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

Abstract

Algorithmic Convergent Evolution (ACE) Algorithmic Convergent Evolution (ACE) is a formal theoretical and computational framework for explaining how initially distinct forms of digital media can become structurally more similar when they repeatedly compete under shared algorithmic ranking systems. News, education, music, comedy, product reviews, political commentary and lifestyle content remain different in meaning and purpose. Yet within recommendation-driven digital platforms, these different media populations may independently develop similar structural characteristics, including faster opening hooks, compressed delivery, dense audiovisual change, persistent captions, platform-native framing and earlier placement of key information. ACE studies this process as a change in digital media morphology, rather than simply similarity in topics or direct imitation between creators. The framework defines Algorithmic Convergent Evolution as a sustained reduction in statistical distance between initially distinct and independently adapting content populations under a partially shared algorithmic selection environment. The paper introduces a measurable Tri-Axial Digital Phenotype based on three primary dimensions: Temporal Velocity, Information Density and Structural Compliance. Content genres are represented as distributions within this phenotype space, allowing their structural evolution and cross-genre convergence to be measured mathematically. A formal selection model examines the interaction between genre-specific requirements and shared platform pressures. The theory is then evaluated through reproducible stochastic simulations involving six initially separated media genres and multiple experimental conditions. The results distinguish convergence caused by shared algorithmic selection from alternative explanations such as random change, genre-local adaptation, interface standardisation and direct copying. The study finds that shared algorithmic selection can produce strong but incomplete structural convergence, while interface constraints alone produce substantially weaker effects. Exploration and identity-preserving regularisation reduce convergence, while direct copying can strengthen it but is not necessary for the central mechanism to occur. ACE therefore differs from memetics, cultural diffusion, platform vernaculars, recommender-system homogenisation and interface standardisation. Its central contribution is a falsifiable population-level theory describing when, why and to what extent independently adapting media genres may converge under common algorithmic visibility pressures. Version 2 substantially expands the original ACE framework with formal definitions, mathematical derivations, null models, robustness experiments, stochastic simulations, evidential criteria, boundary conditions and a programme for future empirical testing. The accompanying reproducibility package contains the complete Python simulations, primary seed-level results, trajectory data, sensitivity analyses, robustness experiments, publication figures, LaTeX manuscript source, bibliography and file manifest. All computational data used in the present study are synthetic; no private platform, creator or user data are used. Research areas: recommender systems, algorithmic ranking, digital media morphology, creator adaptation, platformisation, cultural homogenisation, algorithmic feedback loops, computational social science, media ecology and the attention economy.