Range

David J. Epstein

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This book had wide-ranging research, but the conclusions felt cherry-picked and hard to prove / disprove. It's worth reading because everyone else will. And for the stories. But the takeaway I got was don't be too specialized - otherwise just work hard.

Notes

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.

One study showed that early career specializers jumped out to an earnings lead after college, but that later specializers made up for the head start by finding work that better fit their skills and personalities.

In 2009, Kahneman and Klein took the unusual step of coauthoring a paper in which they laid out their views and sought common ground. And they found it. Whether or not experience inevitably led to expertise, they agreed, depended entirely on the domain in question. Narrow experience made for better chess and poker players and firefighters, but not for better predictors of financial or political trends, or of how employees or patients would perform.

“Kind” learning environments - patterns repeat over and over, and feedback is extremely accurate and usually very rapid.

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

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.

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 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.

Remove one and have a group, a remote villager insisted. The bullet must be loaded in the rifle to kill the bird, and “then you have to cut the bird up with the dagger, since there’s no other way to do it.” These were just the introductions explaining the grouping task, not the actual questions. No amount of cajoling, explanation, or examples could get remote villagers to use reasoning based on any concept that was not a concrete part of their daily lives.

Students Flynn tested often mistook subtle value judgments for scientific conclusions, and in a question that presented a tricky scenario and required students not to mistake a correlation for evidence of causation, they performed worse than random. Almost none of the students in any major showed a consistent understanding of how to apply methods of evaluating truth they had learned in their own discipline to other areas. In that way, the students had something in common with Luria’s remote villagers—even the science majors were typically unable to generalize research methods from their own field to other fields.

Fortunately, 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.

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.

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 the United States, about one-fifth of questions posed to students began as making-connections problems. But by the time the students were done soliciting hints from the teacher and solving the problems, a grand total of zero percent remained making-connections problems. Making-connections problems did not survive the teacher-student interactions.

The results were the same. Being forced to generate answers improves subsequent learning even if the generated answer is wrong. It can even help to be wildly wrong. Metcalfe and colleagues have repeatedly demonstrated a “hypercorrection effect.” The more confident a learner is of their wrong answer, the better the information sticks when they subsequently learn the right answer. Tolerating big mistakes can create the best learning opportunities.

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. The eighth-grade math teacher was essentially testing her students in class, but she was facilitating or outright giving them the answers. Used for learning, testing, including self-testing, is a very desirable difficulty.

If that eighth-grade classroom followed a typical academic plan over the course of the year, it is precisely the opposite of what science recommends for durable learning—one topic was probably confined to one week and another to the next. 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.

Frustration is not a sign you are not learning, but ease is.

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 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.

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.

The private equity investors were told to assess a real project they were currently working on with a detailed description of the steps to success, and to predict the project’s return on investment. They were then asked to write down a batch of other investment projects they knew of with broad conceptual similarity to theirs—for instance, other examples of a business owner looking to sell, or a start-up with a technologically risky product. They were instructed to estimate the return for each of those examples too. In the end, the investors estimated that the return on their own project would be about 50 percent higher than the outside projects they had identified as conceptually similar.

Evaluating an array of options before letting intuition reign is a trick for the wicked world.

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). [I want this!]

For a deep structure example, you might put economic bubbles and melting polar ice caps together as positive-feedback loops. [What if you thought of everything through a systems perspective?]

Or perhaps you would put the act of sweating and actions of the Federal Reserve together as negative-feedback loops.

Alternatively, you might group Federal Reserve rate changes, economic bubbles, and gas price changes together because they are all in the same domain: economics. And you might put sweating and neurotransmission together under biology.

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.

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

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.” [Also feels fluffy]

It is definitely true that a shy child is more likely to foreshadow a shy adult, but it is far from a perfect correlation. And if one particular personality trait does not change, others will. The only certainty is change, both on average as a generation ages, and within each individual.

The most momentous personality changes occur between age eighteen and one’s late twenties

Ogas and Rose call this the “context principle.” In 2007, Mischel wrote, “The gist of such findings is that the child who is aggressive at home may be less aggressive than most when in school; the man exceptionally hostile when rejected in love may be unusually tolerant about criticism of his work; the one who melts with anxiety in the doctor’s office may be a calm mountain climber; the risk-taking entrepreneur may take few social risks.” Rose framed it more colloquially: “If 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.”

All of the strengths-finder stuff, it gives people license to pigeonhole themselves or others in ways that just don’t take into account how much we grow and evolve and blossom and discover new things,” Ibarra told me. “But people want answers, so these frameworks sell. It’s a lot harder to say, ‘Well, come up with some experiments and see what happens.’”

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. [THIS IS SO COOL]

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. [This is obvious. Need to change hiring]

[About Tetlock] He tried on ideas like Instagram filters until it was hard to tell which he actually believed.

After a simple screening, they invited thirty-two hundred to start forecasting. From those, they identified a small group of the foxiest forecasters—just bright people with wide-ranging interests and reading habits but no particular relevant background—and weighted team forecasts toward them. They destroyed the competition [experts].

Agreement is not what they are after; they are after aggregating perspectives, lots of them. In an impressively unsightly image, Tetlock described the very best forecasters as foxes with dragonfly eyes. Dragonfly eyes are composed of tens of thousands of lenses, each with a different perspective, which are then synthesized in the dragonfly’s brain.

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.

Karl Wallenda, the world-famous high-wire performer, who fell 120 feet to his death when he teetered and grabbed first at his balance pole rather than the wire beneath him. He momentarily lost the pole while falling, and grabbed it again in the air. “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.”

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.

And yet those were nothing compared to the withered technology employed by Tu Youyou, who in 2015 became the first (and so far only) Chinese national to win the Nobel Prize in Physiology or Medicine, and the first Chinese woman in any category. Tu is known as the “professor of the three no’s”: no membership in the Chinese Academy of Sciences, no research experience outside of China, and no postgraduate degree. Before Tu, other scientists had reportedly tested 240,000 compounds searching for a malaria cure. Tu was interested in both modern medicine and history, and was inspired by a clue in a recipe for medication made from sweet wormwood, written by a fourth-century Chinese alchemist. [Feels like cherry picking]

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

When Geim was asked (two years before the Nobel) to describe his research style for a science newsletter, he offered this: “It is rather unusual, I have to say. I do not dig deep—I graze shallow. So ever since I was a postdoc, I would go into a different subject every five years or so. . . . I don’t want to carry on studying the same thing from cradle to grave. Sometimes I joke that I am not interested in doing re-search, only search.”

Research on creators in domains from technological innovation to comic books shows that a diverse group of specialists cannot fully replace the contributions of broad individuals.

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