This repository presents Structural Differentiation Information (SDI) v1. 3, a minimal and directly testable framework in which information is defined as the stabilized outcome of irreversible structural differentiation. In contrast to conventional views that treat information as symbolic storage, statistical correlation, or entropy-based abstraction, SDI describes information as a structural and generative phenomenon. The framework is defined by a simple nonlinear relation: Iₛtruct (D) = -log (1 - ηD) where D represents structural differentiation and η is a fixation coefficient. This formulation implies nonlinear amplification: information grows increasingly rapidly as structural differentiation accumulates. A corresponding dynamical equation connects structural evolution to information generation: dIₛtruct/ds = η / (1 - ηD (s) ) * (dD/ds) For generic growth laws such as D (s) = 1 - exp (-λs), information emerges through irreversible accumulation, producing characteristic nonlinear behavior. A key feature of SDI is its cross-domain consistency. The same structural mechanism appears across: - Artificial intelligence (training stability and representation persistence) - Biological systems (synaptic fixation and memory encoding) - Physical processes (decoherence and record formation) All converge to a common observable structure: D → Iₛtruct → measurable persistence Furthermore, SDI integrates naturally into Structural Differentiation Cosmology (SDC), where: C → D → Iₛtruct This establishes SDI as the informational sector of structural differentiation, linking local persistence to global structural evolution. This repository contains: - Conceptual figure set (4 figures) - Reproducible Python generation code- Accompanying PDF document The framework is minimal, structurally grounded, and directly testable, providing a unified interpretation of information across multiple domains. This provides a clear and directly testable pathway toward experimental validation.
Koji Okino (Sun,) studied this question.