So it seems you can get people to change their estimate of the value of bird life just by changing the number in the question.
Are superforecasters just really good at math? Part of it is just understanding the basics. Tetlock was one of these scientists, and his entry into the competition was called the Good Judgment Project.
This is definitely a real thing. Tetlock concludes that the number one most important factor to being a superforecaster is really understanding logic and probability. Two percent of forecasters were in the top two percent. This suggests a discontinuity, a natural division into two groups.
Anyway, the Good Judgment Project then put these superforecasters on teams with other superforecasters, averaged out their decisions, slightly increased the final confidence levels to represent the fact that it was 60 separate people, all of whom were that confidentand presented that to IARPA as their final answer.
It is important that the Good Judgment Project exists. The test seems to measure whether people take a second to step back from their System 1 judgments and analyze them critically. The correlation between math skills and accuracy was about the same as all the other correlations.
They set up an Intelligence Advanced Research Projects Agency to try crazy things and see if any of them worked. Tetlock cooperated with Daniel Kahneman on an experiment to elicit scope insensitivity in forecasters.
Superforecasters tend to focus on the specific problem in front of them and break it down into pieces. For example, how much should an organization pay to save the lives of endangered birds? First of all, is it just luck?
Remember, scope insensitivity is where you give a number-independent answer to a numerical question. Superforecasters, in contrast, showed much reduced scope insensitivity, and their probability of a war in five years was appropriately lower than of a war in fifteen.
None of them are remarkable for spending every single moment behind a newspaper, and none of them had as much data available as the CIA analysts with access to top secret information.
A lot of them are math PhDs or math professors. A lot of people have asked the same question: IARPA approached a bunch of scientists, handed them a list of important world events that might or might not happen, and told them to create some teams and systems for themselves and compete against each other to see who could predict them the best.
And there are a lot of people who are very smart but not very good at predicting. Posted on February 4, by Scott Alexander Philip Tetlock, author of Superforecastinggot famous by studying prediction.
Or they might break the problem down into pieces: Even when they made decisions based on limited information, they still beat other forecasters.
Superforecasters one year tended to remain superforecasters the next.“So as I said before, Superforecasting is not necessarily too useful for people who are already familiar with the cognitive science/rationality tradition, but great for people who need a high-status and official-looking book to justify it.”.
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