Centralized versus Decentralized AI-Assisted Retrieval: An Experimental Design for Quantifying Error Correlation and Collective Forecasting Performance

This paper presents an experimental design for quantifying how centralized versus decentralized AI-assisted retrieval may influence forecast diversity, error correlation, and collective forecasting performance.

The study is motivated by a well-established body of literature in collective intelligence, forecasting, information cascades, algorithmic monoculture, and the Diversity Prediction Theorem, all of which suggest that correlated errors can reduce the benefits of aggregation. While the theoretical foundations are well known, the magnitude of these effects under contemporary large language model (LLM)-mediated retrieval environments remains insufficiently quantified.

Rather than proposing a new theory, framework, or mathematical principle, this paper introduces a structured experimental design for measuring how shared versus independent AI retrieval systems affect the covariance structure of human forecasting errors. The design compares three information environments: (1) a shared AI retrieval source, (2) independent AI retrieval sources, and (3) a human-only baseline condition.

Primary outcome measures include pairwise error correlation, collective forecasting accuracy (Brier Score), and effective diversity. The proposed methodology is intended as a foundation for future empirical work investigating the trade-off between collective accuracy and diversity in AI-mediated information ecosystems.

The contribution of this paper is methodological and empirical. It provides a transparent, reviewer-resistant experimental architecture for estimating the diversity costs and potential accuracy benefits associated with centralized AI-assisted retrieval systems in collective forecasting tasks.