MetaculR_aggregated_forecasts {MetaculR} | R Documentation |
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)