Einstein’s special relativity postulates the constancy of light speed c as an axiom but providesno explanation for its origin. We reformulate the universe as a discrete computational networkoperating at the Planck scale, wherein c emerges not as a fundamental constant but as the bandwidth limit of information processing. We demonstrate that the initial state of the universe—aninfinite-dimensional regular simplex—possesses spectral properties (Laplacian eigenvalue λ2 = N) that naturally enforce cosmic uniformity and clock synchronization without invoking inflationaryexpansion. Dimensional reduction from infinite to three dimensions generates an unavoidable informational collision, which we term informational Pauli repulsion, providing the physical driver forboth the Big Bang and accelerated expansion. The deficit angle δ ≈ 7. 36◦inherent in the 600-celltessellation, combined with Gauss’s Theorema Egregium, guarantees spatial closure without externalembedding dimensions, thereby establishing a decisive advantage over string-theoretic frameworksrequiring 10 or 11 dimensions. We derive the Light-Speed Resource Allocation Principle (LRAP), c² = v² + τ², reinterpreting the Lorentz factor as a processing lag ratio rather than a coordinatetransformation coefficient. Black hole singularities are redefined as computational arrest regionswhere 3D rendering fails, leaving 4D data in a frozen state—a paradigm shift that naturally subsumes string theory and the holographic principle as effective descriptions within these arrestedzones. Finally, we prove that local resource allocation alone cannot resolve the global accumulation of geometric frustration, necessitating the hierarchical jamming transitions detailed in Part III. This work bridges the static geometry of Part I (derivation of G and Λ) with the thermodynamichierarchy of Part III (122-digit vacuum energy suppression), completing the dynamical core of theRegular Simplex Hierarchical Gravity (RSHG) framework.
Building similarity graph...
Analyzing shared references across papers
Loading...
Ryuhei Sato
Building similarity graph...
Analyzing shared references across papers
Loading...
Ryuhei Sato (Sat,) studied this question.
www.synapsesocial.com/papers/69897a25f0ec2af6756e8797 — DOI: https://doi.org/10.5281/zenodo.18518663