Vital Network Science: A Control-Theoretic Framework for Information Ecosystem Governance

This paper presents Vital Network Science (VNS), a control-theoretic framework for information ecosystem governance that addresses cognitive overload, epistemic dysfunction, and AI training data contamination. We formalize vitality (ψ) as a measurable index across seven dimensions: coherence, resilience, civility, epistemic quality, diversity, agency, and affective balance. Drawing on feedback control principles, we propose a vitality governor that monitors information velocity (dφ/dt) and applies proportional temporal damping to prevent harmful viral spikes while preserving access and user agency. Critically, we emphasize that platforms already actively manage information flow (optimizing for engagement), and VNS proposes changing the optimization target to coherence and health rather than virality. The framework includes technical architecture, multi-stakeholder governance principles, phased validation strategy, and policy implications for both public health and AI safety. This is a companion paper to "Training Data Velocity Bias" which analyzes how virality optimization in training data may contribute to LLM hallucination.