Bingqing Xie (Mon,) studied this question.

synapsesocial.com/papers/69c37bd4b34aaaeb1a67e9ea https://doi.org/https://doi.org/10.5281/zenodo.19177530

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