MetaculR (version 0.4.1)

MetaculR_aggregated_forecasts: 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

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."

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.

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)
# }