LLM Book Knowledgebase

Range: Why Generalists Triumph in a Specialized World

44 min read

Range: Why Generalists Triumph in a Specialized World

Metadata

Highlights & Notes

And he refused to specialize in anything, preferring to keep an eye on the overall estate rather than any of its parts… . And Nikolay’s management produced the most brilliant results. —Leo Tolstoy, War and Peace No tool is omnicompetent. There is no such thing as a master-key that will unlock all doors. —Arnold Toynbee, A Study of History

Apparently the idea of an athlete, even one who wants to become elite, following a Roger path and trying different sports is not so absurd. Elite athletes at the peak of their abilities do spend more time on focused, deliberate practice than their near-elite peers.

Eventual elites typically devote less time early on to deliberate practice in the activity in which they will eventually become experts. Instead, they undergo what researchers call a “sampling period.” They play a variety of sports, usually in an unstructured or lightly structured environment; they gain a range of physical proficiencies from which they can draw; they learn about their own abilities and proclivities; and only later do they focus in and ramp up technical practice in one area.

I dove into work showing that highly credentialed experts can become so narrow-minded that they actually get worse with experience, even while becoming more confident—a dangerous combination. And 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.

Overspecialization can lead to collective tragedy even when every individual separately takes the most reasonable course of action.

increasing specialization has created a “system of parallel trenches” in the quest for innovation. Everyone is digging deeper into their own trench and rarely standing up to look in the next trench over, even though the solution to their problem happens to reside there.

The challenge we all face is how to maintain the benefits of breadth, diverse experience, interdisciplinary thinking, and delayed concentration in a world that increasingly incentivizes, even demands, hyperspecialization.

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.

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.

Expert firefighters, when faced with a new situation, like a fire in a skyscraper, can find themselves suddenly deprived of the intuition formed in years of house fires, and prone to poor decisions. With a change of the status quo, chess masters too can find that the skill they took years to build is suddenly obsolete.

“Anything we can do, and we know how to do it, machines will do it better,” he said at a recent lecture. “If we can codify it, and pass it to computers, they will do it better.” Still, losing to Deep Blue gave him an idea. In playing computers, he recognized what artificial intelligence scholars call Moravec’s paradox: machines and humans frequently have opposite strengths and weaknesses.

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?

The machine partner could handle tactics so the human could focus on strategy.

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.

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.

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.

the more a task shifts to an open world of big-picture strategy, the more humans have to add.

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.

“In narrow enough worlds, humans may not have much to contribute much longer. In more open-ended games, I think they certainly will. Not just games, in open ended real-world problems we’re still crushing the machines.”

“AI systems are like savants.” They need stable structures and narrow worlds. When we know the rules and answers, and they don’t change over time—chess, golf, playing classical music—an argument can be made for savant-like hyperspecialized practice from day one. But those are poor models of most things humans want to learn.

“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.”

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.

To use a common metaphor, premodern people miss the forest for the trees; modern people miss the trees for the forest.

The more they had moved toward modernity, the more powerful their abstract thinking, and the less they had to rely on their concrete experience of the world as a reference point.

In Flynn’s terms, 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. We have grown up in a world of classification schemes totally foreign to the remote villagers; we classify some animals as mammals, and inside of that class make more detailed connections based on the similarity of their physiology and DNA. Words that represent concepts that were previously the domain of scholars became widely understood in a few generations. The word “percent” was almost absent from books in 1900. By 2000 it appeared about once every five thousand words. (This chapter is 5,500 words long.) Computer programmers pile layers of abstraction. (They do very well on Raven’s.) In the progress bar on your computer screen that fills up to indicate a download, abstractions are legion, from the fundamental—the programming language that created it is a representation of binary code, the raw 1s and 0s the computer uses—to the psychological: the bar is a visual projection of time that provides peace of mind by estimating the progress of an immense number of underlying activities.

Exposure to the modern world has made us better adapted for complexity, and that has manifested as flexibility, with profound implications for the breadth of our intellectual world. In every cognitive direction, the minds of premodern citizens were severely constrained by the concrete world before them.

a city dweller traveling through the desert will be completely dependent on a nomad to keep him alive. So long as they remain in the desert, the nomad is a genius. But it is certainly true that modern life requires range, making connections across far-flung domains and ideas.

“the traits that earn good grades at [the university] do not include critical ability of any broad significance.”*

everyone needs habits of mind that allow them to dance across disciplines.

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. Students come prepared with scientific spectacles, but do not leave carrying a scientific-reasoning Swiss Army knife.

“Computational thinking is using abstraction and decomposition when attacking a large complex task,” she wrote. “It is choosing an appropriate representation for a problem.”

several studies have found that a little training in broad thinking strategies, like Fermi-izing, can go a long way, and can be applied across domains. Unsurprisingly, Fermi problems were a topic in the “Calling Bullshit” course. It used a deceptive cable news report as a case study to demonstrate “how Fermi estimation can cut through bullshit like a hot knife through butter.”

Like chess masters and firefighters, premodern villagers relied on things being the same tomorrow as they were yesterday. They were extremely well prepared for what they had experienced before, and extremely poorly equipped for everything else. Their very thinking was highly specialized in a manner that the modern world has been telling us is increasingly obsolete. They were perfectly capable of learning from experience, but failed at learning without experience. And that is what a rapidly changing, wicked world demands—conceptual reasoning skills that can connect new ideas and work across contexts. Faced with any problem they had not directly experienced before, the remote villagers were completely lost. That is not an option for us. 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. The ability to apply knowledge broadly comes from broad training. A particular skilled group of performers in another place and time turned broad training into an art form. Their story is older, and yet a much better parable than chess prodigies for the modern age.

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.

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.”

While I was sitting with Cecchini, he reeled off an impressive improvisation. I asked him to repeat it so I could record it. “I couldn’t play that again if you put a gun to my head,” he said. 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.

“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 offering advice to parents, psychologist Adam Grant noted that creativity may be difficult to nurture, but it is easy to thwart.

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.”

“I could show somebody in two minutes what would take them years of screwing around on the fingerboard like I did to find it. You don’t know what’s right or what’s wrong. You don’t have that in your head. You’re just trying to find a solution to problems, and after fifty lifetimes, it starts to come together for you. It’s slow,” he told me, “but at the same time, there’s something to learning that way.”

Kornell was explaining the concept of “desirable difficulties,” obstacles that make learning more challenging, slower, and more frustrating in the short term, but better in the long term. Excessive hint-giving, like in the eighth-grade math classroom, does the opposite; it bolsters immediate performance, but undermines progress in the long run. Several desirable difficulties that can be used in the classroom are among the most rigorously supported methods of enhancing learning, and the engaging eighth-grade math teacher accidentally subverted all of them in the well-intended interest of before-your-eyes progress. 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.

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.*

Training without hints is slow and error-ridden. It is, essentially, what we normally think of as testing, except for the purpose of learning rather than evaluation—when “test” becomes a dreaded four-letter word.

Struggling to retrieve information primes the brain for subsequent learning, even when the retrieval itself is unsuccessful. The struggle is real, and really useful. “Like life,” Kornell and team wrote, “retrieval is all about the journey.”

Repetition, it turned out, was less important than struggle. It isn’t bad to get an answer right while studying. Progress just should not happen too quickly, unless the learner wants to end up like Oberon (or, worse, Macduff), with a knowledge mirage that evaporates when it matters most.

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.

As with the making-connections questions Richland studied, it is difficult to accept that the best learning road is slow, and that doing poorly now is essential for better performance later. It is so deeply counterintuitive that it fools the learners themselves, both about their own progress and their teachers’ skill.

The feeling of learning, it turns out, is based on before-your-eyes progress, while deep learning is not. “When your intuition says block,” Kornell told me, “you should probably interleave.”

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.

Learning deeply means learning slowly. The cult of the head start fails the learners it seeks to serve. 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.”

“I especially love analogies,” he wrote, “my most faithful masters, acquainted with all the secrets of nature… . One should make great use of them.”

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.

“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.”

gift of a single analogy from a different domain tripled the proportion of solvers who got the radiation problem. Two analogies from disparate domains gave an even bigger boost. The impact of the fortress story alone was as large as if solvers were just straight out told this guiding principle: “If you need a large force to accomplish some purpose, but are prevented from applying such a force directly, many smaller forces applied simultaneously from different directions may work just as well.”

The trouble with using no more than a single analogy, particularly one from a very similar situation, is that it does not help battle the natural impulse to employ the “inside view,”

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.

In one famous study, participants judged an individual as more likely to die from “heart disease, cancer, or other natural causes” than from “natural causes.” 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.

Using a full “reference class” of analogies—the pillar of the outside view—was immensely more accurate.

Just being reminded to analogize widely made the business students more creative. Unfortunately, students also said that if they were to use analogy companies at all, they believed the best way to generate strategic options would be to focus on a single example in the same field. Like the venture capitalists, their intuition was to use too few analogies, and to rely on those that were the most superficially similar. “That’s usually exactly the wrong way to go about it regardless of what you’re using analogy for,” Lovallo told me.

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.”

Faced with an unexpected finding, rather than assuming the current theory is correct and that an observation must be off, the unexpected became an opportunity to venture somewhere new—and analogies served as the wilderness guide.

The first fifteen minutes could be housekeeping—whose turn it was to order supplies, or who had left a mess. Then the action started. Someone presented an unexpected or confusing finding, their version of Kepler’s Mars orbit. Prudently, scientists’ first instinct was to blame themselves, some error in calculation or poorly calibrated equipment. If it kept up, the lab accepted the result as real, and ideas about what to try and what might be going on started flying. Every hour of lab meeting Dunbar recorded required eight hours of transcribing and labeling problem-solving behaviors so that he could analyze the process of scientific creativity, and he found an analogy fest.

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.

Whether it is the making-connections knowledge Lindsey Richland studied, or the broad concepts that Flynn tested, or the distant, deep structural analogical reasoning that Gentner assessed, 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.

They all appear to have excelled in spite of their late starts. 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.

“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.

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.”

Switchers are winners. It seems to fly in the face of hoary adages about quitting, and of far newer concepts in modern psychology.

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.

“We fail,” he wrote, when we stick with “tasks we don’t have the guts to 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.

Very young people often have their goals set for them, or at least have a limited menu to choose from, and pursuing them with passion and resilience is the main challenge.

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.

The crux was that some unanticipated experience had led to an unexpected new goal or the discovery of an unexplored talent.

No one in their right mind would argue that passion and perseverance are unimportant, or that a bad day is a cue to quit. But the idea that a change of interest, or a recalibration of focus, is an imperfection and competitive disadvantage leads to a simple, one-size-fits-all Tiger story: pick and stick, as soon as possible. Responding to lived experience with a change of direction, like Van Gogh did habitually, like West Point graduates have been doing since the dawn of the knowledge economy, is less tidy but no less important. It involves a particular behavior that improves your chances of finding the best match, but that at first blush sounds like a terrible life strategy: short-term planning.

“I did not intend to become a leader, I just learned by doing what was needed at the time.”

“You have to carry a big basket to bring something home.” She repeats that phrase today, to mean that a mind kept wide open will take something from every new experience.

“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.’”

“Short-term planning,” Ogas told me. “They all practice it, not long-term planning.” Even people who look like consummate long-term visionaries from afar usually looked like short-term planners up close.

Career goals that once felt safe and certain can appear ludicrous, to use Darwin’s adjective, when examined in the light of more self-knowledge. Our work preferences and our life preferences do not stay the same, because we do not stay the same.

The precise person you are now is fleeting, just like all the other people you’ve been.

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.*

At a given point in life, an individual’s nature influences how they respond to a particular situation, but their nature can appear surprisingly different in some other situation. With Mischel, he began to study “if-then signatures.” If David is at a giant party, then he seems introverted, but if David is with his team at work, then he seems extroverted. (True.) So is David introverted or extroverted? Well, both, and consistently so.

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.”

we learn who we are only by living, and not before. Ibarra concluded that we maximize match quality throughout life by sampling activities, social groups, contexts, jobs, careers, and then reflecting and adjusting our personal narratives. And repeat.

“First act and then think.” Ibarra marshaled social psychology to argue persuasively that we are each made up of numerous possibilities. As she put it, “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.

Themes emerged in the transitions. The protagonists had begun to feel unfulfilled by their work, and then a chance encounter with some world previously invisible to them led to a series of short-term explorations. At first, all career changers fell prey to the cult of the head start and figured it couldn’t possibly make sense to dispense with their long-term plans in favor of rapidly evolving short-term experiments. Sometimes they tried to talk themselves out of it. Their confidants advised them not to do anything rash; don’t change now, they said, just keep the new interest or talent as a hobby. But the more they dabbled, the more certain they were that it was time for a change. A new work identity did not manifest overnight, but began with trying something temporary, Hesselbein style, or finding a new role model, then reflecting on the experience and moving to the next short-term plan. Some career changers got richer, others poorer; all felt temporarily behind, but as in the Freakonomics coin-flip study, they were happier with a change.

Rather than expecting an ironclad a priori answer to “Who do I really want to become?,” their work indicated that it is better to be a scientist of yourself, asking smaller questions that can actually be tested—“Which among my various possible selves should I start to explore now? How can I do that?” Be a flirt with your possible selves.* Rather than a grand plan, find experiments that can be undertaken quickly. “Test-and-learn,” Ibarra told me, “not plan-and-implement.”

“My passion for the sport hasn’t waned,” she said when she retired, “but my passion for new experiences and new challenges is what is now burning the most brightly.”

I was greeted by a giant self-portrait of a smirking Finster in a burgundy suit, affixed to a cinderblock wall. At the bottom are the words “I began painting pictures in Jan-1976—without any training. This is my painting. A person don’t know what he can do unless he tryes. Trying things is the answer to find your talent.”

“Sometimes you just slap your head and go, ‘Well why didn’t I think of that?’ If it was easily solved by people within the industry, it would have been solved by people within the industry,” Pegau said. “I think it happens more often than we’d love to admit, because we tend to view things with all the information we’ve gathered in our industry, and sometimes that puts us down a path that goes into a wall. It’s hard to back up and find another path.” Pegau was basically describing the Einstellung effect, a psychology term for the tendency of problem solvers to employ only familiar methods even if better ones are available.

“the further the problem was from the solver’s expertise, the more likely they were to solve it.” As organizational boxes get smaller and smaller, and as outsiders are more easily engaged online, “exploration [of new solutions] now increasingly resides outside the boundaries of the traditional firm,”

“Big innovation most often happens when an outsider who may be far away from the surface of the problem reframes the problem in a way that unlocks the solution.”

“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.”

“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.”

key to creative problem solving is tapping outsiders who use different approaches “so that the ‘home field’ for the problem does not end up constraining the solution.” Sometimes, the home field can be so constrained that a curious outsider is truly the only one who can see the solution.

The more information specialists create, the more opportunity exists for curious dilettantes to contribute by merging strands of widely available but disparate information—undiscovered public knowledge,

The heart of his philosophy was putting cheap, simple technology to use in ways no one else considered. If he could not think more deeply about new technologies, he decided, he would think more broadly about old ones. He intentionally retreated from the cutting edge, and set to monozukuri.

What its withered technology lacked, the Game Boy made up in user experience. It was cheap. It could fit in a large pocket. It was all but indestructible. If a drop cracked the screen—and it had to be a horrific drop—it kept on ticking. If it were left in a backpack that went in the washing machine, once it dried out it was ready to roll a few days later. Unlike its power-guzzling color competitors, it played for days (or weeks) on AA batteries. Old hardware was extremely familiar to developers inside and outside Nintendo, and with their creativity and speed unencumbered by learning new technology, they pumped out games as if they were early ancestors of iPhone app designers—Tetris, Super Mario Land, The Final Fantasy Legend, and a slew of sports games released in the first year were all smash hits. With simple technology, Yokoi’s team sidestepped the hardware arms race and drew the game programming community onto its team.

When the Game Boy was released, Yokoi’s colleague came to him “with a grim expression on his face,” Yokoi recalled, and reported that a competitor handheld had hit the market. Yokoi asked him if it had a color screen. The man said that it did. “Then we’re fine,” Yokoi replied.

There is, to be sure, no comprehensive theory of creativity. But there is a well-documented tendency people have to consider only familiar uses for objects, an instinct known as functional fixedness. The most famous example is the “candle problem,” in which participants are given a candle, a box of tacks, and a book of matches and told to attach the candle to the wall such that wax doesn’t drip on the table below. Solvers try to melt the candle to the wall or tack it up somehow, neither of which work. When the problem is presented with the tacks outside of their box, solvers are more likely to view the empty box as a potential candle holder, and to solve the problem by tacking it to the wall and placing the candle inside. For Yokoi, the tacks were always outside the box.

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.”

consider alternate uses for old technology.

As the company grew, he worried that young engineers would be too concerned about looking stupid to share ideas for novel uses of old technology, so he began intentionally blurting out crazy ideas at meetings to set the tone. “Once a young person starts saying things like, ‘Well, it’s not really my place to say …’ then it’s all over,” he said.

He needed specialists, but his concern was that as companies grew and technology progressed, vertical-thinking hyperspecialists would continue to be valued but lateral-thinking generalists would not. “The shortcut [for a lack of ideas] is competition in the realm of computing power,” Yokoi explained. “When it comes to that … the screen manufacturers and expert graphics designers come out on top. Then Nintendo’s reason for existence disappears.” He felt that the lateral and vertical thinkers were best together, even in highly technical fields.

Ouderkirk’s data suggest that something analogous happened for narrowly focused specialists in technical fields. They are still absolutely critical, it’s just that their work is widely accessible, so fewer suffice.

Ouderkirk told me, “it became a lot easier to be broader than a specialist, to start combining things in new ways.”

Specialization is obvious: keep going straight. Breadth is trickier to grow.

Seeding the soil for generalists and polymaths who integrate knowledge takes more than money. It takes opportunity.

“My inclination is to attack a problem by building a narrative. I figure out the fundamental questions to ask, and if you ask those questions of the people who actually do know their stuff, you are still exactly where you would be if you had all this other knowledge inherently. It’s mosaic building. I just keep putting those tiles together. Imagine me in a network where I didn’t have the ability to access all these people. That really wouldn’t work well.”

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.

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.” They titled their study 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.”

Their findings about who these people are should sound familiar by now: “high tolerance for ambiguity”; “systems thinkers”; “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.” Get the picture?

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.

Ideally, intellectual sparring partners “hone each other’s arguments so that they are sharper and better,”

As each man amassed more information for his own view, each became more dogmatic, and the inadequacies in their models of the world more stark.

The average expert was a horrific forecaster. Their areas of specialty, years of experience, academic degrees, and even (for some) access to classified information made no difference. They were bad at short-term forecasting, bad at long-term forecasting, and bad at forecasting in every domain.

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. Or, like Ehrlich, their understanding was correct; the timeline was just a bit off. Victories were total victories, and defeats were always just a touch of bad luck away from having been victories too. Experts remained undefeated while losing constantly.

Hedgehog experts were deep but narrow. Some had spent their careers studying a single problem.

The hedgehogs, according to Tetlock, “toil devotedly” within one tradition of their specialty, “and reach for formulaic solutions to ill-defined problems.”

The foxes, meanwhile, “draw from an eclectic array of traditions, and accept ambiguity and contradiction,” Tetlock wrote. Where hedgehogs represented narrowness, foxes ranged outside a single discipline or theory and embodied breadth.

He drew on economics, political science, and history to make one quick point about a current debate in psychology, and then stopped on a dime and noted, “But if your assumptions about human nature and how a good society needs to be structured are different, you would see this completely differently.” When a new idea entered the conversation, he was quick with “Let’s say for the sake of argument,” which led to him playing out viewpoints from different disciplines or political or emotional perspectives. He tried on ideas like Instagram filters until it was hard to tell which he actually believed.

The foxiest forecasters were impressive alone, but together they exemplified the most lofty ideal of teams: they became more than the sum of their parts. A lot more.

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.”

Narrow experts are an invaluable resource, she told me, “but you have to understand that they may have blinders on. So what I try to do is take facts from them, not opinions.” Like polymath inventors, Eastman and Cousins take ravenously from specialists and integrate.

In contrast to politicians, the most adept predictors flip-flop like crazy.

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.

It is not that we are unable to come up with contrary ideas, it is just that our strong instinct is not to.

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. They recognize that they are operating in the very definition of a wicked learning environment, where it can be very hard to learn, from either wins or losses.

In wicked domains that lack automatic feedback, experience alone does not improve performance. Effective habits of mind are more important, and they can be developed.

When an outcome took them by surprise, however, foxes were much more likely to adjust their ideas. Hedgehogs barely budged. Some hedgehogs made authoritative predictions that turned out wildly wrong, and then updated their theories in the wrong direction. They became even more convinced of the original beliefs that led them astray. “Good judges are good belief updaters,” according to Tetlock. If they make a bet and lose, they embrace the logic of a loss just as they would the reinforcement of a win. That is called, in a word: learning. Sometimes, it involves putting experience aside entirely.

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.

Reason without numbers was not accepted. In the face of an unfamiliar challenge, NASA managers failed to drop their familiar tools.

“Dropping one’s tools is a proxy for unlearning, for adaptation, for flexibility,” Weick wrote. “It is the very unwillingness of people to drop their tools that turns some of these dramas into tragedies.”

Rather than adapting to unfamiliar situations, whether airline accidents or fire tragedies, Weick saw that experienced groups became rigid under pressure and “regress to what they know best.” They behaved like a collective hedgehog, bending an unfamiliar situation to a familiar comfort zone, as if trying to will it to become something they actually had experienced before.

“When a firefighter is told to drop his firefighting tools, he is told to forget he is a firefighter.” Weick explained that wildland firefighters have a firm “can do” culture, and dropping tools was not part of it, because it meant they had lost control.

After the tragedy, it emerged that other engineers on the teleconference agreed with Boisjoly, but knew they could not muster quantitative arguments, so they remained silent. Their silence was taken as consent. As one engineer who was on the Challenger conference call later said, “If I feel like I don’t have data to back me up, the boss’s opinion is better than mine.” 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.

The process culminated with more concern for being able to defend a decision than with using all available information to make the right one. Like the firefighters, NASA managers had merged with their tools. As McDonald said, looking only at the quantitative data actually supported NASA’s stance that there was no link between temperature and failure. NASA’s normal quantitative standard was a dearly held tool, but the wrong one for the job. That night, it should have been dropped.

“When you don’t have any data,” Feynman said, “you have to use reason.”

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.

The trick was expanding the organization’s range by identifying the dominant culture and then diversifying it by pushing in the opposite direction.

there is a difference between the chain of command and the chain of communication, and that the difference represents a healthy cross-pressure. “I warned them, I’m going to communicate with all levels of the organization down to the shop floor, and you can’t feel suspicious or paranoid about that,” he said. “I told them I will not intercept your decisions that belong in your chain of command, but I will give and receive information anywhere in the organization, at any time. I just can’t get enough understanding of the organization from listening to the voices at the top.”

It is a difficult balancing act, cultivating aspects of a culture that seem on their face to push against one another. There are no rules for the qualitative hunches of space shuttle engineers or pararescue jumpers lacking intel. Incongruence, as the experimental research testified, helps people to discover useful cues, and to drop the traditional tools when it makes sense.

Seeing small pieces of a larger jigsaw puzzle in isolation, no matter how hi-def the picture, is insufficient to grapple with humanity’s greatest challenges. We have long known the laws of thermodynamics, but struggle to predict the spread of a forest fire. We know how cells work, but can’t predict the poetry that will be written by a human made up of them. The frog’s-eye view of individual parts is not enough. A healthy ecosystem needs biodiversity. Even now, even in endeavors that engender specialization unprecedented in history, there are beacons of breadth. 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.

One needs to let the brain think about something different from its daily work,

‘Don’t end up a clone of your thesis adviser,’” he told me. “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.”

An enthusiastic, even childish, playful streak is a recurring theme in research on creative thinkers.

“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.”

The interface between specialties, and between creators with disparate backgrounds, has been studied, and it is worth defending.

Consistent with the import/export model, scientists who have worked abroad—whether or not they returned—are more likely to make a greater scientific impact than those who have not. The economists who documented that trend suggested one reason could be migrants’ “arbitrage” opportunities, the chance to take an idea from one market and bring it to another where it is more rare and valued.* It echoes Oliver Smithies’s advice to bring new skills to an old problem, or a new problem to old skills. The atypical combination of typical forms—say, hip-hop, a Broadway musical, and American historical biography—is not a strategy fluke of showbiz.

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.

“Scientific progress on a broad front results from the free play of free intellects, working on subjects of their own choice,” Bush wrote, “in the manner dictated by their curiosity for exploration of the unknown.”

That’s how it goes on the disorderly path of experimentation. 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.” It doesn’t mean breakthrough creation is luck, although that helps, but rather that it is hard and inconsistent. Going where no one has is a wicked problem. There is no well-defined formula or perfect system of feedback to follow. It’s like the stock market that way; if you want the sky highs, you have to tolerate a lot of lows. As InnoCentive founder Alph Bingham told me, “breakthrough and fallacy look a lot alike initially.”

Early in the book, I discussed athletes and musicians, because they are practically synonymous with early specialization. But among athletes who go on to become elite, broad early experience and delayed specialization is the norm.

Compare yourself to yourself yesterday, not to younger people who aren’t you. Everyone progresses at a different rate, so don’t let anyone else make you feel behind. You probably don’t even know where exactly you’re going, so feeling behind doesn’t help.

Even when you move on from an area of work or an entire domain, that experience is not wasted.

“It is an experiment, as all life is an experiment.”

“A jack-of-all-trades is a master of none, but oftentimes better than a master of one.”

Once again: we learn who we are in practice, not in theory.

I hope that will help guide him to good match quality, broaden his toolbox en route, and form a habit of regular reflection reminiscent of the dark horses from chapter seven, who repeatedly say to themselves, “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.

‘Mom, why do we make “What I want to be when I grow up” signs on the first day of school? We should make “Top 5 things I want to learn about this year” signs.’ Smart cookie. :-)” I think I’ll borrow the twelve-year-old’s idea.

“Success in the knowledge economy comes to those who know themselves—their strengths, their values, and how they best perform.”

Backlinks