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| Auteur principal: | |
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| Format: | Recurso digital |
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| Publié: |
Zenodo
2026
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| Accès en ligne: | https://doi.org/10.5281/zenodo.18849972 |
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- <p>This paper proposes the Entropy-Minimizing Driven Field (E-Field) as a unified axiomatic framework for describing the common underlying mechanism of physical, biological, cognitive, agentic, and civilizational evolution. The central thesis is that cosmic evolution can be formulated as discrete state updates of informational structures under constraints, biased toward lower-entropy trajectories. Within this framework, energy, entropy, information, computation, observation, and consciousness are not independent primitives, but coordinate projections of a single geometric–informational structure. Based on this view, the paper provides a definition set, minimal formalization, a cross-scale mapping matrix, applied cases, and a cross-disciplinary projection table, together with an expandable system blueprint for future modular validation and development. The framework does not replace existing disciplinary models; rather, it offers a higher-order unifying perspective that compresses cross-domain concepts, reduces theoretical fragmentation, and explains why intelligence and civilization emerge necessarily under thermodynamic and computational constraints. Keywords: entropy-minimizing driven field; informational structure; phase locking; unified axiom; civilizational evolution; computational thermodynamics; consciousness interface;low entropy field, entropy ,information, universe ,evolution, complex ,system evolution, self organization, emergence ,theory, information ,structure, energy and entropy, system convergence, stable structures, attractor theory, low entropy path, system stability, structural evolution, unified theory, scientific framework, cognition and information, consciousness and information, agent evolution, civilization evolution, system coordination, network evolution, collective behavior, meme propagation, industrial clusters, organizational systems, interdisciplinary theory, complex networks, system dynamics, feedback loops, convergence and divergence, stability and chaos, information compression, data and structure, model unification, paradigm shift, cognitive science basics, AI human interaction, artificial intelligence principles, machine learning basics, deep learning explanation, algorithms and structure, optimization paths, constraint systems, system modeling, multi scale analysis, structural similarity, isomorphism homomorphism, coherence and phase locking, clustering mechanisms, adaptive systems, complexity science, system optimization, energy flow, information flow, structural convergence, evolutionary pathways, future science,framework</p>