Self-Referential Ignorance as a Universal Law of Adaptive Systems: A Universal Balance-Feedback Framework Grounding with Formal Benchmarks

Self-Referential Ignorance (SRI) is the systematic gap between a system's actual uncertainty and its estimated uncertainty about its own knowledge boundaries. While existing machine learning literature addresses calibration error, epistemic uncertainty, and metacognitive accuracy as separate constructs, none provides a unified mechanistic account of why adaptive systems — human, institutional, or artificial — systematically fail to model their own ignorance. This paper grounds SRI within the Universal Balance-Feedback Framework (UBFF), demonstrating that SRI is not merely a statistical artifact but a structural consequence of the Universal Feedback Loop Mechanism (Law III) operating under finite self-modeling capacity. We show that SRI is mathematically distinct from calibration error by introducing a higher-order meta-modeling error formulation. We propose the SRI Index (SRII) as a computable metric, derive the Adaptive Failure Theorem and Feedback Stability Theorem from UBFF first principles, and present SRI-Bench: a multi-domain benchmark spanning human forecasters, institutions, and AI systems. The strongest falsifiable prediction of the framework — that SRII predicts future adaptive failure better than accuracy-based metrics alone — is operationalized in a synthetic civilization simulation. These contributions position SRI as a genuinely new scientific construct with practical implications for AI robustness, institutional risk management, and human cognitive science.