SDRIS: The Geometric Origin of Gravity - Recovering General Relativity via the Refractive Index of Information Density

General Relativity (GR) describes gravity as the curvature of spacetime, but it treats the metric tensor as a fundamental field without a distinct microphysical substrate. In this paper, we propose a computational origin of gravity within the Static-Dynamic Recursive Information Space (SDRIS) framework. We model the vacuum as a discrete processing grid where mass-energy corresponds to high local information density ($\rho$). We posit that processing high-density nodes incurs a computational cost, leading to a local reduction in the grid's update rate. In the continuum limit, this "processing lag" manifests as the Lapse Function $\alpha$ of the metric. Key Results: Optical Analogy: By defining an effective Refractive Index $n(\mathbf{x}) \equiv 1/\alpha(\mathbf{x})$, we demonstrate that geodesic motion follows from the optical (Fermat) formulation of null geodesics in a static metric, while preserving local Lorentz invariance ($c_{local} = c$). Phenomenology: We recover the weak-field phenomenology—Newtonian attraction, gravitational time dilation, and light bending—strictly from the variation of the optical path caused by the latency field. Micro-Interpretation: We interpret the event horizon not as a geometric singularity, but as a region where the processing cost diverges ($\alpha \to 0$) relative to asymptotic grid time. Ontological Shift: The paper discusses how replacing "Curvature" with "Refractive Index" resolves interpretational issues regarding quantization and singularities, proposing that geometry is merely the hydrodynamic limit of the information grid. Includes: Mathematical derivation of the Optical Metric from Processing Cost. Python Simulation ((105_18125913_Maier-Lutz_SDRIS_Gravity_Simulationon.ipynb) using Hamiltonian Raytracing to visualize gravitational lensing.