metaggR: 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"
<https://papers.ssrn.com/sol3/Papers.cfm?abstract_id=3504286> 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.
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