But I recently sensed a major shift in Tinseltown, and started making some phone calls to see if I was right. And why the anonymity? Welcome to the new war on the right — Hollywood style.
Posted on February 4, by Scott Alexander Philip Tetlock, author of Superforecastinggot famous by studying prediction. Although this was generally true, he was able to distinguish a small subset of people who were able to do a little better than chance.
Tetlock found that the hedgehogs did worse than the chimp and the foxes did a little better. Cut to the late s. The US intelligence community has just been seriously embarrassed by their disastrous declaration that there were weapons of mass destruction in Iraq.
They set up an Intelligence Advanced Research Projects Agency to try crazy things and see if any of them worked.
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. Tetlock was one of these scientists, and his entry into the competition was called the Good Judgment Project.
The plan was simple: This worked pretty well. This suggests a discontinuity, a natural division into two groups. Two percent of forecasters were in the top two percent. 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.
Having established that this is all pretty neat, Tetlock turns to figuring out how superforecasters are so successful. First of all, is it just luck? Superforecasters one year tended to remain superforecasters the next. The year-to-year correlation in who was most accurate was 0.
This is definitely a real thing. Are superforecasters just really smart? The superforecasters whom Tetlock profiles in his book include a Harvard physics PhD who speaks 6 languages, an assistant math professor at Cornell, a retired IBM programmer data wonk, et cetera.
But the average superforecaster is only at the 80th percentile for IQ — just under And there are a lot of people who are very smart but not very good at predicting. Are superforecasters just really well-informed about the world?
The correlation between well-informedness and accuracy was about the same as the correlation between IQ and accuracy. 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.
Even when they made decisions based on limited information, they still beat other forecasters. Are superforecasters just really good at math? A lot of them are math PhDs or math professors. The correlation between math skills and accuracy was about the same as all the other correlations.
So what are they really good at? Tetlock concludes that the number one most important factor to being a superforecaster is really understanding logic and probability. Part of it is just understanding the basics. Superforecasters tend to focus on the specific problem in front of them and break it down into pieces.
Or they might break the problem down into pieces: Most interesting, they seem to be partly immune to cognitive bias. The strongest predictor of forecasting ability okay, fine, not by much, it was pretty much the same as IQ and well-informedness and all that — but it was a predictor was the Cognitive Reflection Testwhich includes three questions with answers that are simple, obvious, and wrong.
The test seems to measure whether people take a second to step back from their System 1 judgments and analyze them critically. Superforecasters seem especially good at this. Tetlock cooperated with Daniel Kahneman on an experiment to elicit scope insensitivity in forecasters.
Remember, scope insensitivity is where you give a number-independent answer to a numerical question. For example, how much should an organization pay to save the lives of endangered birds?
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. Poor forecasters do the same thing on their predictions.
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. Maybe all this stuff about probability calibration, inside vs.Political Astrology.
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