Calculate the Revealed Aggregator of Probability Predictions

Forecasters predicting the chances of a future event may disagree due to differing evidence or noise. To harness the collective evidence of the crowd, Ville Satopää (2021) "Regularized Aggregation of One-off Probability Predictions" < https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3769945> proposes a Bayesian aggregator that is regularized by analyzing the forecasters' disagreement and ascribing over-dispersion to noise. This aggregator requires no user intervention and can be computed efficiently even for a large numbers of predictions. The author evaluates the aggregator on subjective probability predictions collected during a four-year forecasting tournament sponsored by the US intelligence community. The aggregator improves the accuracy of simple averaging by around 20% and other state-of-the-art aggregators by 10-25%. The advantage stems almost exclusively from improved calibration. This aggregator -- know as "the revealed aggregator" -- inputs a) forecasters' probability predictions (p) of a future binary event and b) the forecasters' base rate (p0) of the future event. In this R-package, the function sample_aggregator(p,p0,...) allows the user to calculate the revealed aggregator. Its use is illustrated with a simple stylized example.


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install.packages("braggR")

0.1.0 by Ville Satopää, 25 days ago


Browse source code at https://github.com/cran/braggR


Authors: Ville Satopää [aut, cre, cph]


Documentation:   PDF Manual  


GPL-2 license


Imports Rcpp

Suggests testthat

Linking to Rcpp


See at CRAN