Psychologist Gary Klein is a pioneer of the “naturalistic decision making” (NDM) model of expertise; NDM researchers observe expert performers in their natural course of work to learn how they make high-stakes decisions under time pressure. Klein has shown that experts in an array of fields are remarkably similar to chess masters in that they instinctively recognize familiar patterns.
When I asked Garry Kasparov, perhaps the greatest chess player in history, to explain his decision process for a move, he told me, “I see a move, a combination, almost instantly,” based on patterns he has seen before. Kasparov said he would bet that grandmasters usually make the move that springs to mind in the first few seconds of thought.
One of Klein’s colleagues, psychologist Daniel Kahneman, studied human decision making from the “heuristics and biases” model of human judgment. His findings could hardly have been more different from Klein’s. When Kahneman probed the judgments of highly trained experts, he often found that experience had not helped at all. Even worse, it frequently bred confidence but not skill.
In those domains, which involved human behavior and where patterns did not clearly repeat, repetition did not cause learning. Chess, golf, and firefighting are exceptions, not the rule.
Do specialists get better with experience, or not?
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. In golf or chess, a ball or piece is moved according to rules and within defined boundaries, a consequence is quickly apparent, and similar challenges occur repeatedly. Drive a golf ball, and it either goes too far or not far enough; it slices, hooks, or flies straight. The player observes what happened, attempts to correct the error, tries again, and repeats for years. That is the very definition of deliberate practice, the type identified with both the ten-thousand-hours rule and the rush to early specialization in technical training. The learning environment is kind because a learner improves simply by engaging in the activity and trying to do better.
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.
In the most devilishly wicked learning environments, experience will reinforce the exact wrong lessons.
There is a saying that “chess is 99 percent tactics.” Tactics are short combinations of moves that players use to get an immediate advantage on the board. When players study all those patterns, they are mastering tactics. Bigger-picture planning in chess—how to manage the little battles to win the war—is called strategy.
Thanks to their calculation power, computers are tactically flawless compared to humans. Grandmasters predict the near future, but computers do it better. What if, Kasparov wondered, computer tactical prowess were combined with human big-picture, strategic thinking?
In 1998, he helped organize the first “advanced chess” tournament, in which each human player, including Kasparov himself, paired with a computer. Years of pattern study were obviated.
His years of repetition would be neutralized, and the contest would shift to one of strategy rather than tactical execution. In chess, it changed the pecking order instantly. “Human creativity was even more paramount under these conditions, not less,” according to Kasparov.
The primary benefit of years of experience with specialized training was outsourced, and in a contest where humans focused on strategy, he suddenly had peers.
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. Those patterns allow expert players to immediately assess the situation based on experience, which is why Garry Kasparov told me that grandmasters usually know their move within seconds.
The reason that elite athletes seem to have superhuman reflexes is that they recognize patterns of ball or body movements that tell them what’s coming before it happens. When tested outside of their sport context, their superhuman reactions disappear.
Studying an enormous number of repetitive patterns is so important in chess that early specialization in technical practice is critical.
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.
Chunking can seem like magic, but it comes from extensive, repetitive practice.
Patterns and familiar structures were critical to the savant’s extraordinary recall ability.
The centaur lesson remains: the more a task shifts to an open world of big-picture strategy, the more humans have to add.
“There are so many layers of thinking,” he said. “We humans sort of suck at all of them individually, but we have some kind of very approximate idea about each of them and can combine them and be somewhat adaptive. That seems to be what the trick is.”
losses.) But the game’s strategic complexity provides a lesson: the bigger the picture, the more unique the potential human contribution. Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.
The progress of AI in the closed and orderly world of chess, with instant feedback and bottomless data, has been exponential. In the rule-bound but messier world of driving, AI has made tremendous progress, but challenges remain. In a truly open-world problem devoid of rigid rules and reams of perfect historical data, AI has been disastrous.
“AI systems are like savants.” They need stable structures and narrow worlds.
When narrow specialization is combined with an unkind domain, the human tendency to rely on experience of familiar patterns can backfire horribly—like the expert firefighters who suddenly make poor choices when faced with a fire in an unfamiliar structure.
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.
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.”
Compared to other scientists, Nobel laureates are at least twenty-two times more likely to partake as an amateur actor, dancer, magician, or other type of performer. Nationally recognized scientists are much more likely than other scientists to be musicians, sculptors, painters, printmakers, woodworkers, mechanics, electronics tinkerers, glassblowers, poets, or writers, of both fiction and nonfiction. And, again, Nobel laureates are far more likely still.
The most successful experts also belong to the wider world. “To him who observes them from afar,” said Spanish Nobel laureate Santiago Ramón y Cajal, the father of modern neuroscience, “it appears as though they are scattering and dissipating their energies, while in reality they are channeling and strengthening them.”
As psychologist and prominent creativity researcher Dean Keith Simonton observed, “rather than obsessively focus[ing] on a narrow topic,” creative achievers tend to have broad interests. “This breadth often supports insights that cannot be attributed to domain-specific expertise alone.”
Connolly’s primary finding was that early in their careers, those who later made successful transitions had broader training and kept multiple “career streams” open even as they pursued a primary specialty. They “traveled on an eight-lane highway,” he wrote, rather than down a single-lane one-way street. They had range. The successful adapters were excellent at taking knowledge from one pursuit and applying it creatively to another, and at avoiding cognitive entrenchment. They employed what Hogarth called a “circuit breaker.” They drew on outside experiences and analogies to interrupt their inclination toward a previous solution that may no longer work. Their skill was in avoiding the same old patterns.
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.
We now see the world through “scientific spectacles.” He means that rather than relying on our own direct experiences, we make sense of reality through classification schemes, using layers of abstract concepts to understand how pieces of information relate to one another.
Modern work demands knowledge transfer: the ability to apply knowledge to new situations and different domains. Our most fundamental thought processes have changed to accommodate increasing complexity and the need to derive new patterns rather than rely only on familiar ones. Our conceptual classification schemes provide a scaffolding for connecting knowledge, making it accessible and flexible.
“Subjects readily shift from one attribute to another and construct suitable categories. They classify objects by substance (animals, flowers, tools), materials (wood, metal, glass), size (large, small), and color (light, dark), or other property. The ability to move freely, to shift from one category to another, is one of the chief characteristics of ‘abstract thinking.’”
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.”*
College departments rush to develop students in a narrow specialty area, while failing to sharpen the tools of thinking that can serve them in every area. This must change, he argues, if students are to capitalize on their unprecedented capacity for abstract thought. They must be taught to think before being taught what to think about.
Three-quarters of American college graduates go on to a career unrelated to their major—a trend that includes math and science majors—after having become competent only with the tools of a single discipline.
One good tool is rarely enough in a complex, interconnected, rapidly changing world.
As statistician Doug Altman put it, “Everyone is so busy doing research they don’t have time to stop and think about the way they’re doing it.”
Wicked world demands—conceptual reasoning skills that can connect new ideas and work across contexts.
The more constrained and repetitive a challenge, the more likely it will be automated, while great rewards will accrue to those who can take conceptual knowledge from one problem or domain and apply it in an entirely new one.
In the genre of modern self-help narratives, music training has stood beside golf atop the podium, exemplars of the power of a narrowly focused head start in highly technical training. Whether it is the story of Tiger Woods or the Yale law professor known as the Tiger Mother, the message is the same: choose early, focus narrowly, never waver.
Parents, Yates told me, increasingly come to him and “want their kids doing what the Olympians are doing right now, not what the Olympians were doing when they were twelve or thirteen,” which included a wider variety of activities that developed their general athleticism and allowed them to probe their talents and interests before they focused narrowly on technical skills. The sampling period is not incidental to the development of great performers—something to be excised in the interest of a head start—it is integral.
A study of music students aged eight to eighteen and ranging in skill from rank novices to students in a highly selective music school found that when they began training there was no difference in the amount of practice undertaken between any of the groups of players, from the least to the most accomplished. The students who would go on to be most successful only started practicing much more once they identified an instrument they wanted to focus on, whether because they were better at it or just liked it more. The instrument, it appeared, was driving the practitioner, rather than the reverse.
When Sloboda and a colleague conducted a study with students at a British boarding school that recruited from around the country—admission rested entirely on an audition—they were surprised to find that the students classified as exceptional by the school came from less musically active families compared to less accomplished students, did not start playing at a younger age, were less likely to have had an instrument in the home at a very young age, had taken fewer lessons prior to entering the school, and had simply practiced less overall before arriving—a lot less. “It seems very clear,” the psychologists wrote, “that sheer amount of lesson or practice time is not a good indicator of exceptionality.” As to structured lessons, every single one of the students who had received a large amount of structured lesson time early in development fell into the “average” skill category, and not one was in the exceptional group. “The strong implication,” the researchers wrote, is “that that too many lessons at a young age may not be helpful.”
“However,” they added, “the distribution of effort across different instruments seems important. Those children identified as exceptional by [the school] turn out to be those children who distributed their effort more evenly across three instruments.” The less skilled students tended to spend their time on the first instrument they picked up, as if they could not give up a perceived head start.
The psychologists highlighted the variety of paths to excellence, but the most common was a sampling period, often lightly structured with some lessons and a breadth of instruments and activities, followed only later by a narrowing of focus, increased structure, and an explosion of practice volume.
The strict deliberate practice school describes useful training as focused consciously on error correction. But the most comprehensive examination of development in improvisational forms, by Duke University professor Paul Berliner, described the childhoods of professionals as “one of osmosis,” not formal instruction.
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.
Improv masters learn like babies: dive in and imitate and improvise first, learn the formal rules later. “At the beginning, your mom didn’t give you a book and say, ‘This is a noun, this is a pronoun, this is a dangling participle,’” Cecchini told me. “You acquired the sound first. And then you acquire the grammar later.”
“It’s easier for a jazz musician to learn to play classical literature than for a classical player to learn how to play jazz,” he said. “The jazz musician is a creative artist, the classical musician is a re-creative artist.”
“Children do not practice exercises to learn to talk. . . . Children learn to read after their ability to talk has been well established.”
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.
In offering advice to parents, psychologist Adam Grant noted that creativity may be difficult to nurture, but it is easy to thwart. He pointed to a study that found an average of six household rules for typical children, compared to one in households with extremely creative children. The parents with creative children made their opinions known after their kids did something they didn’t like, they just did not proscribe it beforehand. Their households were low on prior restraint.
“It’s strange,” Cecchini told me at the end of one of our hours-long discussions, “that some of the greatest musicians were self-taught or never learned to read music. I’m not saying one way is the best, but now I get a lot of students from schools that are teaching jazz, and they all sound the same. They don’t seem to find their own voice. I think when you’re self-taught you experiment more, trying to find the same sound in different places, you learn how to solve problems.”
In every classroom in every country, teachers relied on two main types of questions.
The more common were “using procedures” questions: basically, practice at something that was just learned.
The other common variety was “making connections” questions, which connected students to a broader concept, rather than just a procedure. That was more like when the teacher asked students why the formula works, or made them try to figure out if it works for absolutely any polygon from a triangle to an octagon.
But an important difference emerged in what teachers did after they asked a making-connections problem.
Rather than letting students grapple with some confusion, teachers often responded to their solicitations with hint-giving that morphed a making-connections problem into a using-procedures one.
“We’re very good, humans are, at trying to do the least amount of work that we have to in order to accomplish a task,” Richland told me. Soliciting hints toward a solution is both clever and expedient. The problem is that when it comes to learning concepts that can be broadly wielded, expedience can backfire.
But for learning that is both durable (it sticks) and flexible (it can be applied broadly), fast and easy is precisely the problem.
“Desirable difficulties,” obstacles that make learning more challenging, slower, and more frustrating in the short term, but better in the long term.
One of those desirable difficulties is known as the “generation effect.” Struggling to generate an answer on your own, even a wrong one, enhances subsequent learning. Socrates was apparently on to something when he forced pupils to generate answers rather than bestowing them. It requires the learner to intentionally sacrifice current performance for future benefit.
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.
Used for learning, testing, including self-testing, is a very desirable difficulty. Even testing prior to studying works, at the point when wrong answers are assured.
Struggling to retrieve information primes the brain for subsequent learning, even when the retrieval itself is unsuccessful. The struggle is real, and really useful.
another important desirable difficulty: “spacing,” or distributed practice. 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.
Iowa State researchers read people lists of words, and then asked for each list to be recited back either right away, after fifteen seconds of rehearsal, or after fifteen seconds of doing very simple math problems that prevented rehearsal. The subjects who were allowed to reproduce the lists right after hearing them did the best. Those who had fifteen seconds to rehearse before reciting came in second. The group distracted with math problems finished last. Later, when everyone thought they were finished, they were all surprised with a pop quiz: write down every word you can recall from the lists. Suddenly, the worst group became the best. Short-term rehearsal gave purely short-term benefits. Struggling to hold on to information and then recall it had helped the group distracted by math problems transfer the information from short-term to long-term memory. The group with more and immediate rehearsal opportunity recalled nearly nothing on the pop quiz. Repetition, it turned out, was less important than struggle.
For a given amount of material, learning is most efficient in the long run when it is really inefficient in the short run. If you are doing too well when you test yourself, the simple antidote is to wait longer before practicing the same material again, so that the test will be more difficult when you do. Frustration is not a sign you are not learning, but ease is.
“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.”
Knowledge increasingly needs not merely to be durable, but also flexible—both sticky and capable of broad application.
“Blocked” practice. That is, practicing the same thing repeatedly, each problem employing the same procedure. It leads to excellent immediate performance, but for knowledge to be flexible, it should be learned under varied conditions, an approach called varied or mixed practice, or, to researchers, “interleaving.”
Interleaving has been shown to improve inductive reasoning. When presented with different examples mixed together, students learn to create abstract generalizations that allow them to apply what they learned to material they have never encountered before.
Whether the task is mental or physical, interleaving improves the ability to match the right strategy to a problem. That happens to be a hallmark of expert problem solving. Whether chemists, physicists, or political scientists, the most successful problem solvers spend mental energy figuring out what type of problem they are facing before matching a strategy to it, rather than jumping in with memorized procedures.
Kind learning environment experts choose a strategy and then evaluate; experts in less repetitive environments evaluate and then choose.
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.
Knowledge with enduring utility must be very flexible, composed of mental schemes that can be matched to new problems.
When a knowledge structure is so flexible that it can be applied effectively even in new domains or extremely novel situations, it is called “far transfer.”
Deep analogical thinking is the practice of recognizing conceptual similarities in multiple domains or scenarios that may seem to have little in common on the surface.
Analogical thinking takes the new and makes it familiar, or takes the familiar and puts it in a new light, and allows humans to reason through problems they have never seen in unfamiliar contexts. It also allows us to understand that which we cannot see at all.
Like kind learning environments, a kind world is based on repeating patterns. “It’s perfectly fine,” she said, “if you stay in the same village or the same savannah all your life.” The current world is not so kind; it requires thinking that cannot fall back on previous experience. Like math students, we need to be able to pick a strategy for problems we have never seen before. “In the life we lead today,” Gentner told me, “we need to be reminded of things that are only abstractly or relationally similar. And the more creative you want to be, the more important that is.”
In a wicked world, relying upon experience from a single domain is not only limiting, it can be disastrous.
That it does not help battle the natural impulse to employ the “inside view,” a term coined by psychologists Daniel Kahneman and Amos Tversky. We take the inside view when we make judgments based narrowly on the details of a particular project that are right in front of us.
Our natural inclination to take the inside view can be defeated by following analogies to the “outside view.” The outside view probes for deep structural similarities to the current problem in different ones. The outside view is deeply counterintuitive because it requires a decision maker to ignore unique surface features of the current project, on which they are the expert, and instead look outside for structurally similar analogies. It requires a mindset switch from narrow to broad.
Psychologists have shown repeatedly that the more internal details an individual can be made to consider, the more extreme their judgment becomes.
Focusing narrowly on many fine details specific to a problem at hand feels like the exact right thing to do, when it is often exactly wrong.
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.
Interestingly, if the researchers used only the single film that the movie fans ranked as most analogous to the new release, predictive power collapsed. What seemed like the single best analogy did not do well on its own. Using a full “reference class” of analogies—the pillar of the outside view—was immensely more accurate.
Evaluating an array of options before letting intuition reign is a trick for the wicked world.
Successful problem solvers are more able to determine the deep structure of a problem before they proceed to match a strategy to it. Less successful problem solvers are more like most students in the Ambiguous Sorting Task: they mentally classify problems only by superficial, overtly stated features, like the domain context. For the best performers, they wrote, problem solving “begins with the typing of the problem.”
As education pioneer John Dewey put it in Logic, The Theory of Inquiry, “a problem well put is half-solved.”
Before he began his tortuous march of analogies toward reimagining the universe, Kepler had to get very confused on his homework. Unlike Galileo and Isaac Newton, he documented his confusion.
Dunbar witnessed important breakthroughs live, and saw that the labs most likely to turn unexpected findings into new knowledge for humanity made a lot of analogies, and made them from a variety of base domains. The labs in which scientists had more diverse professional backgrounds were the ones where more and more varied analogies were offered, and where breakthroughs were more reliably produced when the unexpected arose. Those labs were Keplers by committee. They included members with a wide variety of experiences and interests. When the moment came to either dismiss or embrace and grapple with information that puzzled them, they drew on their range to make analogies. Lots of them.
In the face of the unexpected, the range of available analogies helped determine who learned something new.
“When all the members of the laboratory have the same knowledge at their disposal, then when a problem arises, a group of similar minded individuals will not provide more information to make analogies than a single individual,” Dunbar concluded.
There is often no entrenched interest fighting on the side of range, or of knowledge that must be slowly acquired. All forces align to incentivize a head start and early, narrow specialization, even if that is a poor long-term strategy. That is a problem, because another kind of knowledge, perhaps the most important of all, is necessarily slowly acquired—the kind that helps you match yourself to the right challenge in the first place.
It would be easy enough to cherry-pick stories of exceptional late developers overcoming the odds. But they aren’t exceptions by virtue of their late starts, and those late starts did not stack the odds against them. Their late starts were integral to their eventual success.
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.
Winston Churchill’s “never give in, never, never, never, never” is an oft-quoted trope. The end of the sentence is always left out: “except to convictions of honor and good sense.”
Duckworth learned that the Whole Candidate Score—an agglomeration of standardized test scores, high school rank, physical fitness tests, and demonstrated leadership—is the single most important factor for admission, but that it is useless for predicting who will drop out before completing Beast. She had been talking to high performers across domains, and decided to study passion and perseverance, a combination she cleverly formulated as “grit.” She designed a self-assessment that captured the two components of grit. One is essentially work ethic and resilience, and the other is “consistency of interests”—direction, knowing exactly what one wants.
The expression “young and foolish,” he wrote, describes the tendency of young adults to gravitate to risky jobs, but it is not foolish at all. It is ideal. They have less experience than older workers, and so the first avenues they should try are those with high risk and reward, and that have high informational value. Attempting to be a professional athlete or actor or to found a lucrative start-up is unlikely to succeed, but the potential reward is extremely high. Thanks to constant feedback and an unforgiving weed-out process, those who try will learn quickly if they might be a match, at least compared to jobs with less constant feedback. If they aren’t, they go test something else, and continue to gain information about their options and themselves.
Persevering through difficulty is a competitive advantage for any traveler of a long road, but he suggested that knowing when to quit is such a big strategic advantage that every single person, before undertaking an endeavor, should enumerate conditions under which they should quit. The important trick, he said, is staying attuned to whether switching is simply a failure of perseverance, or astute recognition that better matches are available.
The more skilled the Army thought a prospective officer could become, the more likely it was to offer a scholarship. And as those hardworking and talented scholarship recipients blossomed into young professionals, they tended to realize that they had a lot of career options outside the military. Eventually, they decided to go try something else. In other words, they learned things about themselves in their twenties and responded by making match quality decisions.
In the industrial era, or the “company man” era, as the monograph authors called it, “firms were highly specialized,” with employees generally tackling the same suite of challenges repeatedly.
There was little incentive for companies to recruit from outside when employees regularly faced kind learning environments, the type where repetitive experience alone leads to improvement. By the 1980s, corporate culture was changing. The knowledge economy created “overwhelming demand for . . . employees with talents for conceptualization and knowledge creation.” Broad conceptual skills now helped in an array of jobs, and suddenly control over career trajectory shifted from the employer, who looked inward at a ladder of opportunity, to the employee, who peered out at a vast web of possibility. In the private sector, an efficient talent market rapidly emerged as workers shuffled around in pursuit of match quality.
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.’”
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. “The people we study who are fulfilled do pursue a long-term goal, but they only formulate it after a period of discovery,”
Some personality traits change over time in fairly predictable ways. Adults tend to become more agreeable, more conscientious, more emotionally stable, and less neurotic with age, but less open to experience. In middle age, adults grow more consistent and cautious and less curious, open-minded, and inventive.*
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.
Marshmallow test: The longer a child had been able to wait, the more likely she was to be successful socially, academically, and financially, and the less likely she was to abuse drugs.
You are conscientious and neurotic while driving today, it’s a pretty safe bet you will be conscientious and neurotic while driving tomorrow. At the same time . . . you may not be conscientious and neurotic when you are playing Beatles cover songs with your band in the context of the local pub.”
Instead of asking whether someone is gritty, we should ask when they are. “If you get someone into a context that suits them,” Ogas said, “they’ll more likely work hard and it will look like grit from the outside.”
Because personality changes more than we expect with time, experience, and different contexts, we are ill-equipped to make ironclad long-term goals when our past consists of little time, few experiences, and a narrow range of contexts.
We maximize match quality throughout life by sampling activities, social groups, contexts, jobs, careers, and then reflecting and adjusting our personal narratives. And repeat.
“We discover the possibilities by doing, by trying new activities, building new networks, finding new role models.” We learn who we are in practice, not in theory.
Instead of working back from a goal, work forward from promising situations. This is what most successful people actually do anyway.
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.
As schools offering standardized paths in art have proliferated, “one of the problems is that artists tend to be products of those schools,” said Naifeh, an artist himself.
A person don’t know what he can do unless he tries. Trying things is the answer to find your talent.
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.
Yokoi was convinced, though, that if users were drawn into the games, technological power would be an afterthought. “If you draw two circles on a blackboard, and say, ‘That’s a snowman,’ everyone who sees it will sense the white color of the snow,” he argued.
In the Unusual (or Alternative) Uses Task, test takers have to come up with original uses for an object. Given the prompt “brick,” a test taker will generate familiar uses first (part of a wall, a doorstop, a weapon). To score higher, they have to generate uses that are conceptually distant and rarely given by other test takers, but still feasible.
There is a well-documented tendency people have to consider only familiar uses for objects, an instinct known as functional fixedness.
Yokoi was the first to admit it. “I don’t have any particular specialist skills,” he once said. “I have a sort of vague knowledge of everything.” He advised young employees not just to play with technology for its own sake, but to play with ideas. Do not be an engineer, he said, be a producer. “The producer knows that there’s such a thing as a semiconductor, but doesn’t need to know its inner workings. . . . That can be left to the experts.” He argued, “Everyone takes the approach of learning detailed, complex skills. If no one did this then there wouldn’t be people who shine as engineers. . . . Looking at me, from the engineer’s perspective, it’s like, ‘Look at this idiot,’ but once you’ve got a couple hit products under your belt, this word ‘idiot’ seems to slip away somewhere.”
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.”
The world, he wrote, is both broad and deep. “We need birds and frogs working together to explore it.”
The specialists were adept at working for a long time on difficult technical problems, and for anticipating development obstacles. The generalists tended to get bored working in one area for too long. They added value by integrating domains, taking technology from one area and applying it in others. Neither an inventor’s breadth nor their depth alone predicted the likelihood that one of their inventions would win the Carlton Award—the “Nobel Prize of 3M.”
Ouderkirk’s group unearthed one more type of inventor. They called them “polymaths,” broad with at least one area of depth.
Their patents were spread across many classes. The polymaths had depth in a core area—so they had numerous patents in that area—but they were not as deep as the specialists. They also had breadth, even more than the generalists, having worked across dozens of technology classes. Repeatedly, they took expertise accrued in one domain and applied it in a completely new one, which meant they were constantly learning new technologies.
Communication technology has limited the number of hyperspecialists required to work on a particular narrow problem, because their breakthroughs can be communicated quickly and widely to others—the Yokois of the world—who work on clever applications.
“When information became more widely disseminated,” Ouderkirk told me, “it became a lot easier to be broader than a specialist, to start combining things in new ways.”
“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.”
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.
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.
Individual creators started out with lower innovativeness than teams—they were less likely to produce a smash hit—but as their experience broadened they actually surpassed teams: an individual creator who had worked in four or more genres was more innovative than a team whose members had collective experience across the same number of genres. Taylor and Greve suggested that “individuals are capable of more creative integration of diverse experiences than teams are.”
Superman or the Fantastic Four? “When seeking innovation in knowledge-based industries,” they wrote, “it is best to find one ‘super’ individual. If no individual with the necessary combination of diverse knowledge is available, one should form a ‘fantastic’ team.” Diverse experience was impactful when created by platoon in teams, and even more impactful when contained within an individual.
“In product development,” Taylor and Greve concluded, “specialization can be costly.”
University of Utah professor Abbie Griffin has made it her work to study modern Thomas Edisons—“serial innovators,” she and two colleagues termed them. Their findings about who these people are should sound familiar by now:
“high tolerance for ambiguity”
“additional technical knowledge from peripheral domains”
“repurposing what is already available”
“adept at using analogous domains for finding inputs to the invention process”
“ability to connect disparate pieces of information in new ways”
“synthesizing information from many different sources”
“they appear to flit among ideas”
“broad range of interests”
“they read more (and more broadly) than other technologists and have a wider range of outside interests”
“need to learn significantly across multiple domains”
“Serial innovators also need to communicate with various individuals with technical expertise outside of their own domain.”
Darwin always juggled multiple projects, what Gruber called his “network of enterprise.” He had at least 231 scientific pen pals who can be grouped roughly into thirteen broad themes based on his interests, from worms to human sexual selection. He peppered them with questions. He cut up their letters to paste pieces of information in his own notebooks, in which “ideas tumble over each other in a seemingly chaotic fashion.” When his chaotic notebooks became too unwieldy, he tore pages out and filed them by themes of inquiry.
As Gruber wrote, the activities of a creator “may appear, from the outside, as a bewildering miscellany,” but he or she can “map” each activity onto one of the ongoing enterprises. “In some respects,” Gruber concluded, “Charles Darwin’s greatest works represent interpretative compilations of facts first gathered by others.” He was a lateral-thinking integrator.
Their breadth of interests do not neatly fit a rubric. They are “π-shaped people” who dive in and out of multiple specialties.
“Look for wide-ranging interests,” they advised. “Look for multiple hobbies and avocations. . . . When the candidate describes his or her work, does he or she tend to focus on the boundaries and the interfaces with other systems?”
How to identify potential innovators. “We think a lot of them might be frustrated by school,” he said, “because by nature they’re very broad.”
Facing uncertain environments and wicked problems, breadth of experience is invaluable. Facing kind problems, narrow specialization can be remarkably efficient.
Ideally, intellectual sparring partners “hone each other’s arguments so that they are sharper and better,”
There is a particular kind of thinker, one who becomes more entrenched in their single big idea about how the world works even in the face of contrary facts, whose predictions become worse, not better, as they amass information for their mental representation of the world.
Many experts never admitted systematic flaws in their judgment, even in the face of their results. When they succeeded, it was completely on their own merits—their expertise clearly enabled them to figure out the world. When they missed wildly, it was always a near miss; they had certainly understood the situation, they insisted, and if just one little thing had gone differently, they would have nailed it.
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.
The integrators outperformed their colleagues on pretty much everything, but they especially trounced them on long-term predictions.
Narrow-view hedgehogs, who “know one big thing,” and the integrator foxes, who “know many little things.”
Often if you’re too much of an insider, it’s hard to get good perspective.” Eastman described the core trait of the best forecasters to me as: “genuinely curious about, well, really everything.”
Superforecasters’ online interactions are exercises in extremely polite antagonism, disagreeing without being disagreeable. Even on a rare occasion when someone does say, “‘You’re full of beans, that doesn’t make sense to me, explain this,’” Cousins told me, “they don’t mind that.” Agreement is not what they are after; they are after aggregating perspectives, lots of them.
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.
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.
The most science-curious folk always chose to look at new evidence, whether or not it agreed with their current beliefs. Less science-curious adults were like hedgehogs: they became more resistant to contrary evidence and more politically polarized as they gained subject matter knowledge.
It is not what they think, but how they think. The best forecasters are high in active open-mindedness. They are also extremely curious, and don’t merely consider contrary ideas, they proactively cross disciplines looking for them.
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.
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.
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.
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.
“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.
Dropping familiar tools is particularly difficult for experienced professionals who rely on what Weick called overlearned behavior. That is, they have done the same thing in response to the same challenges over and over until the behavior has become so automatic that they no longer even recognize it as a situation-specific tool.
When Weick spoke with hotshot Paul Gleason, one of the best wildland firefighters in the world, Gleason told him that he preferred to view his crew leadership not as decision making, but as sensemaking. “If I make a decision, it is a possession, I take pride in it, I tend to defend it and not listen to those who question it,” Gleason explained. “If I make sense, then this is more dynamic and I listen and I can change it.” He employed what Weick called “hunches held lightly.” Gleason gave decisive directions to his crew, but with transparent rationale and the addendum that the plan was ripe for revision as the team collectively made sense of a fire.
“Congruence” is a social science term for cultural “fit” among an institution’s components—values, goals, vision, self-concepts, and leadership styles. Since the 1980s, congruence has been a pillar of organizational theory. An effective culture is both consistent and strong. When all signals point clearly in the same direction, it promotes self-reinforcing consistency, and people like consistency.
Researchers who studied cultural congruence at 334 institutions of higher education found that it had no influence on any measure of organizational success whatsoever.
The most effective leaders and organizations had range; they were, in effect, paradoxical. They could be demanding and nurturing, orderly and entrepreneurial, even hierarchical and individualistic all at once. A level of ambiguity, it seemed, was not harmful. In decision making, it can broaden an organization’s toolbox in a way that is uniquely valuable.
The experiments showed that an effective problem-solving culture was one that balanced standard practice—whatever it happened to be—with forces that pushed in the opposite direction. If managers were used to process conformity, encouraging individualism helped them to employ “ambidextrous thought,” and learn what worked in each situation. If they were used to improvising, encouraging a sense of loyalty and cohesion did the job. The trick was expanding the organization’s range by identifying the dominant culture and then diversifying it by pushing in the opposite direction.
Prior to Challenger, there was a long span when NASA culture harnessed incongruence. Gene Kranz, the flight director when Apollo 11 first landed on the moon, lived by that same mantra, the valorized process—“In God We Trust, All Others Bring Data”—but he also made a habit of seeking out opinions of technicians and engineers at every level of the hierarchy. If he heard the same hunch twice, it didn’t take data for him to interrupt the usual process and investigate.
“The chain of communication has to be informal,” he told me, “completely different from the chain of command.” He wanted a culture where everyone had the responsibility to protest if something didn’t feel right.
Researchers argued that hierarchical teams benefitted from a clear chain of command, but suffered from a one-way chain of communication that obscured problems. The teams needed elements of both hierarchy and individualism to both excel and survive.
Individuals who live by historian Arnold Toynbee’s words that “no tool is omnicompetent. There is no such thing as a master-key that will unlock all doors.” Rather than wielding a single tool, they have managed to collect and protect an entire toolshed, and they show the power of range in a hyperspecialized world.
“Take your skills to a place that’s not doing the same sort of thing. Take your skills and apply them to a new problem, or take your problem and try completely new skills.”
“A paradox of innovation and mastery is that breakthroughs often occur when you start down a road, but wander off for a ways and pretend as if you have just begun,” Lewis wrote.
“Do we really need to go through courses with very specialized knowledge that often provides a huge amount of stuff that is very detailed, very specialized, very arcane, and will be totally forgotten in a couple of weeks? Especially now, when all the information is on your phone. You have people walking around with all the knowledge of humanity on their phone, but they have no idea how to integrate it. We don’t train people in thinking or reasoning.”
In professional networks that acted as fertile soil for successful groups, individuals moved easily among teams, crossing organizational and disciplinary boundaries and finding new collaborators. Networks that spawned unsuccessful teams, conversely, were broken into small, isolated clusters in which the same people collaborated over and over. Efficient and comfortable, perhaps, but apparently not a creative engine.
New collaborations allow creators “to take ideas that are conventions in one area and bring them into a new area, where they’re suddenly seen as invention,” said sociologist Brian Uzzi, Amaral’s collaborator. Human creativity, he said, is basically an “import/export business of ideas.”
Uzzi documented an import/export trend that began in both the physical and social sciences in the 1970s, pre-internet: more successful teams tended to have more far-flung members. Teams that included members from different institutions were more likely to be successful than those that did not, and teams that included members based in different countries had an advantage as well.
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.
“I always advise my people to read outside your field, everyday something. And most people say, ‘Well, I don’t have time to read outside my field.’ I say, ‘No, you do have time, it’s far more important.’ Your world becomes a bigger world, and maybe there’s a moment in which you make connections.”