The Compressibility Selection Principle (Integrated Final)

This work presents the Compressibility Selection Principle: physical laws can be modeled as if selected to maximize long-term predictive compressibility under energetic constraint. We integrate simulation results across regimes of low, moderate, and high noise, and show that apparent failures of discovery arise from mis-specified evaluation criteria rather than learning failure. Scientific learning is reframed as movement along a compressibility gradient, not recovery of uniquely true equations. The framework is mathematically coherent, simulation-supported, and compatible with established physics.