Range — David Epstein
“A well-supported and smoothly written case on behalf of breadth and late starts. . . . as David Epstein shows us, cultivating range prepares us for the wickedly unanticipated.” — Wall Street Journal
The world is not golf, and most of it isn’t even tennis. [That is to say they aren’t kind learning environments where hyper-specialization does well, as in chess] As Robin Hogarth put it, much of the world is “Martian tennis.” You can see the players on a court with balls and rackets, but nobody has shared the rules. It is up to you to derive them, and they are subject to change without notice.
If the amount of early, specialized practice in a narrow area were the key to innovative performance, savants would dominate every domain they touched, and child prodigies would always go on to adult eminence. As psychologist Ellen Winner, one of the foremost authorities on gifted children, noted, no savant has ever been known to become a “Big-C creator,” who changed their field.
When experienced accountants were asked in a study to use a new tax law for deductions that replaced a previous one, they did worse than novices. Erik Dane, a Rice University professor who studies organizational behavior, calls this phenomenon “cognitive entrenchment.”
In 2007, the U.S. Department of Education published a report by six scientists and an accomplished teacher who were asked to identify learning strategies that truly have scientific backing. Spacing, testing, and using making-connections questions were on the extremely short list. All three impair performance in the short term.
Netflix came to a similar conclusion for improving its recommendation algorithm. Decoding movies’ traits to figure out what you like was very complex and less accurate than simply analogizing you to many other customers with similar viewing histories. Instead of predicting what you might like, they examine who you are like, and the complexity is captured therein.
Paul Graham, computer scientist and cofounder of Y Combinator—the start-up funder of Airbnb, Dropbox, Stripe, and Twitch—encapsulated Ibarra’s tenets in a high school graduation speech he wrote, but never delivered: It might seem that nothing would be easier than deciding what you like, but it turns out to be hard, partly because it’s hard to get an accurate picture of most jobs... Most of the work I’ve done in the last ten years didn’t exist when I was in high school… In such a world it’s not a good idea to have fixed plans. And yet every May, speakers all over the country fire up the Standard Graduation Speech, the theme of which is: don’t give up on your dreams. I know what they mean, but this is a bad way to put it, because it implies you’re supposed to be bound by some plan you made early on. The computer world has a name for this: premature optimization… Instead of working back from a goal, work forward from promising situations. This is what most successful people actually do anyway. In the graduation-speech approach, you decide where you want to be in twenty years, and then ask: what should I do now to get there? I propose instead that you don’t commit to anything in the future, but just look at the options available now, and choose those that will give you the most promising range of options afterward.
Facing uncertain environments and wicked problems, breadth of experience is invaluable. Facing kind problems, narrow specialization can be remarkably efficient. The problem is that we often expect the hyperspecialist, because of their expertise in a narrow area, to magically be able to extend their skill to wicked problems. The results can be disastrous.
Yale law and psychology professor Dan Kahan has shown that more scientifically literate adults are actually more likely to become dogmatic about politically polarizing topics in science. Kahan thinks it could be because they are better at finding evidence to confirm their feelings: the more time they spend on the topic, the more hedgehog-like they become.
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Range
INTRODUCTION: Roger vs. Tiger
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The title of one study of athletes in individual sports proclaimed “Late Specialization” as “the Key to Success”; another, “Making It to the Top in Team Sports: Start Later, Intensify, and Be Determined.” When I began to write about these studies, I was met with thoughtful criticism, but also denial. “Maybe in some other sport,” fans often said, “but that’s not true of our sport.” The community of the world’s most popular sport, soccer, was the loudest. And then, as if on cue, in late 2014 a team of German scientists published a study showing that members of their national team, which had just won the World Cup, were typically late specializers who didn’t play more organized soccer than amateur-league players until age twenty-two or later. They spent more of their childhood and adolescence playing nonorganized soccer and other sports.
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The professed necessity of hyperspecialization forms the core of a vast, successful, and sometimes well-meaning marketing machine, in sports and beyond. In reality, the Roger path to sports stardom is far more prevalent than the Tiger path, but those athletes’ stories are much more quietly told, if they are told at all. Some of their names you know, but their backgrounds you probably don’t.
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I was stunned when cognitive psychologists I spoke with led me to an enormous and too often ignored body of work demonstrating that learning itself is best done slowly to accumulate lasting knowledge, even when that means performing poorly on tests of immediate progress. That is, the most effective learning looks inefficient; it looks like falling behind.
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Mark Zuckerberg famously noted that “young people are just smarter.” And yet a tech founder who is fifty years old is nearly twice as likely to start a blockbuster company as one who is thirty, and the thirty-year-old has a better shot than a twenty-year-old. Researchers at Northwestern, MIT, and the U.S. Census Bureau studied new tech companies and showed that among the fastest-growing start-ups, the average age of a founder was forty-five when the company was launched.
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While it is undoubtedly true that there are areas that require individuals with Tiger’s precocity and clarity of purpose, as complexity increases—as technology spins the world into vaster webs of interconnected systems in which each individual only sees a small part—we also need more Rogers: people who start broad and embrace diverse experiences and perspectives while they progress. People with range.
CHAPTER 1: The Cult of the Head Start
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Just how much of the world, and how many of the things humans want to learn and do, are really like chess and golf?
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In 2009, Kahneman and Klein took the unusual step of coauthoring a paper in which they laid out their views and sought common ground. And they found it. Whether or not experience inevitably led to expertise, they agreed, depended entirely on the domain in question. Narrow experience made for better chess and poker players and firefighters, but not for better predictors of financial or political trends, or of how employees or patients would perform. The domains Klein studied, in which instinctive pattern recognition worked powerfully, are what psychologist Robin Hogarth termed “kind” learning environments. Patterns repeat over and over, and feedback is extremely accurate and usually very rapid.
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Kahneman was focused on the flip side of kind learning environments; Hogarth called them “wicked.” In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.
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A few years later, the first “freestyle chess” tournament was held. Teams could be made up of multiple humans and computers. The lifetime-of-specialized-practice advantage that had been diluted in advanced chess was obliterated in freestyle. A duo of amateur players with three normal computers not only destroyed Hydra, the best chess supercomputer, they also crushed teams of grandmasters using computers. Kasparov concluded that the humans on the winning team were the best at “coaching” multiple computers on what to examine, and then synthesizing that information for an overall strategy. Human/Computer combo teams—known as “centaurs”—were playing the highest level of chess ever seen. If Deep Blue’s victory over Kasparov signaled the transfer of chess power from humans to computers, the victory of centaurs over Hydra symbolized something more interesting still: humans empowered to do what they do best without the prerequisite of years of specialized pattern recognition.
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That test reenacted an experiment from 1973, in which two Carnegie Mellon University psychologists, William G. Chase and soon-to-be Nobel laureate Herbert A. Simon, repeated the De Groot exercise, but added a wrinkle. This time, the chess players were also given boards with the pieces in an arrangement that would never actually occur in a game. Suddenly, the experts performed just like the lesser players. The grandmasters never had photographic memories after all. Through repetitive study of game patterns, they had learned to do what Chase and Simon called “chunking.” Rather than struggling to remember the location of every individual pawn, bishop, and rook, the brains of elite players grouped pieces into a smaller number of meaningful chunks based on familiar patterns.
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Studying an enormous number of repetitive patterns is so important in chess that early specialization in technical practice is critical. Psychologists Fernand Gobet (an international master) and Guillermo Campitelli (coach to future grandmasters) found that the chances of a competitive chess player reaching international master status (a level down from grandmaster) dropped from one in four to one in fifty-five if rigorous training had not begun by age twelve.
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Chris Argyris, who helped create the Yale School of Management, noted the danger of treating the wicked world as if it is kind. He studied high-powered consultants from top business schools for fifteen years, and saw that they did really well on business school problems that were well defined and quickly assessed. But they employed what Argyris called single-loop learning, the kind that favors the first familiar solution that comes to mind. Whenever those solutions went wrong, the consultant usually got defensive. Argyris found their “brittle personalities” particularly surprising given that “the essence of their job is to teach others how to do things differently.”
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The world is not golf, and most of it isn’t even tennis. As Robin Hogarth put it, much of the world is “Martian tennis.” You can see the players on a court with balls and rackets, but nobody has shared the rules. It is up to you to derive them, and they are subject to change without notice.
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If the amount of early, specialized practice in a narrow area were the key to innovative performance, savants would dominate every domain they touched, and child prodigies would always go on to adult eminence. As psychologist Ellen Winner, one of the foremost authorities on gifted children, noted, no savant has ever been known to become a “Big-C creator,” who changed their field.
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When experienced accountants were asked in a study to use a new tax law for deductions that replaced a previous one, they did worse than novices. Erik Dane, a Rice University professor who studies organizational behavior, calls this phenomenon “cognitive entrenchment.”
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Claude Shannon, who launched the Information Age thanks to a philosophy course he took to fulfill a requirement at the University of Michigan. In it, he was exposed to the work of self-taught nineteenth-century English logician George Boole, who assigned a value of 1 to true statements and 0 to false statements and showed that logic problems could be solved like math equations. It resulted in absolutely nothing of practical importance until seventy years after Boole passed away, when Shannon did a summer internship at AT&T’s Bell Labs research facility. There he recognized that he could combine telephone call-routing technology with Boole’s logic system to encode and transmit any type of information electronically. It was the fundamental insight on which computers rely. “It just happened that no one else was familiar with both those fields at the same time,” Shannon said.
CHAPTER 2: How the Wicked World Was Made
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The Flynn effect—the increase in correct IQ test answers with each new generation in the twentieth century—has now been documented in more than thirty countries. The gains are startling: three points every ten years. To put that in perspective, if an adult who scored average today were compared to adults a century ago, she would be in the 98th percentile.
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Flynn conducted a study in which he compared the grade point averages of seniors at one of America’s top state universities, from neuroscience to English majors, to their performance on a test of critical thinking. The test gauged students’ ability to apply fundamental abstract concepts from economics, social and physical sciences, and logic to common, real-world scenarios. Flynn was bemused to find that the correlation between the test of broad conceptual thinking and GPA was about zero. In Flynn’s words, “the traits that earn good grades at [the university] do not include critical ability of any broad significance.”* Each of twenty test questions gauged a form of conceptual thinking that can be put to widespread use in the modern world. For test items that required the kind of conceptual reasoning that can be gleaned with no formal training—detecting circular logic, for example—the students did well. But in terms of frameworks that can best put their conceptual reasoning skills to use, they were horrible. Biology and English majors did poorly on everything that was not directly related to their field. None of the majors, including psychology, understood social science methods. Science students learned the facts of their specific field without understanding how science should work in order to draw true conclusions. Neuroscience majors did not do particularly well on anything. Business majors performed very poorly across the board, including in economics. Econ majors did the best overall. Economics is a broad field by nature, and econ professors have been shown to apply the reasoning principles they’ve learned to problems outside their area.* Chemists, on the other hand, are extraordinarily bright, but in several studies struggled to apply scientific reasoning to nonchemistry problems.
CHAPTER 3: When Less of the Same Is More
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Charles Limb, a musician, hearing specialist, and auditory surgeon at the University of California, San Francisco, designed an iron-free keyboard so that jazz musicians could improvise while inside an MRI scanner. Limb saw that brain areas associated with focused attention, inhibition, and self-censoring turned down when the musicians were creating. “It’s almost as if the brain turned off its own ability to criticize itself,” he told National Geographic. While improvising, musicians do pretty much the opposite of consciously identifying errors and stopping to correct them.
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In totality, the picture is in line with a classic research finding that is not specific to music: breadth of training predicts breadth of transfer. That is, the more contexts in which something is learned, the more the learner creates abstract models, and the less they rely on any particular example. Learners become better at applying their knowledge to a situation they’ve never seen before, which is the essence of creativity.
CHAPTER 4: Learning, Fast and Slow
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Teachers in every country fell into the same trap at times, but in the higher-performing countries plenty of making-connections problems remained that way as the class struggled to figure them out. In Japan, a little more than half of all problems were making-connections problems, and half of those stayed that way through the solving. An entire class period could be just one problem with many parts. When a student offered an idea for how to approach a problem, rather than engaging in multiple choice, the teacher had them come to the board and put a magnet with their name on it next to the idea. By the end of class, one problem on a blackboard the size of an entire wall served as a captain’s log of the class’s collective intellectual voyage, dead ends and all. Richland originally tried to label the videotaped lessons with a single topic of the day, “but we couldn’t do it with Japan,” she said, “because you could engage with these problems using so much different content.” (There is a specific Japanese word to describe chalkboard writing that tracks conceptual connections over the course of collective problem solving: bansho.)
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Kornell and psychologist Janet Metcalfe tested sixth graders in the South Bronx on vocabulary learning, and varied how they studied in order to explore the generation effect. Students were given some of the words and definitions together. For example, To discuss something in order to come to an agreement: Negotiate. For others, they were shown only the definition and given a little time to think of the right word, even if they had no clue, before it was revealed. When they were tested later, students did way better on the definition-first words. The experiment was repeated on students at Columbia University, with more obscure words (Characterized by haughty scorn: Supercilious). The results were the same. Being forced to generate answers improves subsequent learning even if the generated answer is wrong. It can even help to be wildly wrong. Metcalfe and colleagues have repeatedly demonstrated a “hypercorrection effect.” The more confident a learner is of their wrong answer, the better the information sticks when they subsequently learn the right answer. Tolerating big mistakes can create the best learning opportunities.*
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It is what it sounds like—leaving time between practice sessions for the same material. You might call it deliberate not-practicing between bouts of deliberate practice. “There’s a limit to how long you should wait,” Kornell told me, “but it’s longer than people think. It could be anything, studying foreign language vocabulary or learning how to fly a plane, the harder it is, the more you learn.” Space between practice sessions creates the hardness that enhances learning. One study separated Spanish vocabulary learners into two groups—a group that learned the vocab and then was tested on it the same day, and a second that learned the vocab but was tested on it a month later. Eight years later, with no studying in the interim, the latter group retained 250 percent more. For a given amount of Spanish study, spacing made learning more productive by making it easy to make it hard.
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Repetition, it turned out, was less important than struggle.
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In 2007, the U.S. Department of Education published a report by six scientists and an accomplished teacher who were asked to identify learning strategies that truly have scientific backing. Spacing, testing, and using making-connections questions were on the extremely short list. All three impair performance in the short term.
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Psychologist Robert Bjork first used the phrase “desirable difficulties” in 1994. Twenty years later, he and a coauthor concluded a book chapter on applying the science of learning like this: “Above all, the most basic message is that teachers and students must avoid interpreting current performance as learning. Good performance on a test during the learning process can indicate mastery, but learners and teachers need to be aware that such performance will often index, instead, fast but fleeting progress.”
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In 2017, Greg Duncan, the education economist, along with psychologist Drew Bailey and colleagues, reviewed sixty-seven early childhood education programs meant to boost academic achievement. Programs like Head Start did give a head start, but academically that was about it.
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The research team recommended that if programs want to impart lasting academic benefits they should focus instead on “open” skills that scaffold later knowledge. Teaching kids to read a little early is not a lasting advantage. Teaching them how to hunt for and connect contextual clues to understand what they read can be. As with all desirable difficulties, the trouble is that a head start comes fast, but deep learning is slow. “The slowest growth,” the researchers wrote, occurs “for the most complex skills.”
CHAPTER 5: Thinking Outside Experience
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Following their private-equity-investor experiment, the outside-view researchers turned to the movie business, a notoriously uncertain realm with high risk, high reward, and a huge store of data on actual outcomes. They wondered if forcing analogical thinking on moviegoers could lead to accurate forecasts of film success. They started by giving hundreds of movie fans basic film information—lead actor names, the promotional poster, and a synopsis—for an upcoming release. At the time, those included Wedding Crashers, Fantastic Four, Deuce Bigalow: European Gigolo, and others. The moviegoers were also given a list of forty older movies, and asked to score how well each one probably served as an analogy to each upcoming release. The researchers used those similarity scores (and a little basic film information, like whether it was a sequel) to predict the eventual revenue of the upcoming releases. They pitted those predictions against a mathematical model stuffed with information about seventeen hundred past movies and each upcoming film, including genre, budget, star actors, release year, and whether it was a holiday release. Even without all that detailed information, the revenue predictions that used moviegoer analogy scores were vastly better. The moviegoer-analogies forecast performed better on fifteen of nineteen upcoming releases. Using the moviegoers’ analogies gave revenue projections that were less than 4 percent off for War of the Worlds, Bewitched, and Red Eye, and 1.7 percent off for Deuce Bigalow: European Gigolo. Netflix came to a similar conclusion for improving its recommendation algorithm. Decoding movies’ traits to figure out what you like was very complex and less accurate than simply analogizing you to many other customers with similar viewing histories. Instead of predicting what you might like, they examine who you are like, and the complexity is captured therein.
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The good news is that it is easy to ride analogies from the intuitive inside view to the outside view. In 2001, the Boston Consulting Group, one of the most successful in the world, created an intranet site to provide consultants with collections of material to facilitate wide-ranging analogical thinking. The interactive “exhibits” were sorted by discipline (anthropology, psychology, history, and others), concept (change, logistics, productivity, and so on), and strategic theme (competition, cooperation, unions and alliances, and more). A consultant generating strategies for a post-merger integration might have perused the exhibit on how William the Conqueror “merged” England with the Norman Kingdom in the eleventh century. An exhibit that described Sherlock Holmes’s observational strategies could have provided ideas for learning from details that experienced professionals take for granted. And a consultant working with a rapidly expanding start-up might have gleaned ideas from the writing of a Prussian military strategist who studied the fragile equilibrium between maintaining momentum after a victory and overshooting a goal by so much that it turns into a defeat. If that all sounds incredibly remote from pressing business concerns, that is exactly the point.
CHAPTER 6: The Trouble with Too Much Grit
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“Match quality” is a term economists use to describe the degree of fit between the work someone does and who they are—their abilities and proclivities.
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Malamud could not randomly assign people to life in order to study specialization timing, but he found a natural experiment in the British school system. For the period he studied, English and Welsh students had to specialize before college so that they could apply to specific, narrow programs. In Scotland, on the other hand, students were actually required to study different fields for their first two years of college, and could keep sampling beyond that. In each country, every college course that a student took provided skills that could be applied in a specific field, as well as information about their match quality with the field itself. If students focused earlier, they compiled more skills that prepared them for gainful employment. If they sampled and focused later, they entered the job market with fewer domain-specific skills, but a greater sense of the type of work that fit their abilities and inclinations. Malamud’s question was: Who usually won the trade-off, early or late specializers?
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Malamud analyzed data for thousands of former students, and found that college graduates in England and Wales were consistently more likely to leap entirely out of their career fields than their later-specializing Scottish peers. And despite starting out behind in income because they had fewer specific skills, the Scots quickly caught up. Their counterparts in England and Wales were more often switching fields after college and after beginning a career even though they had more disincentive to switch, having focused on that field. With less sampling opportunity, more students headed down a narrow path before figuring out if it was a good one. The English and Welsh students were specializing so early that they were making more mistakes. Malamud’s conclusion: “The benefits to increased match quality . . . outweigh the greater loss in skills.” Learning stuff was less important than learning about oneself. Exploration is not just a whimsical luxury of education; it is a central benefit.
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Steven Levitt, the economist who coauthored Freakonomics, cleverly leveraged his readership for a test of switching. On the “Freakonomics Experiments” home page, he invited readers who were considering life changes to flip a digital coin. Heads meant they should go ahead and make the change, tails that they should not. Twenty thousand volunteers responded, agonizing over everything from whether they should get a tattoo, try online dating, or have a child, to the 2,186 people who were pondering a job change.* But could they really trust a momentous decision to chance? The answer for the potential job changers who flipped heads was: only if they wanted to be happier. Six months later, those who flipped heads and switched jobs were substantially happier than the stayers.* According to Levitt, the study suggested that “admonitions such as ‘winners never quit and quitters never win,’ while well-meaning, may actually be extremely poor advice.” Levitt identified one of his own most important skills as “the willingness to jettison” a project or an entire area of study for a better fit.
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Where the Whole Candidate Score failed to predict Beast dropouts, the Grit Scale was better. Duckworth extended the study to other domains, like the finals of the Scripps National Spelling Bee. She found that both verbal IQ tests and grit predicted how far a speller would get in the competition, but that they did so separately. It was best to have a ton of both, but spellers with little grit could make up for it with high verbal IQ scores, and spellers with lower verbal IQ scores could compensate with grit. Duckworth’s intriguing work spawned a cottage industry, for a very large cottage. Sports teams, Fortune 500 companies, charter school networks, and the U.S. Department of Education began touting grit, attempting to develop grit, even testing for grit. Duckworth won a MacArthur “genius” grant for her work, but nonetheless responded thoughtfully to the fervor with an op-ed in the New York Times. “I worry I’ve contributed, inadvertently, to an idea I vigorously oppose: high-stakes character assessment,” she wrote. That is not the only way in which grit research has been extended or exaggerated beyond its evidence.
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In the wider world of work, finding a goal with high match quality in the first place is the greater challenge, and persistence for the sake of persistence can get in the way.
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The Grit Scale statement “I have been obsessed with a certain idea or project for a short time but later lost interest” is Van Gogh in a nutshell, at least up until the final few years of his life when he settled on his unique style and creatively erupted. Van Gogh was an example of match quality optimization, Robert Miller’s multi-armed bandit process come to life. He tested options with maniacal intensity and got the maximum information signal about his fit as quickly as possible, and then moved to something else and repeated, until he had zigzagged his way to a place no one else had ever been, and where he alone excelled. Van Gogh’s Grit Scale score, according to Naifeh’s assessment, was flush with hard work but low on sticking with every goal or project. He landed in the 40th percentile.
CHAPTER 7: Flirting with Your Possible Selves
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Dark horses were on the hunt for match quality. “They never look around and say, ‘Oh, I’m going to fall behind, these people started earlier and have more than me at a younger age,’” Ogas told me. “They focused on, ‘Here’s who I am at the moment, here are my motivations, here’s what I’ve found I like to do, here’s what I’d like to learn, and here are the opportunities. Which of these is the best match right now? And maybe a year from now I’ll switch because I’ll find something better.’”
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Ogas uses the shorthand “standardization covenant” for the cultural notion that it is rational to trade a winding path of self-exploration for a rigid goal with a head start because it ensures stability.
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The most momentous personality changes occur between age eighteen and one’s late twenties, so specializing early is a task of predicting match quality for a person who does not yet exist. It could work, but it makes for worse odds. Plus, while personality change slows, it does not stop at any age. Sometimes it can actually happen instantly.
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Rather than a grand plan, find experiments that can be undertaken quickly. “Test-and-learn,” Ibarra told me, “not plan-and-implement.”
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Paul Graham, computer scientist and cofounder of Y Combinator—the start-up funder of Airbnb, Dropbox, Stripe, and Twitch—encapsulated Ibarra’s tenets in a high school graduation speech he wrote, but never delivered: It might seem that nothing would be easier than deciding what you like, but it turns out to be hard, partly because it’s hard to get an accurate picture of most jobs. . . . Most of the work I’ve done in the last ten years didn’t exist when I was in high school. . . . In such a world it’s not a good idea to have fixed plans. And yet every May, speakers all over the country fire up the Standard Graduation Speech, the theme of which is: don’t give up on your dreams. I know what they mean, but this is a bad way to put it, because it implies you’re supposed to be bound by some plan you made early on. The computer world has a name for this: premature optimization. . . . . . . Instead of working back from a goal, work forward from promising situations. This is what most successful people actually do anyway. In the graduation-speech approach, you decide where you want to be in twenty years, and then ask: what should I do now to get there? I propose instead that you don’t commit to anything in the future, but just look at the options available now, and choose those that will give you the most promising range of options afterward.
CHAPTER 8: The Outsider Advantage
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Bingham had noticed that established companies tended to approach problems with so-called local search, that is, using specialists from a single domain, and trying solutions that worked before. Meanwhile, his invitation to outsiders worked so well that it was spun off as an entirely separate company. Named InnoCentive, it facilitates entities in any field acting as “seekers,” paying to post “challenges” and rewards for outside “solvers.” A little more than one-third of challenges were completely solved, a remarkable portion given that InnoCentive selected for problems that had stumped the specialists who posted them. Along the way, InnoCentive realized it could help seekers tailor their posts to make a solution more likely. The trick: to frame the challenge so that it attracted a diverse array of solvers. The more likely a challenge was to appeal not just to scientists but also to attorneys and dentists and mechanics, the more likely it was to be solved. Bingham calls it “outside-in” thinking: finding solutions in experiences far outside of focused training for the problem itself. History is littered with world-changing examples.
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Almost twenty years after the Exxon Valdez spill, thirty-two thousand gallons of oil remained stubbornly stuck along Alaska’s coast. One of the most intractable challenges for oil spill remediation was pumping oil out of recovery barges after it was skimmed from the water. In 2007, Scott Pegau, research program manager at the Alaska-based Oil Spill Recovery Institute, figured he might as well try InnoCentive. He offered a $20,000 reward for a solution to getting cold chocolate mousse out of recovery barges. Ideas rolled in. Most were too expensive to be practical. And then there was the solution from John Davis, so cheap and simple it made Pegau chuckle. “Everyone kind of looked at it,” Pegau told me, “and just said, ‘Yep, this should work.’”
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“It took me three evenings to write it up,” an outside solver told the journal Science after he answered Johnson & Johnson’s request for help with a production problem in the manufacture of tuberculosis medication. “I think it’s strange that a major pharma company cannot solve this kind of problem.” Karim Lakhani, codirector of the Laboratory for Innovation Science at Harvard, had InnoCentive solvers rate problems on how relevant they were to their own field of specialization, and found that “the further the problem was from the solver’s expertise, the more likely they were to solve it.”
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Shubin Dai, who lives in Changsha, China, was the top-ranked Kaggle solver in the world as of this writing, out of more than forty thousand contributors. His day job is leading a team that processes data for banks, but Kaggle competitions gave him an opportunity to dabble in machine learning. His favorite problems involve human health or nature conservation, like a competition in which he won $30,000 by wielding satellite imagery to distinguish human-caused from natural forest loss in the Amazon. Dai was asked, for a Kaggle blog post, how important domain expertise is for winning competitions. “To be frank, I don’t think we can benefit from domain expertise too much. . . . It’s very hard to win a competition just by using [well-known] methods,” he replied. “We need more creative solutions.” “The people who win a Kaggle health competition have no medical training, no biology training, and they’re also often not real machine learning experts,” Pedro Domingos, a computer science professor and machine learning researcher, told me. “Knowledge is a double-edged sword. It allows you to do some things, but it also makes you blind to other things that you could do.”
CHAPTER 9: Lateral Thinking with Withered Technology
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He became so interested in classifying innovators that he wrote a computer algorithm to analyze ten million patents from the last century and learn to identify and classify different types of inventors. Specialist contributions skyrocketed around and after World War II, but more recently have declined. “Specialists specifically peaked about 1985,” Ouderkirk told me. “And then declined pretty dramatically, leveled off about 2007, and the most recent data show it’s declining again, which I’m trying to understand.” He is careful to say that he can’t pinpoint a cause of the current trend. His hypothesis is that organizations simply don’t need as many specialists. “As information becomes more broadly available, the need for somebody to just advance a field isn’t as critical because in effect they are available to everybody,” he said.
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Research by Spanish business professors Eduardo Melero and Neus Palomeras backed up Ouderkirk’s idea. They analyzed fifteen years of tech patents from 32,000 teams at 880 different organizations, tracking each individual inventor as he or she moved among teams, and then tracking the impact of each invention. Melero and Palomeras measured uncertainty in each technological domain: a high-uncertainty area had a lot of patents that proved totally useless, and some blockbusters; low-uncertainty domains were characterized by linear progression with more obvious next steps and more patents that were moderately useful. In low-uncertainty domains, teams of specialists were more likely to author useful patents. In high-uncertainty domains—where the fruitful questions themselves were less obvious—teams that included individuals who had worked on a wide variety of technologies were more likely to make a splash. The higher the domain uncertainty, the more important it was to have a high-breadth team member.
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Taylor and Greve tracked individual creators’ careers and analyzed the commercial value of thousands of comic books from 234 publishers since that time.
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A high-repetition workload negatively impacted performance. Years of experience had no impact at all. If not experience, repetition, or resources, what helped creators make better comics on average and innovate? The answer (in addition to not being overworked) was how many of twenty-two different genres a creator had worked in, from comedy and crime, to fantasy, adult, nonfiction, and sci-fi. Where length of experience did not differentiate creators, breadth of experience did. Broad genre experience made creators better on average and more likely to innovate.
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Facing uncertain environments and wicked problems, breadth of experience is invaluable. Facing kind problems, narrow specialization can be remarkably efficient. The problem is that we often expect the hyperspecialist, because of their expertise in a narrow area, to magically be able to extend their skill to wicked problems. The results can be disastrous.
CHAPTER 10: Fooled by Expertise
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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.
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There was also a “perverse inverse relationship” between fame and accuracy. The more likely an expert was to have his or her predictions featured on op-ed pages and television, the more likely they were always wrong. Or, not always wrong. Rather, as Tetlock and his coauthor succinctly put it in their book Superforecasting, “roughly as accurate as a dart-throwing chimpanzee.”
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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.”
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The volunteers drawn from the general public beat experienced intelligence analysts with access to classified data “by margins that remain classified,” according to Tetlock. (He has, though, referenced a Washington Post report indicating that the Good Judgment Project performed about 30 percent better than a collection of intelligence community analysts.)
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In separate work, from 2000 to 2010 German psychologist Gerd Gigerenzer compiled annual dollar-euro exchange rate predictions made by twenty-two of the most prestigious international banks—Barclays, Citigroup, JPMorgan Chase, Bank of America Merrill Lynch, and others. Each year, every bank predicted the end-of-year exchange rate. Gigerenzer’s simple conclusion about those projections, from some of the world’s most prominent specialists: “Forecasts of dollar-euro exchange rates are worthless.” In six of the ten years, the true exchange rate fell outside the entire range of all twenty-two bank forecasts. Where a superforecaster quickly highlighted a change in exchange rate direction that confused him, and adjusted, major bank forecasts missed every single change of direction in the decade Gigerenzer analyzed.
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Yale law and psychology professor Dan Kahan has shown that more scientifically literate adults are actually more likely to become dogmatic about politically polarizing topics in science. Kahan thinks it could be because they are better at finding evidence to confirm their feelings: the more time they spend on the topic, the more hedgehog-like they become.
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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. Kahan and colleagues measured science curiosity cleverly, smuggling relevant questions into what looked like consumer marketing surveys, and tracking how people pursued follow-up information after viewing videos with particular content, some of them science-related. The most science-curious folk always chose to look at new evidence, whether or not it agreed with their current beliefs.
CHAPTER 11: Learning to Drop Your Familiar Tools
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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.
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A trio of psychology and management professors who analyzed a century of Himalayan mountain climbers—5,104 expedition groups in all—found that teams from countries that strongly valued hierarchical culture got more climbers to the summit, but also had more climbers die along the way.
CHAPTER 12: Deliberate Amateurs
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A Science profile of him bore the section titles “Going for Breadth” and “Spread Thin,” which would sound really bad and like he was falling behind if the article wasn’t also about how at thirty-six he was the youngest physics Nobel laureate in forty years. Like Van Gogh or Frances Hesselbein or hordes of young athletes, Novoselov probably looked from the outside like he was behind, until all of a sudden he very much wasn’t. He was lucky. He arrived in a workspace that treated mental meandering as a competitive advantage, not a pest to be exterminated in the name of efficiency. That kind of protection from the cult of the head start is increasingly rare. At some point or other, we all specialize to one degree or another, so the rush to get there can seem logical.
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To recap: work that builds bridges between disparate pieces of knowledge is less likely to be funded, less likely to appear in famous journals, more likely to be ignored upon publication, and then more likely in the long run to be a smash hit in the library of human knowledge.
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“When I went to medical school, I was taught that there were no human diseases caused by retroviruses, that retroviruses were a curiosity that occurred in some animal tumors. In 1981, a new disease emerges that nobody knows anything about. In 1984, it’s found to be a retrovirus, HIV. In 1987, you have the first therapy. In 1996, you have such effective therapy that people don’t have to die of it anymore. How did that happen? Was it because companies all of a sudden rushed to make drugs? No. If you really look back and analyze it, before that time society had spent some of its very hard-earned money to study a curiosity called retroviruses. Just a curiosity in animals. So by the time HIV was found to be a retrovirus, you already knew that if you interfered with the protease [a type of enzyme] that you could deactivate it. So when HIV arrived, society had right off the shelf a huge amount of knowledge from investments made in a curiosity that at the time had no use. It may very well be that if you were to take all the research funding in the country and you put it in Alzheimer’s disease, you would never get to the solution. But the answer to Alzheimer’s disease may come from a misfolding protein in a cucumber. But how are you going to write a grant on a cucumber? And who are you going to send it to? If somebody gets interested in a folding protein in a cucumber and it’s a good scientific question, leave them alone. Let them torture the cucumber.”
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A curious phenomenon has appeared in recent years on a near-annual basis when the Nobel Prizes are awarded. Someone who receives one explains that their breakthrough could not have occurred today. In 2016, Japanese biologist Yoshinori Ohsumi closed his Nobel lecture ominously: “Truly original discoveries in science are often triggered by unpredictable and unforeseen small findings. . . . Scientists are increasingly required to provide evidence of immediate and tangible applications of their work.” That is head start fervor come full circle; explorers have to pursue such narrowly specialized goals with such hyperefficiency that they can say what they will find before they look for it.
CONCLUSION: Expanding Your Range
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Original creators tend to strike out a lot, but they also hit mega grand slams, and a baseball analogy doesn’t really do it justice. As business writer Michael Simmons put it, “Baseball has a truncated outcome distribution. When you swing, no matter how well you connect with the ball, the most runs you can get is four.” In the wider world, “every once in a while, when you step up to the plate, you can score 1,000 runs.”
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The formula
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So, about that one sentence of advice: Don’t feel behind.