Hi again Misha,
Not sure what the finding here is: "...the 30% edge is likely partly due to the different aggregation techniques used...." [emphasis mine]
How can we know more than likely partly? On what basis can we make a determination? Goldstein et. al. posit several hypotheses for the 30% advantage Good Judgment had over the ICPM: 1) GJ folks were paid; 2) a "secrecy heuristic" posited by Travers et. al.; 3) aggregation algorithms; 4) etc.
Have you disaggregated these effects such that we can know the extent to which the aggregation techniques boosted accuracy? Maybe the effect was entirely related to the $150 Amazon gift cards that GJ forecasters received for 12 months work? Maybe the "secrecy heuristic" explains the delta?
Hi @Misha, Thank you for your patience and sorry for the delay.
I triple-checked. Without any doubt, the "All Surveys Logit" used forecast data from thousands of "regular" forecasters and several dozen Superforecasters.
So it is the case that [regular forecasters + Superforecasters] outperformed U.S. intelligence analysts on the same questions by roughly 30%. It is NOT the case that the ICPM was compared directly and solely against Superforecasters.
It may be true, as you say, that there is a "common misconception...that superforecasters outperformed intelligence analysts by 30%" -- but the Goldstein paper does not contain data that permits a direct comparison of intelligence analysts and Superforecasters.
The sentence in the 2017 article you cite contains an error. Simple typo? No idea. But typos happen and it's not the end of the world. For example, in the table above, in the box with the Goldstein study, we see "N = 193 geopolitical questions." That's a typo. It is N = 139.
So here's a potentially fatal flaw in this analysis:
You write, "Goldstein et al showed that superforecasters outperformed the intelligence community...."
But the Goldstein paper was not about the Superforecasters. Your analysis, footnote 4, says, "'All Surveys Logit' takes the most recent forecasts from a selection of individuals in GJP’s survey elicitation condition...."
Thousands of individuals were in GJP's survey elicitation condition, of whom only fraction (a few dozen) were Superforecasters.
So Goldstein did not find that "superforecasters outperformed the intelligence community"; rather, he found that [thousands of regular forecasters + a few dozen Superforecasters] outperformed the intelligence community. That's an even lower bar.
Please check for yourself. All GJP data is publicly-available here: https://dataverse.harvard.edu/dataverse/gjp.
Thanks for the clarification, @Misha-Yagudin.
So to be clear, in his November 1, 2013 article, David Ignatius had access to forecasting data from the period August 1, 2013 through May 9, 2014!! (See section 5 of the Seth Goldstein paper underlying your analysis).
That, my friend, is quite the feat!!
Curious: You say the 2015 Seth Goldstein "unpublished document" was "used to justify the famous 'Supers are 30% better than the CIA' claim."
But that was reported two years earlier, in 2013: https://www.washingtonpost.com/opinions/david-ignatius-more-chatter-than-needed/2013/11/01/1194a984-425a-11e3-a624-41d661b0bb78_story.html.
So how was the 2015 paper the justification?
May I ask who provided the forecasting training? My team is also interested in training to reduce bias and think more probabilistically. We've all read Superforecasting and Scout Mindset, etc. Ready to make it practical!