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May 13, 20260 citationsOpen Access

Fractal Metascience Paradigm as Dynamic Visualization: Process Ontology, Recursive Systems, and AI-Simulated Superposition

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AAAbdurashid Abdukarimov

Key Points

  • This research aims to redefine visualization in scientific publishing as a dynamic and participatory process, challenging static representation norms.
  • Formalization of the Fractal Metascience Paradigm
  • Integration of fractal structures across mathematics, physics, and philosophy
  • Utilization of artificial intelligence for simulating superposition
  • Demonstrated that conventional scientific divisions can be unified through fractal ontology.
  • Showed that dynamic visualization enhances the understanding of complex systems.
  • Provided a framework for operational superposition using AI-simulated dynamics.

Abstract

This paper formalizes the Fractal Metascience Paradigm (FMP) as a corrective to the limitations of static representations in scientific publishing, redefining image, visualization, and representation as processual, recursive, and dynamically observable phenomena. Within FMP, an image is not a picture but an unfolding system; visualization is not depiction but participation in dynamics. We demonstrate that what is conventionally fragmented into mathematics, physics, computer science, and philosophy constitutes a single recursive ontology describable through fractal structures and operational superposition. Furthermore, we show how artificial intelligence systems, when used reflexively, enable the experimental simulation of this superposition regime, providing a unified framework for integrating theory, method, and dynamic visualization.

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Cite This Study

Abdurashid Abdukarimov (2026) studied this question.

synapsesocial.com/papers/6a0414f679e20c90b4444d45https://doi.org/10.5281/zenodo.20116239
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  3. 3The Fragmentation Crisis and the Ontology of Continuity: From Linear Data Optimization to the Stewardship of Epistemic Conditions2026 · 2 citations
  4. 4The Fragmentation Crisis and the Ontology of Continuity: From Linear Data Optimization to the Stewardship of Epistemic Conditions2026
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