Then again, why we don't trust experts

Continuing the last post, one reason that many of us don't trust experts and social engineers is because they often get it wrong. I'm reading the new book Range: Why Generalists Triumph in a Specialized World. Here's an except:

"The pattern is by now familiar [....] the track record of expert forecasters—in science, in economics, in politics—is as dismal as ever. In business, esteemed (and lavishly compensated) forecasters routinely are wildly wrong in their predictions of everything from the next stock-market correction to the next housing boom. Reliable insight into the future is possible, however. It just requires a style of thinking that’s uncommon among experts who are certain that their deep knowledge has granted them a special grasp of what is to come. [...] Even faced with their results, many experts never admitted systematic flaws in their judgment. When they missed wildly, it was a near miss; if just one little thing had gone differently, they would have nailed it. 'There is often a curiously inverse relationship,' Tetlock concluded, 'between how well forecasters thought they were doing and how well they did.'"

"One subgroup of scholars, however, did manage to see more of what was coming [....] they were not vested in a single discipline. They took from each argument and integrated apparently contradictory worldviews. [...] The integrators outperformed their colleagues in pretty much every way, but especially trounced them on long-term predictions. Eventually, Tetlock bestowed nicknames (borrowed from the philosopher Isaiah Berlin) on the experts he’d observed: The highly specialized hedgehogs knew 'one big thing,' while the integrator foxes knew 'many little things.'”

"When an outcome took them by surprise, foxes were much more likely to adjust their ideas. Hedgehogs barely budged. Some made authoritative predictions that turned out to be wildly wrong—then updated their theories in the wrong direction. They became even more convinced of the original beliefs that had led them astray. The best forecasters, by contrast, view their own ideas as hypotheses in need of testing. If they make a bet and lose, they embrace the logic of a loss just as they would the reinforcement of a win. This is called, in a word, learning."