Range - David Epstein Page 0,94

open mind and not tell God how to run the universe.

Beneath complexity, hedgehogs tend to see simple, deterministic rules of cause and effect framed by their area of expertise, like repeating patterns on a chessboard. Foxes see complexity in what others mistake for simple cause and effect. They understand that most cause-and-effect relationships are probabilistic, not deterministic. There are unknowns, and luck, and even when history apparently repeats, it does not do so precisely. They recognize that they are operating in the very definition of a wicked learning environment, where it can be very hard to learn, from either wins or losses.

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In wicked domains that lack automatic feedback, experience alone does not improve performance. Effective habits of mind are more important, and they can be developed. In four straight years of forecasting tournaments, Tetlock and Mellers’s research group showed that an hour of basic training in foxy habits improved accuracy. One habit was a lot like the analogical thinking that helped the venture capitalists and movie enthusiasts in chapter 5 make better projections of investment returns and film revenues. Basically, forecasters can improve by generating a list of separate events with deep structural similarities, rather than focusing only on internal details of the specific event in question. Few events are 100 percent novel—uniqueness is a matter of degree, as Tetlock puts it—and creating the list forces a forecaster implicitly to think like a statistician.

For example, in 2015, forecasters were asked if Greece would exit the eurozone that year. No country had ever left, so the question seemed totally unique. But there were plenty of examples of international negotiation failures, exits from international agreements, and forced currency conversions that allowed the best forecasters to ground themselves in what usually happens without focusing narrowly on all the unique details of the present situation. Starting with the details—the inside view—is dangerous. Hedgehog experts have more than enough knowledge about the minutiae of an issue in their specialty to do just what Dan Kahan suggested: cherry-pick details that fit their all-encompassing theories. Their deep knowledge works against them. Skillful forecasters depart from the problem at hand to consider completely unrelated events with structural commonalities rather than relying on intuition based on personal experience or a single area of expertise.

Another aspect of the forecaster training involved ferociously dissecting prediction results in search of lessons, especially for predictions that turned out bad. They made a wicked learning environment, one with no automatic feedback, a little more kind by creating rigorous feedback at every opportunity. In Tetlock’s twenty-year study, both foxes and hedgehogs were quick to update their beliefs after successful predictions, by reinforcing them even more strongly. When an outcome took them by surprise, however, foxes were much more likely to adjust their ideas. Hedgehogs barely budged. Some hedgehogs made authoritative predictions that turned out wildly wrong, and then updated their theories in the wrong direction. They became even more convinced of the original beliefs that led them astray. “Good judges are good belief updaters,” according to Tetlock. If they make a bet and lose, they embrace the logic of a loss just as they would the reinforcement of a win.

That is called, in a word: learning. Sometimes, it involves putting experience aside entirely.

CHAPTER 11

Learning to Drop Your Familiar Tools

JAKE, THE ATHLETIC-LOOKING sandy blond, speaks first. He wants to race the car. “What if everybody just agrees?” he asks. “I say, race this thang.”

It was early afternoon in fall, and Jake and six of his second-year Harvard Business School classmates found a shady spot where they could eat their lunches and talk.* Their professor had given them three pages containing one of the most famous business school case studies ever created, known as Carter Racing. The crux is whether the fictional Carter Racing team’s car should compete in the biggest race of the season, which begins in one hour.

The argument in favor of racing: thanks to a custom turbocharger, Carter Racing has placed in the money (top five) in twelve of twenty-four races. That success secured an oil company sponsorship, and a trial sponsorship from prestigious (and also fictional) Goodstone Tire. Carter Racing won the last race, its fourth win of the season. Today’s race will be on national TV, and if Carter Racing finishes in the top five, it will likely draw a $2 million sponsorship from Goodstone. If Carter Racing chooses not to race and withdraws, it would lose part of its entry fee and have to pay back some