Abstract

This paper develops a rigorous dynamical systems formulation of Self-Referential Ignorance (SRI) within media ecosystems. Classical communication theories treat media as mechanisms for ignorance reduction through information transmission. Contemporary digital environments, however, reveal that information abundance may generate new forms of structural ignorance through recursive feedback. We formalize SRI as a measurable state variable governed by coupled ordinary differential equations, derive stability conditions using Lyapunov analysis, prove that under standard algorithmic incentive structures the SRI-dominant equilibrium is asymptotically stable, and construct a network-theoretic formulation grounded in the DeGroot social influence model. Critically, we differentiate our contribution from prior filter-bubble and echo-chamber literature by providing a dimensionally consistent formal apparatus, explicit parameter estimation pathways, and five falsifiable empirical predictions. We further propose a composite Media-SRI Index operationalizable on existing social-media datasets. The framework integrates cybernetics, information theory, network science, complex adaptive systems, and media studies into a unified account of collective epistemic failure under recursive informational self-validation.