MetaculR_aggregated_forecasts: Aggregate Community Forecasts for MetaculR

In MetaculR: Analyze Metaculus Predictions and Questions

Aggregate Community Forecasts for MetaculR

Description

Provides different results of aggregating current community forecasts to help you make your next forecast.

Usage

MetaculR_aggregated_forecasts(MetaculR_questions, Metaculus_id, baseline = 0.5)

Arguments

MetaculR_questions	A MetaculR_questions object
Metaculus_id	The ID of the question to plot
baseline	Climatological baseline for binary questions

Details

Sevilla (2021) found a Metaculus baseline of 0.36 looking at ~900 questions. While Sevilla has at times referred to the geometric mean of odds, this function uses the equivalent mean of logodds. Also note that mu + (d - 1)(mu + b) (Neyman & Roughgarden) is equivalent to b + d(mu + b), this function uses the former.

Value

A dataframe of forecast aggregations.

id	Question ID.
community_q2	Community median.
community_ave	Community mean.
community_q2_unweighted	Community median, unweighted by recency.
community_ave_unweighted	Community mean, unweighted by recency.
community_mean_logodds	Community mean of logodds.
community_mean_logodds_extremized_baseline	Community mean of logodds, extremized with reference to a baseline. If the baseline is 0.5, this is "classical extremizing."

References

Neyman, E., & Roughgarden, T. (2022). Are You Smarter Than a Random Expert? The Robust Aggregation of Substitutable Signals. ArXiv:2111.03153 [Cs]. https://arxiv.org/abs/2111.03153

Sevilla, J. (2021, December 29). Principled extremizing of aggregated forecasts. https://forum.effectivealtruism.org/posts/biL94PKfeHmgHY6qe/principled-extremizing-of-aggregated-forecasts

Examples

## Not run: 
MetaculR_aggregate_forecasts(
  MetaculR_questions = questions_myPredictions,
  Metaculus_id = 10004)

## End(Not run)

MetaculR documentation built on April 26, 2022, 1:07 a.m.