braggR: 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' common prior (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 example.
||testthat (≥ 3.0.0)
||Ville Satopää [aut, cre, cph]
||Ville Satopää <ville.satopaa at gmail.com>
||(c) Ville Satopaa
||braggR citation info
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