Calculate the Knowledge-Weighted Estimate

According to a phenomenon known as "the wisdom of the crowds," combining point estimates from multiple judges often provides a more accurate aggregate estimate than using a point estimate from a single judge. However, if the judges use shared information in their estimates, the simple average will over-emphasize this common component at the expense of the judges’ private information. Asa Palley & Ville Satopää (2021) "Boosting the Wisdom of Crowds Within a Single Judgment Problem: Selective Averaging Based on Peer Predictions" <> proposes a procedure for calculating a weighted average of the judges’ individual estimates such that resulting aggregate estimate appropriately combines the judges' collective information within a single estimation problem. The authors use both simulation and data from six experimental studies to illustrate that the weighting procedure outperforms existing averaging-like methods, such as the equally weighted average, trimmed average, and median. This aggregate estimate -- know as "the knowledge-weighted estimate" -- inputs a) judges' estimates of a continuous outcome (E) and b) predictions of others' average estimate of this outcome (P). In this R-package, the function knowledge_weighted_estimate(E,P) implements the knowledge-weighted estimate. Its use is illustrated with a simple stylized example and on real-world experimental data.


Reference manual

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0.1.0 by Ville Satopää, 3 months ago

Browse source code at

Authors: Ville Satopää [aut, cre, cph] , Asa Palley [aut]

Documentation:   PDF Manual  

GPL-2 license

Imports MASS, stats

Suggests knitr, rmarkdown, testthat

See at CRAN