Range: Why Generalists Triumph in a Specialized World

by David Epstein

Swanson became concerned about increasing specialization, that it would lead to publications that catered only to a very small group of specialists and inhibit creativity. “The disparity between the total quantity of recorded knowledge . . . and the limited human capacity to assimilate it, is not only enormous now but grows unremittingly,” he once said. How can frontiers be pushed, Swanson wondered, if one day it will take a lifetime just to reach them in each specialized domain?

The more information specialists create, the more opportunity exists for curious dilettantes to contribute by merging strands of widely available but disparate information—undiscovered public knowledge, as Don Swanson called it. The larger and more easily accessible the library of human knowledge, the more chances for inquisitive patrons to make connections at the cutting edge. An operation like InnoCentive, which at first blush seems totally counterintuitive, should become even more fruitful as specialization accelerates.

Yokoi was well aware of his engineering limitations. As one aficionado of game history put it, “He studied electronics at a time where the technology was evolving faster than the snow melts in sunlight.” Yokoi had no desire (or capability) to compete with electronics companies that were racing one another to invent some entirely new sliver of dazzling technology. Nor could Nintendo compete with Japan’s titans of traditional toys—Bandai, Epoch, and Takara—on their familiar turf. With that, and Drive Game, in mind, Yokoi embarked on an approach he called “lateral thinking with withered technology.” Lateral thinking is a term coined in the 1960s for the reimagining of information in new contexts, including the drawing together of seemingly disparate concepts or domains that can give old ideas new uses. By “withered technology,” Yokoi meant tech that was old enough to be extremely well understood and easily available, so it didn’t require a specialist’s knowledge. The heart of his philosophy was putting cheap, simple technology to use in ways no one else considered. If he could not think more deeply about new technologies, he decided, he would think more broadly about old ones.

Eminent physicist and mathematician Freeman Dyson styled it this way: we need both focused frogs and visionary birds. “Birds fly high in the air and survey broad vistas of mathematics out to the far horizon,” Dyson wrote in 2009. “They delight in concepts that unify our thinking and bring together diverse problems from different parts of the landscape. Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects, and they solve problems one at a time.” As a mathematician, Dyson labeled himself a frog, but contended, “It is stupid to claim that birds are better than frogs because they see farther, or that frogs are better than birds because they see deeper.” The world, he wrote, is both broad and deep. “We need birds and frogs working together to explore it.” Dyson’s concern was that science is increasingly overflowing with frogs, trained only in a narrow specialty and unable to change as science itself does. “This is a hazardous situation,” he warned, “for the young people and also for the future of science.”

In his first eight years at 3M, Ouderkirk worked with more than a hundred different teams. Nobody handed him important projects, like multilayer optical film, with potential impact spanning an enormous array of technologies; his breadth helped him identify them. “If you’re working on well-defined and well-understood problems, specialists work very, very well,” he told me. “As ambiguity and uncertainty increases, which is the norm with systems problems, breadth becomes increasingly important.”

Charles Darwin “could be considered a professional outsider,” according to creativity researcher Dean Keith Simonton. Darwin was not a university faculty member nor a professional scientist at any institution, but he was networked into the scientific community. For a time, he focused narrowly on barnacles, but got so tired of it that he declared, “I am unwilling to spend more time on the subject,” in the introduction to a barnacle monograph.

Tetlock decided to put expert predictions to the test. With the Cold War in full swing, he began a study to collect short- and long-term forecasts from 284 highly educated experts (most had doctorates) who averaged more than twelve years of experience in their specialties. The questions covered international politics and economics, and in order to make sure the predictions were concrete, the experts had to give specific probabilities of future events. Tetlock had to collect enough predictions over enough time that he could separate lucky and unlucky streaks from true skill. The project lasted twenty years, and comprised 82,361 probability estimates about the future. The results limned a very wicked world. The average expert was a horrific forecaster. Their areas of specialty, years of experience, academic degrees, and even (for some) access to classified information made no difference. They were bad at short-term forecasting, bad at long-term forecasting, and bad at forecasting in every domain.

The integrators outperformed their colleagues on pretty much everything, but they especially trounced them on long-term predictions. Eventually, Tetlock conferred nicknames (borrowed from philosopher Isaiah Berlin) that became famous throughout the psychology and intelligence-gathering communities: the narrow-view hedgehogs, who “know one big thing,” and the integrator foxes, who “know many little things.”

Hedgehog experts were deep but narrow. Some had spent their careers studying a single problem. Like Ehrlich and Simon, they fashioned tidy theories of how the world works through the single lens of their specialty, and then bent every event to fit them. The hedgehogs, according to Tetlock, “toil devotedly” within one tradition of their specialty, “and reach for formulaic solutions to ill-defined problems.” Outcomes did not matter; they were proven right by both successes and failures, and burrowed further into their ideas. It made them outstanding at predicting the past, but dart-throwing chimps at predicting the future. The foxes, meanwhile, “draw from an eclectic array of traditions, and accept ambiguity and contradiction,” Tetlock wrote. Where hedgehogs represented narrowness, foxes ranged outside a single discipline or theory and embodied breadth.

Incredibly, the hedgehogs performed especially poorly on long-term predictions within their domain of expertise. They actually got worse as they accumulated credentials and experience in their field. The more information they had to work with, the more they could fit any story to their worldview. This did give hedgehogs one conspicuous advantage. Viewing every world event through their preferred keyhole made it easy to fashion compelling stori...

In an impressively unsightly image, Tetlock described the very best forecasters as foxes with dragonfly eyes. Dragonfly eyes are composed of tens of thousands of lenses, each with a different perspective, which are then synthesized in the dragonfly’s brain.

A hallmark of interactions on the best teams is what psychologist Jonathan Baron termed “active open-mindedness.” The best forecasters view their own ideas as hypotheses in need of testing. Their aim is not to convince their teammates of their own expertise, but to encourage their teammates to help them falsify their own notions. In the sweep of humanity, that is not normal. Asked a difficult question—for example, “Would providing more money for public schools significantly improve the quality of teaching and learning?”—people naturally come up with a deluge of “myside” ideas. Armed with a web browser, they don’t start searching for why they are probably wrong. It is not that we are unable to come up with contrary ideas, it is just that our strong instinct is not to.

In a study during the run-up to the Brexit vote, a small majority of both Remainers and Brexiters were able to correctly interpret made-up statistics about the efficacy of a rash-curing skin cream, but when voters were given the same exact data presented as if it indicated that immigration either increased or decreased crime, hordes of Brits suddenly became innumerate and misinterpreted statistics that disagreed with their political beliefs. Kahan found the same phenomenon in the United States using skin cream and gun control. Kahan also documented a personality feature that fought back against that propensity: science curiosity. Not science knowledge, science curiosity.

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.

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.

“Like all of you, nobody [at NASA or Thiokol] asked for the seventeen data points for which there had been no problems,” he explains. “Obviously that data existed, and they were having a discussion like we had. If I was in your situation I would probably say, ‘But in a classroom the teacher typically gives us material we’re supposed to have.’ But it’s often the case in group meetings where the person who made the PowerPoint slides puts data in front of you, and we often just use the data people put in front of us. I would argue we don’t do a good job of saying, ‘Is this the data that we want to make the decision we need to make?’”

The presidential commission that investigated the Challenger accident concluded that simply including the nonfailure flights would have revealed the correlation between O-ring damage and temperature. A University of Chicago professor of organizational psychology wrote that the missed data was such a rudimentary mistake that it came down to “a professional weakness shared by all participants” on the conference call. “Arguments against launching at cold temperatures could have been quantified, but were not quantified.” The engineers were poorly educated, he declared.

Business professors around the world have been teaching Carter Racing for thirty years because it provides a stark lesson in the danger of reaching conclusions from incomplete data, and the folly of relying only on what is in front of you. And now for one last surprise. They all got it wrong. The Challenger decision was not a failure of quantitative analysis. NASA’s real mistake was to rely on quantitative analysis too much.

The Carter Racing study teaches that the answer was available, if only engineers looked at the right numbers. In reality, the right numbers did not contain an answer at all. The Challenger decision was truly ambiguous. It was a wicked problem, rife with uncertainty, and outside of previous experience, where demanding more data actually became the problem itself.

In 1945, former MIT dean Vannevar Bush, who oversaw U.S. military science during World War II—including the mass production of penicillin and the Manhattan Project—authored a report at the request of President Franklin Roosevelt in which he explained successful innovation culture. It was titled “Science, the Endless Frontier,” and led to the creation of the National Science Foundation that funded three generations of wildly successful scientific discovery, from Doppler radar and fiber optics to web browsers and MRIs. “Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice,” Bush wrote, “in the manner dictated by their curiosity for exploration of the unknown.”