Dynamics of Human-AI Collective Knowledge on the Web: A Scalable Model and Insights for Sustainable Growth
Buddhika Nettasinghe, Kang Zhao

TL;DR
This paper presents a scalable dynamical model of human-AI knowledge ecosystems on the web, analyzing growth regimes and policy impacts to inform sustainable development of shared knowledge archives.
Contribution
It introduces an interpretable model capturing co-evolution of human and AI knowledge, revealing how platform policies influence growth regimes and stability.
Findings
Identifies different growth regimes such as healthy growth and oscillations.
Shows how platform policies can shift the system between regimes.
Fits the model to Wikipedia data, revealing changes in human and AI contributions.
Abstract
Humans and large language models (LLMs) now co-produce and co-consume the web's shared knowledge archives. Such human-AI collective knowledge ecosystems contain feedback loops with both benefits (e.g., faster growth, easier learning) and systemic risks (e.g., quality dilution, skill reduction, model collapse). To understand such phenomena, we propose a minimal, interpretable dynamical model of the co-evolution of archive size, archive quality, model (LLM) skill, aggregate human skill, and query volume. The model captures two content inflows (human, LLM) controlled by a gate on LLM-content admissions, two learning pathways for humans (archive study vs. LLM assistance), and two LLM-training modalities (corpus-driven scaling vs. learning from human feedback). Through numerical experiments, we identify different growth regimes (e.g., healthy growth, inverted flow, inverted learning,…
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Taxonomy
TopicsWikis in Education and Collaboration · Advanced Graph Neural Networks · Ethics and Social Impacts of AI
