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| Hauptverfasser: | , |
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| Format: | Recurso digital |
| Sprache: | Englisch |
| Veröffentlicht: |
Zenodo
2025
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| Schlagworte: | |
| Online-Zugang: | https://doi.org/10.5281/zenodo.16941694 |
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Inhaltsangabe:
- <p>This paper introduces the Probabilistic Survivor Model (PSM), a novel conceptual framework for understanding the distribution of prime numbers. In PSM, primes are modeled as survivors of recursive elimination filters, providing an explanation for both the high density of primes in early intervals and the structural growth of prime gaps. Computational simulations up to confirm 95% accuracy in predicting prime density, while Parity Resonance Amplification (PRA) enhances coherence with a zero-resonance ratio of 0.91.</p> <p>The model is validated through statistical tests (Kolmogorov-Smirnov, chi-square) and extended across interdisciplinary domains, including artificial intelligence, evolutionary biology, and cognitive science, where it shows 12–20% improvements in resilience and adaptability.</p> <p>By bridging number theory, philosophy, and complex systems, this work reframes primes as resonant survivors in an infinite elimination lattice, offering new tools for prime prediction and interdisciplinary modeling.</p> <p> </p>