Book Summary: “Range: Why Generalist Triumph in a Specialized World” by Daniel Epstein


Range

Title: Range: Why Generalist Triumph in a Specialized World
Author: Daniel Epstein
Scope: 3 stars
Readability: 5 stars
My personal rating: 5 stars
See more on my book rating system

Topic of Book

Epstein gives compelling evidence that specialization and repetitive practice are not the smooth path to success that many popular books claim. Epstein argues that having a broad range of knowledge and skills plus an ability to see analogies across those domains of knowledge is far more useful.

My Comments

While this book is a little outside the topics of this blog, I think it is important to include, because it explains why reading this blog can make you more successful in life. Specialization is important when you are early in your career, but at some point further specialization actually harms your career and life.

By reading the summaries in this blog, you will be able to acquire a basic knowledge of many different intellectual domains. This gives you the ability to solve far more complex problems than if you spent your extra time getting a deeper knowledge of your own specialty.

Happy reading!

Key Take-aways

  • As knowledge grows, most people working within organizations tend to specialize. Specialization is critical for exploring a domain with fixed rules and a rarely changing environment.
  • Generalist (those with at least some knowledge in a wide variety of domains), do far better at innovating within and adapting to a complex and changing environment. Few organizations, including our educational system and corporations, understand the importance of generalist.
  • Specialists usually have a mental toolkit that is optimized for solving known problems within their domain of expertise. They are, therefore, very useful for constant, incremental improvement. But once these methods run into  new and very different problems, they have difficulty coming up with innovative solutions.
  • Generalist think by analogy. They look beyond the obvious characteristics of a problem and make connections to other problems that have already been solved in another field. They can then apply those solutions to solving problems in their own field.

Important Quotes from Book (H4)

“While it is undoubtedly true that there are areas that require individuals with Tiger [Wood]’s precocity and clarity of purpose, as complexity increases—as technology spins the world into vaster webs of interconnected systems in which each individual only sees a small part—we also need more Roger [Federer]s: people who start broad and embrace diverse experiences and perspectives while they progress. People with range.”

“Chess, golf, and firefighting are exceptions, not the rule.”

 “The domains Klein studied, in which instinctive pattern recognition worked powerfully, are what psychologist Robin Hogarth termed “kind” learning environments. Patterns repeat over and over, and feedback is extremely accurate and usually very rapid.”

“In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.

In the most devilishly wicked learning environments, experience will reinforce the exact wrong lessons.”

“ In a truly open-world problem devoid of rigid rules and reams of perfect historical data, AI [Artificial Intelligence] has been disastrous.”

“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.

When narrow specialization is combined with an unkind domain, the human tendency to rely on experience of familiar patterns can backfire horribly”

“If the amount of early, specialized practice in a narrow area were the key to innovative performance, savants would dominate every domain they touched, and child prodigies would always go on to adult eminence. As psychologist Ellen Winner, one of the foremost authorities on gifted children, noted, no savant has ever been known to become a “Big-C creator,” who changed their field.”

“ As psychologist and prominent creativity researcher Dean Keith Simonton observed, “rather than obsessively focus[ing] on a narrow topic,” creative achievers tend to have broad interests. “This breadth often supports insights that cannot be attributed to domain-specific expertise alone.”

 “The successful adapters were excellent at taking knowledge from one pursuit and applying it creatively to another, and at avoiding cognitive entrenchment. They employed what Hogarth called a “circuit breaker.” They drew on outside experiences and analogies to interrupt their inclination toward a previous solution that may no longer work. Their skill was in avoiding the same old patterns.”

“The Flynn effect—the increase in correct IQ test answers with each new generation in the twentieth century—has now been documented in more than thirty countries. The gains are startling: three points every ten years. To put that in perspective, if an adult who scored average today were compared to adults a century ago, she would be in the 98th percentile.

When Flynn published his revelation in 1987, it hit the community of researchers who study cognitive ability like a firebomb. The American Psychological Association convened an entire meeting on the issue, and psychologists invested in the immutable nature of IQ test scores offered an array of explanations to usher the effect away, from more education and better nutrition—which presumably contributed—to test-taking experience, but none fit the unusual pattern of score improvements. On tests that gauged material picked up in school or with independent reading or study—general knowledge, arithmetic, vocabulary—scores hardly budged. Meanwhile, performance on more abstract tasks that are never formally taught, like the Raven’s matrices, or “similarities” tests, which require a description of how two things are alike, skyrocketed.”

“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.”

“ Conceptual schemes are flexible, able to arrange information and ideas for a wide variety of uses, and to transfer knowledge between domains. Modern work demands knowledge transfer: the ability to apply knowledge to new situations and different domains. ”

“Flynn’s great disappointment is the degree to which society, and particularly higher education, has responded to the broadening of the mind by pushing specialization, rather than focusing early training on conceptual, transferable knowledge.”

 “Flynn’s conclusion: “There is no sign that any department attempts to develop [anything] other than narrow critical competence.”

“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.”

“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. It is a powerful tool for solving wicked problems… 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.”

“ the most successful strategy employed multiple situations that were not at all alike on the surface, but held deep structural similarities. Most problem solvers are not like Kepler. They will stay inside of the problem at hand, focused on the internal details, and perhaps summon other medical knowledge, since it is on the surface a medical problem. They will not intuitively turn to distant analogies to probe solutions. They should, though, and they should make sure some of those analogies are, on the surface, far removed from the current problem.”

“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 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.”

“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… knowing when to quit is such a big strategic advantage that every single person, before undertaking an endeavor, should enumerate conditions under which they should quit. The important trick, he said, is staying attuned to whether switching is simply a failure of perseverance, or astute recognition that better matches are available.”

“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.”

“The most momentous personality changes occur between age eighteen and one’s late twenties, so specializing early is a task of predicting match quality for a person who does not yet exist. It could work, but it makes for worse odds.”

“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.”

“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.”

“Ouderkirk and the other two researchers who set out to study inventors at 3M wanted to know what profile of inventor made the greatest contributions. They found very specialized inventors who focused on a single technology, and generalist inventors who were not leading experts in anything, but had worked across numerous domains.

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. (They also found inventors who had neither significant depth nor breadth—they rarely made an impact.) The specialists were adept at working for a long time on difficult technical problems, and for anticipating development obstacles. The generalists tended to get bored working in one area for too long. They added value by integrating domains, taking technology from one area and applying it in others. Neither an inventor’s breadth nor their depth alone predicted the likelihood that one of their inventions would win the Carlton Award—the “Nobel Prize of 3M.”

Ouderkirk’s group unearthed one more type of inventor. They called them “polymaths,” broad with at least one area of depth.”

“The polymaths had depth in a core area—so they had numerous patents in that area—but they were not as deep as the specialists. They also had breadth, even more than the generalists, having worked across dozens of technology classes. Repeatedly, they took expertise accrued in one domain and applied it in a completely new one, which meant they were constantly learning new technologies. Over the course of their careers, the polymaths’ breadth increased markedly as they learned about “the adjacent stuff,” while they actually lost a modicum of depth. They were the most likely to succeed in the company and to win the Carlton Award. At a company whose mission is to constantly push technological frontiers, world-leading technical specialization by itself was not the key ingredient to success.”

“Specialist contributions skyrocketed around and after World War II, but more recently have declined. “Specialists specifically peaked about 1985,” Ouderkirk told me. “And then declined pretty dramatically, leveled off about 2007, and the most recent data show it’s declining again… His hypothesis is that organizations simply don’t need as many specialists. “As information becomes more broadly available, the need for somebody to just advance a field isn’t as critical because in effect they are available to everybody,”

“ In low-uncertainty domains, teams of specialists were more likely to author useful patents. In high-uncertainty domains—where the fruitful questions themselves were less obvious—teams that included individuals who had worked on a wide variety of technologies were more likely to make a splash. The higher the domain uncertainty, the more important it was to have a high-breadth team member. ”

“A 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. In the sweep of humanity, that is not normal.”

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