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A superforecaster is a person who makes forecasts that can be shown by statistical means to have been consistently more accurate than the general public or experts. . Superforecasters sometimes use modern analytical and statistical methodologies to augment estimates of base rates of events; research finds that such forecasters are typically more accurate than experts in the field who do not ...
The Economist reports that superforecasters are clever (with a good mental attitude), but not necessarily geniuses. It reports on the treasure trove of data coming from The Good Judgment Project, showing that accurately selected amateur forecasters (and the confidence they had in their forecasts) were often more accurately tuned than experts. [1]
Overall, their superforecasters gave a median estimate of 9.05 percent for a catastrophe from whatever source by 2100 while the median according to the experts was 20 percent, with 95 percent confidence intervals of [6.13, 10.25] and [15.44, 27.60] percent for superforecasters and experts, respectively.
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The Good Judgment Project (GJP) is an organization dedicated to "harnessing the wisdom of the crowd to forecast world events".It was co-created by Philip E. Tetlock (author of Superforecasting and Expert Political Judgment), decision scientist Barbara Mellers, and Don Moore, all professors at the University of Pennsylvania.
Although the above formulation is the most widely used, the original definition by Brier [1] is applicable to multi-category forecasts as well as it remains a proper scoring rule, while the binary form (as used in the examples above) is only proper for binary events.
Andrew Eric Gelman (born February 11, 1965) is an American statistician and professor of statistics and political science at Columbia University.. Gelman received bachelor of science degrees in mathematics and in physics from MIT, where he was a National Merit Scholar, in 1986.
The book's main scenario proposes that in about a hundred years from now, human brains will be scanned at "fine enough spatial and chemical resolution," and combined with rough models of signal-processing functions of brain cells, "to create a cell-by-cell dynamically executable model of the full brain in artificial hardware, a model whose signal input-output behavior is usefully close to that ...