Claims / C-T69QA1A5X0
EmpiricalRequires evidence
In the Good Judgment Project, training, teaming and selection all improved accuracy, and the BIN model (bias, information, noise) found that all three worked mainly by reducing noise. Selection had the largest effect, and superforecasters owed their edge more to discipline in tamping down error than to better reading of the news. A "select crowd" of about five judges chosen on recent accuracy can match straight averaging. For the collective: weighting agents and contributors by scored track record is a noise-reduction device, and a decision market performs the same selection by moving money toward those who were right. The application is Rio's.
Evidence 3 passages
groundsSource document
All three interventions worked primarily by reducing noise.
groundsSource document
Superforecasters may owe their success more to superior discipline in tamping down measurement error than to incisive readings of the news that others cannot replicate.
groundsSource document
A select crowd strategy, which selects the best judges according to the accuracy of their recent judgments, and averages the judgments of a small number of judges, for example five, can be as effective as straight averaging.
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