I found some revelant discussion in the EA Forum about extremizing in footnote 5 of this post.
The aggregation algorithm was elitist, meaning that it weighted more heavily forecasters with good track-records who had updated their forecasts more often. In these slides, Tetlock describes the elitism differently: He says it gives weight to higher-IQ, more open-minded forecasters. The extremizing step pushes the aggregated judgment closer to 1 or 0, to make it more confident. The degree to which they extremize depends on how diverse and sophisticated the pool of forecasters is. The academic papers on this topic can be found here and here. Whether extremizing is a good idea is controversial; according to one expert I interviewed, more recent data suggests that the successes of the extremizing algorithm during the forecasting tournament were a fluke. After all, a priori one would expect extremizing to lead to small improvements in accuracy most of the time, but big losses in accuracy some of the time.
The post in general is quite good, and I recommend it.
I found some revelant discussion in the EA Forum about extremizing in footnote 5 of this post.
The post in general is quite good, and I recommend it.