Print Email Facebook Twitter Averaging quantiles, variance shrinkage, and overconfidence Title Averaging quantiles, variance shrinkage, and overconfidence Author Cooke, R.M. (TU Delft Applied Probability) Date 2022 Abstract Averaging quantiles as a way of combining experts' judgments is studied both mathematically and empirically. Quantile averaging is equivalent to taking the harmonic mean of densities evaluated at quantile points. A variance shrinkage law is established between equal and harmonic weighting. Data from 49 post-2006 studies are extended to include harmonic weighting in addition to equal and performance-based weighting. It emerges that harmonic weighting has the highest average information and degraded statistical accuracy. The hypothesis that the quantile average is statistically accurate would be rejected at the 5% level in 28 studies and at the 0.1% level in 15 studies. For performance weighting, these numbers are 3 and 1, for equal weighting 2 and 1. Subject averaging distributionsaveraging quantilescombing expertsexpert judgmentover-confidencevariance shrinkage To reference this document use: http://resolver.tudelft.nl/uuid:4ea9c97d-474c-49d9-bfb3-3275e82b3724 DOI https://doi.org/10.1002/ffo2.139 Source Futures and Foresight Science, 5 (2023) (1) Part of collection Institutional Repository Document type journal article Rights © 2022 R.M. Cooke Files PDF Futures_Foresight_Science ... idence.pdf 838.24 KB Close viewer /islandora/object/uuid:4ea9c97d-474c-49d9-bfb3-3275e82b3724/datastream/OBJ/view