Information Inbreeding Law (R): Entropic Collapse in Synthetic Intelligence Ecosystems

Description Information Inbreeding Law (R): Entropic Collapse in Synthetic Intelligence EcosystemsVolume III of the Civilization Physics Series This paper formulates the Information Inbreeding Hypothesis—a degenerative feedback phenomenon in generative AI—and establishes it as the Entropy Law (R) within the broader Civilization Physics framework. It demonstrates how recursive self-training, where AI models are increasingly trained on data produced by other AIs, leads to an entropic collapse of informational diversity, integrity, and epistemic richness. Through analytical modeling and case studies in language and image generation, the paper shows that closed synthetic ecosystems lose informational “Frame” (structure and meaning) exponentially across generations. It proposes that Frame Theory (Frame = Presence × Integrity) serves as the negentropic counter-law: by maintaining human presence (fresh, diverse data and oversight) and informational integrity (truthfulness, ethical curation), AI systems can resist collapse and preserve knowledge coherence. The work integrates empirical evidence on model collapse, synthetic data drift, and recursive degeneration, deriving a quantitative formulation of informational decay (Qₖ = α⁽ᵏ⁾(1–ε)⁽ᵏ⁾ Q₀). It extends the Civilization Physics sequence: Volume I – The Law of Frame: the structural law of societal stability. Volume II – Frame Theory for AI: trust and governance in intelligent systems. Volume III – Entropy Law (R): the law of informational decay and its prevention. Philosophically, the paper argues that information inbreeding in AI mirrors biological and cognitive inbreeding in nature—closed systems without fresh input always drift toward disorder. By embedding Frame principles (Presence × Integrity) into AI design and data governance, civilization can inject “negative entropy,” ensuring that synthetic intelligence remains a force of enlightenment rather than decay. Keywords: Information Inbreeding · Entropy Law (R) · Generative AI · Model Collapse · Synthetic Data Drift · Recursive Training · Frame Theory · Presence · Integrity · Civilization Physics · AI Governance