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August 10, 2026Open Access

Algorithmic Convergent Evolution: A Formal Theory and Reproducible Computational Study of Cross-Genre Media Morphology under Shared Ranking Systems

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Authors

SBSatyajit Beura

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Overview

Formal theory reveals structural convergence in media genres under common algorithmic pressures, suggesting new insights into digital content adaptation.

Key Points

  • The study aims to explain how different forms of digital media become structurally similar due to shared algorithmic ranking systems.
  • Introduced the ACE framework to define the interaction of content under shared algorithmic selection.
  • Conducted reproducible stochastic simulations with six media genres to evaluate convergence.
  • Developed a measurable Tri-Axial Digital Phenotype based on Temporal Velocity, Information Density, and Structural Compliance.
  • Shared algorithmic selection produced strong but incomplete structural convergence among media genres.
  • Interface constraints alone showed significantly weaker convergence effects.
  • Exploration and identity-preserving regularisation reduced convergence, while direct copying could strengthen convergence.

Cite This Study

Satyajit Beura (2026) studied this question.

synapsesocial.com/papers/6a797d479c20a9bbd31848bdhttps://doi.org/10.5281/zenodo.21852619
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