Range: Why Generalists Triumph in a Specialized World by David Epstein: Summary & Notes

Summary

A great book on developing broad expertise instead of specializing in a narrow field.

Not only does this provide some welcome respite from the common narrative that "you must specialize early", but it provides context for why broad experience can be a big advantage.

The book provides guidance on finding your optimal work and life, and how to view explorations that might seem inefficient (and how to make the most of them).

Key Points

We are often pushed to focus early, and that is often not the optimal path. The top performers in many fields often have a large "sampling period" where they try many things, and then when they find something they like, they naturally tend to dedicate more time to it.

For those that are interested in many things, or who have interests that change, they are often plagued with the thought "I am so behind", rather than viewing their experience as a unique advantage.

• "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."

Experience leads to expertise only in "kind" environments, where there are repeating patterns, like firefighting and chess.

In "wicked" domains, where the rules are unclear or incomplete, narrow experience doesn't improve outcomes.

• "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."

"Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly."

The other benefit of broad experience is that the more constrained and repetitive a challenge, the more likely it is to be automated in future.

• "great rewards will accrue to those who can take conceptual knowledge from one problem or domain and apply it in an entirely new one"

We should generally try to learn like improve masters: "dive in and imitate and improvise first, learn the formal rules later."

Learning that sticks and can be applied broadly is often slow and frustrating. We need to find ways to incentivize this kind of learning.

• "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."

Three learning strategies with scientific backing: spacing, testing, and using making-connection questions (these all impair performance in the short-term).

"The feeling of learning is based on before-your-eyes progress, while deep learning is not."

Analogical thinking–taking learning and experience from one domain, and applying it to another–allows us to reason through problems that we haven't seen before.

To be a successful problem-solver, you must learn how to determine the deep structure of problems first, and then match a strategy, instead of classifying problems by superficial things like their domain.

• "As education pioneer John Dewey put it in Logic, The Theory of Inquiry, "a problem well put is half-solved.""

We often set goals and objectives based on the theory that we will never change. This is a problem, as we are always changing, yet we don't think we will in the future.

• "Psychologist Dan Gilbert called it the "end of history illusion." From teenagers to senior citizens, we recognize that our desires and motivations sure changed a lot in the past (see: your old hairstyle), but believe they will not change much in the future. In Gilbert’s terms, we are works in progress claiming to be finished."

We can maximize our fit with our work and our life by "sampling activities, social groups, contexts, jobs, careers, and then reflecting and adjusting our personal narratives. And repeat."

• "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?""

Experts are terrible forecasters, and often worse than amateurs because their confidence is much higher.

• "the core trait of the best forecasters to me as: "genuinely curious about, well, really everything.""

"Don't feel behind. Compare yourself to yourself yesterday, not to younger people who aren't you."

Notes

Introduction: Roger vs. Tiger

• The push to focus early and narrowly extends well beyond sports. We are often taught that the more competitive and complicated the world gets, the more specialized we all must become (and the earlier we must start) to navigate it.
• Prominent sports scientist Ross Tucker summed up research in the field simply: "We know that early sampling is key, as is diversity."
• Somehow, a unique advantage had morphed in their heads into a liability.
• I began worrying that I was a job-commitment-phobic drifter who must be doing this whole career thing wrong.
• 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.

Chapter 1: The Cult of the Head Start

• 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.
• Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.
• 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.
• Scientists and members of the general public are about equally likely to have artistic hobbies, but scientists inducted into the highest national academies are much more likely to have avocations outside of their vocation. And those who have won the Nobel Prize are more likely still.

Chapter 2: How the Wicked World Was Made

• 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.

Chapter 3: When Less of the Same Is More

• 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.
• Improv masters learn like babies: dive in and imitate and improvise first, learn the formal rules later.
• 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.

Chapter 4: Learning, Fast and Slow

• For learning that is both durable (it sticks) and flexible (it can be applied broadly), fast and easy is precisely the problem.
• 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.
• 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.
• 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.
• Like a lot of professional development efforts, each particular concept or skill gets a short period of intense focus, and then on to the next thing, never to return. That structure makes intuitive sense, but it forgoes another important desirable difficulty: “spacing," or distributed practice.
• In 2007, the U.S. Department of Education published a report by six scientists and an accomplished teacher who were asked to identify learning strategies that truly have scientific backing. Spacing, testing, and using making-connections questions were on the extremely short list. All three impair performance in the short term.
• 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.
• Unsurprisingly, there was a group of Calculus I professors whose instruction most strongly boosted student performance on the Calculus I exam, and who got sterling student evaluation ratings. Another group of professors consistently added less to student performance on the exam, and students judged them more harshly in evaluations. But when the economists looked at another, longer-term measure of teacher value added—how those students did on subsequent math and engineering courses that required Calculus I as a prerequisite—the results were stunning. The Calculus I teachers who were the best at promoting student overachievement in their own class were somehow not great for their students in the long run. "Professors who excel at promoting contemporaneous student achievement," the economists wrote, "on average, harm the subsequent performance of their students in more advanced classes." What looked like a head start evaporated.
• In a study using college math problems, students who learned in blocks—all examples of a particular type of problem at once—performed a lot worse come test time than students who studied the exact same problems but all mixed up. The blocked-practice students learned procedures for each type of problem through repetition. The mixed-practice students learned how to differentiate types of problems.
• 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."
• Interleaving is a desirable difficulty that frequently holds for both physical and mental skills.
• 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. In that way, they are just about the precise opposite of experts who develop in kind learning environments, like chess masters, who rely heavily on intuition. Kind learning environment experts choose a strategy and then evaluate; experts in less repetitive environments evaluate and then choose.
• The research team recommended that if programs want to impart lasting academic benefits they should focus instead on “open" skills that scaffold later knowledge. Teaching kids to read a little early is not a lasting advantage. Teaching them how to hunt for and connect contextual clues to understand what they read can be. As with all desirable difficulties, the trouble is that a head start comes fast, but deep learning is slow. "The slowest growth," the researchers wrote, "occurs for the most complex skills."
• 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."

Chapter 5: Thinking Outside Experience

• 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. Students might learn about the motion of molecules by analogy to billiard-ball collisions; principles of electricity can be understood with analogies to water flow through plumbing.
• 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," 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.
• 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.
• In 2001, the Boston Consulting Group, one of the most successful in the world, created an intranet site to provide consultants with collections of material to facilitate wide-ranging analogical thinking. The interactive “exhibits" were sorted by discipline (anthropology, psychology, history, and others), concept (change, logistics, productivity, and so on), and strategic theme (competition, cooperation, unions and alliances, and more).
• If that all sounds incredibly remote from pressing business concerns, that is exactly the point.
• In one of the most cited studies of expert problem solving ever conducted, an interdisciplinary team of scientists came to a pretty simple conclusion: 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."

Chapter 6: The Trouble with Too Much Grit

• "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.
• Seth Godin, author of some of the most popular career writing in the world, wrote a book disparaging the idea that "quitters never win." Godin argued that "winners"—he generally meant individuals who reach the apex of their domain—quit fast and often when they detect that a plan is not the best fit, and do not feel bad about it. "We fail," he wrote, "when we stick with tasks we don’t have the guts to quit." Godin clearly did not advocate quitting simply because a pursuit is difficult.
• A recent international Gallup survey of more than two hundred thousand workers in 150 countries reported that 85 percent were either "not engaged" with their work or "actively disengaged." In that condition, according to Seth Godin, quitting takes a lot more guts than continuing to be carried along like debris on an ocean wave. The trouble, Godin noted, is that humans are bedeviled by the "sunk cost fallacy." Having invested time or money in something, we are loath to leave it, because that would mean we had wasted our time or money, even though it is already gone.

Chapter 7: Flirting with Your Possible Selves

• 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.
• 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.
• Psychologist Dan Gilbert called it the "end of history illusion." From teenagers to senior citizens, we recognize that our desires and motivations sure changed a lot in the past (see: your old hairstyle), but believe they will not change much in the future. In Gilbert’s terms, we are works in progress claiming to be finished.
• 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."
• When she compiled her findings, the central premise was at once simple and profound: 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.
• 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.
• Ibarra’s advice is nearly identical to the short-term planning the Dark Horse researchers documented. 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."

Chapter 8: The Outsider Advantage

• Lakhani: "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."

Chapter 9: Lateral Thinking with Withered Technology

• Eminent physicist and mathematician Freeman Dyson styled it this way: we need both focused frogs and visionary birds. "Birds fly high in the air and survey broad vistas of mathematics out to the far horizon," Dyson wrote in 2009. "They delight in concepts that unify our thinking and bring together diverse problems from different parts of the landscape. Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects, and they solve problems one at a time." As a mathematician, Dyson labeled himself a frog, but contended, “It is stupid to claim that birds are better than frogs because they see farther, or that frogs are better than birds because they see deeper.” The world, he wrote, is both broad and deep. “We need birds and frogs working together to explore it."
• They examined patents, and with Ouderkirk’s internal access to 3M, the actual commercial impact inventors made. The specialists and the generalists, they found, both made contributions. One was not uniformly superior to the other.
• Ouderkirk’s group unearthed one more type of inventor. They called them “polymaths," broad with at least one area of depth.
• 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.
• 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.
• 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."
• Toward the end of their book Serial Innovators, Abbie Griffin and her coauthors depart from stoically sharing their data and observations and offer advice to human resources managers. They are concerned that HR policies at mature companies have such well-defined, specialized slots for employees that potential serial innovators will look like "round pegs to the square holes" and get screened out. 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?"

Chapter 10: Fooled by Expertise

• 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. When experts declared that some future event was impossible or nearly impossible, it nonetheless occurred 15 percent of the time. When they declared a sure thing, it failed to transpire more than one-quarter of the time. The Danish proverb that warns "It is difficult to make predictions, especially about the future," was right. Dilettantes who were pitted against the experts were no more clairvoyant, but at least they were less likely to call future events either impossible or sure things, leaving them with fewer laugh-out-loud errors to atone for—if, that was, the experts had believed in atonement.
• 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. "There is often a curiously inverse relationship," Tetlock concluded, "between how well forecasters thought they were doing and how well they did."
• There was also a "perverse inverse relationship" between fame and accuracy.
• The integrators outperformed their colleagues on pretty much everything, but they especially trounced them on long-term predictions. Eventually, Tetlock conferred nicknames (borrowed from philosopher Isaiah Berlin) that became famous throughout the psychology and intelligence-gathering communities: the narrow-view hedgehogs, who "know one big thing," and the integrator foxes, who "know many little things."
• Eastman described the core trait of the best forecasters to me as: "genuinely curious about, well, really everything."
• 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.
• 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
• Basically, forecasters can improve by generating a list of separate events with deep structural similarities, rather than focusing only on internal details of the specific event in question. Few events are 100 percent novel—uniqueness is a matter of degree, as Tetlock puts it—and creating the list forces a forecaster implicitly to think like a statistician.
• 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.

Chapter 11: Learning to Drop Your Familiar Tools

• 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.
• She found that 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.
• Business school students are widely taught to believe the congruence model, that a good manager can always align every element of work into a culture where all influences are mutually reinforcing—whether toward cohesion or individualism. But cultures can actually be too internally consistent. With incongruence, "you’re building in cross-checks," Tetlock told me.
• Wernher von Braun, who led the Marshall Space Flight Center’s development of the rocket that propelled the moon mission, balanced NASA’s rigid process with an informal, individualistic culture that encouraged constant dissent and cross-boundary communication. Von Braun started "Monday Notes": every week engineers submitted a single page of notes on their salient issues. Von Braun hand-wrote comments in the margins, and then circulated the entire compilation. Everyone saw what other divisions were up to, and how easily problems could be raised. Monday Notes were rigorous, but informal.
• Geveden saw everywhere a collective culture that nudged conflict into darkened corners. "You almost couldn’t go into a meeting without someone saying, ‘Let’s take that offline,’" he recalled, just as Morton Thiokol had done for the infamous offline caucus.

Chapter 12: Deliberate Amateurs

• At its core, all hyperspecialization is a well-meaning drive for efficiency—the most efficient way to develop a sports skill, assemble a product, learn to play an instrument, or work on a new technology. But inefficiency needs cultivating too. The wisdom of a Polgar-like method of laser-focused, efficient development is limited to narrowly constructed, kind learning environments.
• "When you push the boundaries, a lot of it is just probing. It has to be inefficient," Casadevall told me. "What’s gone totally is that time to talk and synthesize. People grab lunch and bring it into their offices. They feel lunch is inefficient, but often that’s the best time to bounce ideas and make connections."

Conclusion: Expanding Your Range

• So, about that one sentence of advice: Don’t feel behind.
• 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. Instead, as Herminia Ibarra suggested for the proactive pursuit of match quality, start planning experiments. Your personal version of Friday night or Saturday morning experiments, perhaps.
• Approach your own personal voyage and projects like Michelangelo approached a block of marble, willing to learn and adjust as you go, and even to abandon a previous goal and change directions entirely should the need arise.
• Finally, remember that there is nothing inherently wrong with specialization. We all specialize to one degree or another, at some point or other. As Supreme Court justice Oliver Wendell Holmes wrote a century ago, of the free exchange of ideas, "It is an experiment, as all life is an experiment."