Range

This is a comprehensive summary of a mediocre 🌿 branch book. The primary argument of the book is that it is worth it to become a generalist in the modern world. The author, David Epstein, uses the word ‘Range’ to describe this generalist quality; the rest of the book goes into the various ways range-y individuals beat out specialists. You should not buy this book, it is not worth your time; you should read this summary and then subscribe to Epstein’s newsletter. I’ll explain more at the end. Read more about book classifications here.

There’s this writing style in popular non-fiction that I’ll call the ‘Malcolm Gladwell method of shoving-a-story-in-your-face’. It substitutes argumentation for storytelling and anecdote, and in so doing sidesteps the difficulty of making a case, since the reader is too distracted by narrative to comprehend the point the author is actually attempting to make.

Whenever this happens, I take care to pay special attention, because often the point is banal, or flawed, or too inconsequential to stand on its own. (I happen to know this because I’ve used this technique a few times on this very blog, and I know from reader feedback how effective it is).

Range uses the Malcolm Gladwell shove-a-story-in-your-face technique for the entire length of the book. It has some fantastic ideas. But Range’s arguments are often sloppy, and the reader is always, always distracted by the latest story, filled with interesting characters doing interesting things at interesting times in interesting places. You look up from the story of Gunpei Yokoi creating the Game Boy at Nintendo with a stupid grin on your face and ask “wait, what was Epstein trying to say again?”

But by then you are onto the next story, so you forget.

This summary will strip as many stories as possible out from Range, and reduce everything down to concise arguments that Epstein implies (by way of storytelling) but doesn’t actually make. This does two things:

1. It compresses the ideas down to a fraction of the book’s reading time.
2. It exposes the holes in Epstein’s arguments, which gives us opportunity to investigate those holes for ourselves.

This allows us to get at the best arguments Epstein has, even if it’s buried under a mountain of narrative in the book itself.

Intro: Roger vs Tiger

The core thesis of Range is captured pretty neatly by a comparison between Tiger Woods and Roger Federer in the introduction of the book.

Woods started young. His father saw that the young boy was a prodigy at age two, and made it his life mission to mould Tiger into the greatest golf player the world had ever seen.

Federer started late. When he was younger, he dabbled in skiing, wrestling, swimming, skateboarding. He was interested in ball sports: basketball, handball, tennis, table tennis, badminton, and soccer. He was also enormously talented in tennis (“That boy was born with a racquet in his hand” said one of his early coaches). He eventually gave up soccer at age 12 to focus on tennis, and remains today one of the best players in the world.

What is the point of this anecdote? The point is that some athletes start late and succeed. Epstein uses this story as a way of introducing us to his main goal with Range: he intends to push back on the mainstream idea of excessive specialisation. We believe that sports superstars should start early. We think high achievers should stick to one career path. We frown on people who sample widely and jump from sport to sport, or from job to job.

Epstein argues that a circuitous path has merit. He argues that wide sampling isn’t bad. This is the core thesis of the book.

1. The Cult of the Head Start

Epstein introduces us to Robin Hogarth’s conception of ‘kind’ and ‘wicked’ learning environments — the same research that Gary Klein and Daniel Kahneman cited in their Failure to Disagree paper, and that I cited in my series on mental models.

A kind learning environment is an environment where “patterns repeat over and over, and feedback is extremely accurate and usually very rapid. In golf or chess, a ball or piece is moved according to rules and within defined boundaries, a consequence is quickly apparent, and similar challenges occur repeatedly.”

Meanwhile, 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.”

Epstein’s implied argument is that specialisation is rewarded in kind learning environments, and that deliberate practice is the practice of specialisation. He then implies that chess and golf are kinder than tennis, and in those fields, early specialisation are rewarded. In other words, because tennis is more wicked, Federer could afford to be more range-y than Tiger Woods — he could do a period of sampling in his childhood before doubling down on his chosen sport.

Is this argument a strong one? No. Epstein implies this argument but never makes it outright because (I think) he knows this; he knows it conflates a whole array of factors. Perhaps Federer was a talented outlier. Perhaps Federer had monster genes (especially relevant because Epstein wrote a whole book about athletes and genetics). Perhaps junior tennis was relatively unprofessionalised in the early 90s, and the meta has shifted to earlier and earlier training programs that focus on conditioning and technique and strength in a way that makes a Federer impossible today. Perhaps we’ll never see a Federer again.

To be fair, the important idea is the notion of kind vs wicked learning environments, not the argument of whether tennis is more wicked than golf or that the path to success in tennis is to play a lot of ball sports in one’s youth. Epstein shifts topic abruptly at the end of the chapter to point out that:

Nobel laureates are at least twenty-two times more likely to partake as an amateur actor, dancer, magician, or other type of performer. Nationally recognized scientists are much more likely than other scientists to be musicians, sculptors, painters, printmakers, woodworkers, mechanics, electronics tinkerers, glassblowers, poets, or writers, of both fiction and nonfiction. And, again, Nobel laureates are far more likely still. The most successful experts also belong to the wider world.

He then closes the chapter by asserting that the modern world is more wicked than kind.

2. How the Wicked World Was Made

Is the modern world really wicked? Epstein spends a chapter to assert that it is.

His argument is (again) implied through anecdote and never explicitly made, but it goes something like this:

The modern world is wicked because it changes our minds. Modern citizens think very differently from pre-modern people. For instance, pre-modern villagers find it difficult to perform categorical reasoning. They do not do well on the Ravens Progressive Matrices (a type of IQ test); they refuse to reason by induction, and they find it difficult to elucidate conceptual categories when presented with a collection of items. For instance, when presented with a hammer, a saw, a hatchet, and a log, modern thinkers would point out that three of them are tools. But a pre-modern villager presented with this test would say that the tools did not belong together; “they are useless without the log, so why would they be together?”

Modern thinkers are more categorical in their thinking. Epstein writes:

In every cognitive direction, the minds of premodern citizens were severely constrained by the concrete world before them. With cajoling, some solved the following logic sequence: “Cotton grows well where it is hot and dry. England is cold and damp. Can cotton grow there or not?” They had direct experience growing cotton, so some of them could answer (tentatively and when pushed) for a country they had never visited. The same exact puzzle with different details stumped them: “In the Far North, where there is snow, all bears are white. Novaya Zemlya is in the Far North and there is always snow there. What colors are the bears there?” That time, no amount of pushing could get the remote villagers to answer. They would respond only with principles. “Your words can be answered only by someone who was there,” one man said, even though he had never been to England but had just answered the cotton question. But even a faint taste of modern work began to change that. Given the white bear puzzle, Abdull, forty-five and barely literate but chairman of a collective farm, would not give an answer confidently, but he did exercise formal logic. “To go by your words,” he said, “they should all be white.”

Exposure to the modern world forces us to put on ‘scientific spectacles’. This quote is from James Flynn, he of the Flynn effect; Epstein introduces him at the start of the chapter and writes:

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 5000 words.

Epstein concludes that seeing the world this way must mean that the world has become increasingly wicked. And because the world is wicked, generalists should do better, since generalists are the type of people who thrive in wicked learning environments.

(Exercise for the alert reader: what is wrong with this chain of logic?)

Epstein closes the chapter with a number of observations from Flynn about modern education systems. Flynn believes that schools and universities have equipped us for specialised roles, but have failed to equip us with the types of thinking necessary to deal with a modern, ‘wicked’ world. These are things like critical thinking, multi-disciplinary analysis, and ‘Fermi-izing’ (doing rough calculations on the back of an envelope).

Epstein closes the chapter with the following paragraph:

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.

If this argument feels a bit off to you, you’re not alone. I get Epstein’s larger point, but he does seem to conflate a number of different things together. For instance, ask yourself:

1. Does range necessarily lead to better critical thinking abilities? Or is critical thinking a completely different and orthogonal measure to the specialist vs generalist discussion?
2. Is everything about the modern world complex and ever-changing? Are there not pockets where experience and expertise matter? (One would think that things like management ability or writing ability, for instance, are things that are unchanging, and that benefit from experience).
3. Is analysis in the face of new and unexpected situations truly important? Epstein’s core assertion is that generalists have an advantage when facing novel situations, since they can draw on their broader span of knowledge. But is this as critical in the modern world as he makes it out to be? Are there situations where it doesn’t apply?
4. Is it even possible to learn without experience? (The answer, as far as I’m aware, is no.)

Epstein doesn’t pre-empt these questions, nor does he deal with them in the book. But they’re worth chewing on, I think, because Epstein’s main thesis is incredibly broad in its scope.

3. When Less of the Same Is More

Chapter 3 in Range is about practice, and it was something I thoroughly enjoyed.

Epstein opens by describing the figlie del coro — a group of female orphan musicians in Venice, trained with extreme rigour in public-private institutions called ospedali — charitable institutions that functioned like orphanages — that were funded by upper-class Venetians and unofficially supported by the Church. Epstein writes, of these remarkable women:

Shortly after he received his music doctorate from Oxford, eighteenth-century English composer and historian Charles Burney set out to write a definitive history of modern music, which involved several ospedali visits. Burney, who became famous as both a travel writer and the foremost music scholar of the day, was astounded by what he saw in Venice. On one ospedali trip, he was given a two-hour private performance, with no curtain between him and the performers. “It was really curious to see, as well as to hear, every part of this excellent concert, performed by female violins, hautbois oboes, tenors, bases, harpsichords, french-horns, and even double bases,” Burney wrote. More curious still, “these young persons frequently change instruments.”

Figlie took singing lessons, and learned to play every instrument their institution owned. It helped that they were paid for learning new skills. A musician named Maddalena married and left institutional life, and toured from London to St. Petersburg, performing as a violinist, harpsichordist, cellist, and soprano. She wrote of “acquiring skills not expected of my sex,” and became so famous that her personal life was covered by one of the day’s gossip writers.

(…) The Pieta’s musicians loved to show off their versatility. According to a French writer, they were trained “in all styles of music, sacred or profane,” and gave concerts that “lent themselves to the most varied vocal and instrumental combinations.” Audience members commonly remarked on the wide range of instruments the figlie could play, or on their surprise at seeing a virtuosa singer come out during intermission to improvise an instrumental solo.

The point of the vignette is to drive home the sheer range of instruments these women played, and how that training went against the grain of what we believe about musical training.

The mainstream take on musical excellence is the image of a young musician, specialising in a single instrument from what looks like birth. But Epstein takes great pains to point out that this idea is mistaken. He cites John Sloboda, one of the most influential researchers in the psychology of music:

When Sloboda and a colleague conducted a study with students at a British boarding school that recruited from around the country—admission rested entirely on an audition—they were surprised to find that the students classified as exceptional by the school came from less musically active families compared to less accomplished students, did not start playing at a younger age, were less likely to have had an instrument in the home at a very young age, had taken fewer lessons prior to entering the school, and had simply practiced less overall before arriving—a lot less. “It seems very clear,” the psychologists wrote, “that sheer amount of lesson or practice time is not a good indicator of exceptionality.” As to structured lessons, every single one of the students who had received a large amount of structured lesson time early in development fell into the “average” skill category, and not one was in the exceptional group. “The strong implication,” the researchers wrote, is “that that too many lessons at a young age may not be helpful.”

“However,” they added, “the distribution of effort across different instruments seems important. Those children identified as exceptional by (the school) turn out to be those children who distributed their effort more evenly across three instruments.” The less skilled students tended to spend their time on the first instrument they picked up, as if they could not give up a perceived head start. The exceptional students developed more like the figlie del coro. “The modest investment in a third instrument paid off handsomely for the exceptional children,” the scientists concluded.

Epstein then changes topics to jazz, and points out that the deliberate practice literature is remarkably silent on this branch of musical skill. In fact, The Cambridge Handbook of Expertise and Expert Performance simply notes that “in contrast to classical players, jazz and folk and modern popular musicians and singers do not follow a simple, narrow trajectory of technical training, and they ‘start much later.’”

I’ve written extensively about this phenomenon, so you might be familiar with it: essentially, deliberate practice is relevant only to certain domains, and in most other domains, expertise is tacit in nature and acquired through trial and error or osmosis, not structured practice.

But I’m hesitant to endorse Epstein’s take on things. The ‘wide sampling’ that Epstein describes is sometimes called ‘perceptual learning’; it is the method of going through a large number of perceptual samples over a period of time in order to build up deep tacit mental models of expertise.

This dichotomy between perceptual sampling and deliberate practice seem only tangentially related to the generalist vs specialist discussion. A student who learns by osmosis is in many ways less efficient compared to the student who is able to take advantage of deliberate practice; it is no surprise, then, that the former must sample a much larger set of practice situations than the latter. This is not necessarily an argument for generalists.

It is, however, an argument against pure deliberate practice, but if you’ve looked seriously into the literature (or attempted to put deliberate practice to practice) you are likely to conclude this by yourself!

4. Learning, Fast and Slow

Epstein further conflates effective learning strategy with being a generalist in Chapter 4. The core argument of this chapter is that the most effective learning strategies don’t feel like progress when you’re in the middle of doing them.

These are learning strategies like:

• Interleaving — You mix topics and practice them in one session, instead of studying one thing at a time. This feels terribly difficult while you’re doing it.
• Spacing — You return to old topics after a period of time, instead of studying one topic for a short, intense period, never to return to it again. This is, again, horrible to do; your mind is forced to dredge up old things it can barely remember.
• The generation effect — (More commonly known as the testing effect), you struggle to ‘generate’ an answer of your own. Even if you get it wrong, your subsequent learning performance improves.

Each of these learning techniques feel absolutely horrible to do in the moment. Worse still, students don’t feel that they are learning much while they are doing these things, even if they are improving. In pedagogy, these interventions are known as ‘desirable difficulties’, and Epstein points out that the vast majority of educators and students believe that learning should feel like you’re making progress, when in truth effective learning feels nothing like that.

It’s not clear how this chapter fits into the broad argument that Epstein is trying to make about generalists. Epstein seems to conflate the deliberate practice tradition with ‘specialists’ and … maybe he thinks that generalists sample a lot and struggle with things over a long period of time? The problem with that interpretation is that the deliberate practice people are very clear on what DP should feel like: good DP, they say, should feel absolutely horrible.

Perhaps I’m missing the point of this chapter. But I’ve reread it thrice now, twice while writing this section, and I’m not sure that I have. The closest Epstein comes to linking back to his thesis is in the closing paragraphs of the chapter, where he writes:

Knowledge with enduring utility must be very flexible, composed of mental schemes that can be matched to new problems. The virtual naval officers in the air defense simulation and the math students who engaged in interleaved practice were learning to recognize deep structural commonalities in types of problems. They could not rely on the same type of problem repeating, so they had to identify underlying conceptual connections in simulated battle threats, or math problems, that they had never actually seen before. They then matched a strategy to each new problem. 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’ve read enough pedagogical research to know that ‘far transfer’ is an elusive thing. Perhaps this is what’s related to range? I do not know.

5. Thinking Outside Experience

Epstein takes the throwaway comment he makes about ‘far transfer’ and expands it into what is — for me — one of the most intriguing aspects of the book. This is mostly by accident, as you’ll see in a bit.

The core argument of Chapter 5 is that “analogical thinking is awesome, you are better able to do analogical thinking if you have wider range, therefore range is beneficial and you should go acquire that.” Again, Epstein never explicitly states this argument; he just implies it via a series of anecdotes.

The anecdotes are:

• People who use the ‘outside view’ (sometimes called ‘base rate reasoning’) to make judgments do better than those who just rely on the ‘inside view’. Epstein points out this is a form of analogical thinking — you’re drawing parallels to a similar example that is external to your own, instead of reasoning completely from your own context.
• Scientists with more diverse experiences, or research teams with more diverse academic backgrounds, do better when tackling problems in a lab setting. This is because they can draw from a wider range of analogies during their problem solving (“oh this looks a lot like how mushrooms cluster in the rain …”)
• Problem solvers do better if given hints via analogies from different domains.

The key to good analogical thinking is when you can map deep structural similarities between examples drawn from different fields. Epstein asserts that it’s not worth it to just compare surface similarities; the more range you have, the deeper the structural similarities and the larger the set of analogies you may draw on.

Why is this interesting to me? This is interesting because analogical thinking happens to be the primary thinking method that Charlie Munger uses.

Take this story by Daniel Lewis, for instance, titled What we learned pitching our startup to Charlie Munger – and how we almost went hunting elephants together. Lewis writes:

As we sat around the board table eating simple sandwiches (turkey on white bread), potato chips, and drinking Coke products (Berkshire invested $1B in Coke in 1988), we laid out our vision in more detail. Charlie eased back in his chair, listening to us while happily munching chips. Crumbs gathered on his shirt.

“We want to win a position in the legal research market with analytics and, as lawyers turn to us for unique insights and easier user interfaces, we can expand our tools and content until we’re a complete alternative to the incumbents. Our technology scales efficiently, so we can also offer lower prices.”

Charlie spotted another pattern. “This reminds me of the ‘Cola Wars’ between Pepsi and Coke. Up until the Great Depression, Pepsi and Coke were priced the same and Coke was dominating the market. But then Pepsi cut their price per ounce by half and their sales took off, with profits doubling too. Price can be a powerful competitive tool when you have a good substitute product.”

Eventually, Charlie asked us for more detail about our funding plans. We told him about the round we were assembling and some of the other investors involved.

“I bet” he said, “that if I invested I could be a Judas goat for you.”

He noticed our confused looks.“You’re not familiar with a Judas goat?”

We shook our heads.

“A Judas goat is the goat that they lead from one pen to another, or the slaughterhouse, that all the other animals follow. I bet if I invested, you’d have a lot of other investors that would sign up too.”

“I’m sure you’re right” I smiled, enjoying his positive interest and the folksy, if somewhat dark, analogy.

“Well, it’s very exciting and we thank you for coming down here to meet with us” he said. “We’ll be in touch.”

Notice how Munger is pattern-matching against a vast library of ‘mental models’, ideas, stories, and historical patterns he has stored up in his head.

I happen to have a long history of criticising the mental model movement, but I’m beginning to think that what Munger does (instead of what he says he does) that is worth copying is his brand of analogical reasoning, NOT the mental models that he uses as fodder for his thinking. I’ll probably write more about this in the future.

6. The Trouble With Too Much Grit

In Chapter 6, Epstein introduces what might be my favourite idea of the book: ‘match quality’. This chapter also happens to contain one of the most emotionally moving descriptions of Vincent Van Gogh’s life and career that I’ve ever read. If I could take one chapter from Range with me, this one would be it.

‘Match quality’ is defined by economists as the degree of fit between the work someone does and who they are — that is, their abilities and their interests. Epstein paints a picture of Van Gogh’s incredibly painful search for his ‘match quality’: at one point, Van Gogh writes to his brother, saying “A man doesn’t always know himself what he could do, but he feels by instinct, I’m good for something, even so! … I know that I could be a quite different man! … There’s something within me, so what is it!” The anguish is palpable.

Epstein continues:

“He had been a student, an art dealer, a teacher, a bookseller, a prospective pastor, and an itinerant catechist. After promising starts, he had failed spectacularly in every path he tried.”

(…) His next letter to his brother was very short: “I’m writing to you while drawing and I’m in a hurry to get back to it.”

Epstein cites Northwestern University economist Ofer Malamud’s work on match quality to make a salient point. Career choices have a tension to them that we should all be familiar with: if a student specialises in a subject earlier in their lives, they would acquire more skills that prepares them for the job market. On the other hand, if they sampled and focused later in their lives, they would enter the job market with fewer domain-specific skills, but a greater sense of the type of work that would fit their abilities and interests. So which is better?

Malamud found a natural experiment in the British school system to study this very problem. For the period he studied, English and Welsh students had to specialise before college so that they could apply to specific, narrow programs. In Scotland, however, students were actually required to study different fields for their first two years of college, and could keep sampling beyond that. Malamud’s results are affirming:

If the benefit of higher education was simply that it provided skills for work, then early-specializing students would be less likely to career switch after college to a field unrelated to their studies: they have amassed more career-specific skills, so they have more to lose by switching. But if a critical benefit of college was that it provided information about match quality, then early specializers should end up switching to unrelated career fields more often, because they did not have time to sample different matches before choosing one that fit their skills and interests.

Malamud analyzed data for thousands of former students, and found that college graduates in England and Wales were consistently more likely to leap entirely out of their career fields than their later-specializing Scottish peers. And despite starting out behind in income because they had fewer specific skills, the Scots quickly caught up. Their counterparts in England and Wales were more often switching fields after college and after beginning a career even though they had more disincentive to switch, having focused on that field. With less sampling opportunity, more students headed down a narrow path before figuring out if it was a good one. The English and Welsh students were specializing so early that they were making more mistakes. Malamud’s conclusion: “The benefits to increased match quality . . . outweigh the greater loss in skills.” Learning stuff was less important than learning about oneself. Exploration is not just a whimsical luxury of education; it is a central benefit. (emphasis added)

Epstein then turns his attention to Angela Duckworth’s conception of ‘grit’. One of the most famous studies that Duckworth has done in her research is the study predicting which incoming freshmen would drop out of the U.S. Military Academy’s basic-training/orientation, traditionally known as ‘Beast Barracks’.

The study unfolded like this: Duckworth would assign cadets a self-assessment that captured two components of grit: the first being work ethic and resilience, and the second something called ‘consistency of interests’ (which is a fancy way of saying “knowing exactly what you want”).

Duckworth’s goal was to see if her Grit Scale would serve as a better predictor of completion than the military’s Whole Candidate Score, which was also taken at intake. It was. But Epstein pushes back on the idea that Duckworth’s Grit Scale amounts to very much. He argues:

The vast majority of plebes complete Beast, no matter their grit scores. In the first year Duckworth studied them, 71 out of 1,218 dropped out. In 2016, 32 of 1,308 plebes dropped out. The deeper question is whether dropping out might actually be a good decision. Alums told me that cadets drop out for varied reasons, during Beast and beyond it. “I think for the kids that are more cerebral and less physical, the short length makes it easy to just fight through to get to the academic year. For the more physical kids, Beast will be one of the best experiences they have,” Ashley Nicolas, an ’09 alum who worked as an intelligence officer in Afghanistan, told me. Some of those cadets make it through Beast only to realize that the academy was not the right place for their abilities or interests. “I remember a lot more leaving during first semester when they realized they could not hang academically. The ones who left earlier were either very homesick or just realized they were not a good fit. Most of the latter seemed to be kids who were pressured into coming to West Point without any real desire themselves.

In other words, of the small number of cadets who left during Beast, rather than a failing of persistence, some of them were simply responding to match quality information—they weren’t a good fit. (emphasis added)

Epstein also points to the US Army at large, which had been dealing with high drop out rates for decades. In the early 2000s, the Army began offering retention bonuses to its officers — packages that cost taxpayers $500 million and made no difference to the problem: officers who planned to stay anyway took it, while those planning to leave didn't. The Army learned that it had a match quality problem, not a financial one. Epstein writes about this realisation:

(The Army) has, though, begun to subtly change. That most hierarchical of entities has found success embracing match flexibility. The Officer Career Satisfaction Program was designed so that scholarship-ROTC and West Point graduates can take more control of their own career progression. In return for three additional years of active service, the program increased the number of officers who can choose a branch (infantry, intelligence, engineering, dental, finance, veterinary, communication technology, and many more), or a geographic post. Where dangling money for junior officers failed miserably, facilitating match quality succeeded. In the first four years of the program, four thousand cadets agreed to extend their service commitments in exchange for choice.

Epstein seems to be calling for a kinder, broader recognition that grit isn’t the 100% good thing that we seem to think it is. Sometimes, grit is bad, and quitting is good. And that ‘sometimes’ is when you have enough match quality information to decide that you want to leave to do something else.

7. Flirting With Your Possible Selves

From grit, Epstein moves on to the idea that it is ok to meander in your life. Chapter 7 is essentially his take on the whole “you’re not too late in your life to succeed” meme — which one may occasionally see on Facebook or Twitter with the heads of famous, successful people juxtaposed next to inspirational quotes, all of whom started in their 40s or 50s.

Epstein tells us a small handful of stories of people who did not have a long term plan, who did not know where they were going, and who succeeded anyway. These are the anecdotes. But he then cites Todd Rose and Ogi Ogas’s study of people with unusually winding career paths — essentially a collection of one-on-one interviews, but hundreds of them, done over the course of decades. Rose and Ogas discovered that the majority of people they interviewed (notice the caveat) have professional paths that were circuitous — and then further concluded that this was more norm than exception in the age of the knowledge worker.

Epstein riffs on that result and concludes:

• Most people who’ve had circuitous career paths performed ‘short-term planning’ — they picked from the best options in front of them. They had no long-term plan for the future. (Epstein quotes Nike’s Phil Knight: “I feel sorry for the people who know exactly what they’re going to do from the time they’re sophomores in high school.”)
• The reason this works is because we cannot predict how we will change as individuals in the future. Epstein cites Professor Dan Gilbert, who calls this the ‘end of history illusion’ — we recognise that our desires and motivations have changed a lot in the past, but believe they will not change as much in the future.
• Epstein that quotes Professor Herminia Ibarra of the London Business School, who argues that taking bets early in your career work better for optimising match quality than using introspection alone. “All of that 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. 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.”

Ibarra has a compelling quote in this chapter: “We discover the possibilities by doing, by trying new activities, building new networks, finding new role models,” she says, “Test-and-learn, not plan-and-implement.”

Or, to put this more succinctly: “I know who I am when I see what I do.”

This is not the sort of argument that you can make convincingly: for every person who had no long term plan, there are people who did, and executed it, and succeeded; for every person who meandered and found something later, there are those who wandered and then lived unhappy, unfulfilled lives. Epstein resorts to anecdote because he knows the argument isn't airtight.

But I think that's ok. I get his point. Life is complicated and there are no easy answers; this was an entertaining chapter with a number of nice stories.

8. The Outsider Advantage

There’s a thrilling episode of the This American Life podcast where Epstein tells the story of Jill Viles, who had muscular dystrophy from a genetic disease and can’t walk. She believes that she somehow has the same condition as one of the best hurdlers in the world, Priscilla Lopes-Schliep, and reaches out to Epstein to tell her of her hypothesis.

Viles is incessant in her pursuit. She sends Epstein scientific papers, old family photographs, and a 19-page bound packet about her genetics. She asks Epstein to put her in touch with Priscilla. Epstein does, even though he is perplexed by the comparison (Lopes-Schliep is extremely muscular, while Viles is confined to a wheelchair). But Viles turns out to be right.

Chapter 8 is a retelling of this story. (You can read a version of it over at ProPublica; it is thrilling and you should do so.) Epstein uses it to make one point: the more specialisation exists in the world, the more easy pickings exist for non-specialists to synthesise by combining the results from multiple fields. This is because the incentives for science are to come up with ever more specialised discoveries, so there’s lots of ideas lying around for a generalist to pick up, research, and then piece together.

In 2010, the dean of the University of Chicago’s Graduate Library School, Don Swanson, began developing something he called his ‘undiscovered public knowledge’ hypothesis. Epstein writes:

(Swanson) realized he could make discoveries by connecting information from scientific articles in subspecialty domains that never cited one another and that had no scientists who worked together. For example, by systematically cross-referencing databases of literature from different disciplines, he uncovered “eleven neglected connections” between magnesium deficiency and migraine research, and proposed that they be tested. All of the information he found was in the public domain; it had just never been connected. “Undiscovered public knowledge,” Swanson called it. In 2012, the American Headache Society and the American Academy of Neurology reviewed all the research on migraine prevention and concluded that magnesium should be considered as a common treatment. The evidence for magnesium was as strong as the evidence for the most common remedies, like ibuprofen.

Swanson wanted to show that areas of specialist literature that never normally overlapped were rife with hidden interdisciplinary treasures waiting to be connected. He created a computer system, Arrowsmith, that helped other users do what he did—devise searches that might turn up distant but relevant sets of scientific articles, and ignited a field of information science that grapples with connecting diverse areas of knowledge, as specialties that can inform one another that can drift apart.”

You can sort of see where Epstein is going with this. If the world is so specialised, then perhaps multi-disciplinary synthesisers have an edge.

(If I may add a possible corollary: if you are a blogger with an ability to read scientific papers at the edge of some field, you may be nearly as useful for the advancement of human knowledge as a scientist producing primary research in a lab; you merely have to read the right papers and ask the right questions and then write about what you’ve found.)

9. Lateral Thinking With Withered Technology

This chapter introduces a number of half-baked ideas, embedded in some great stories. They are all loosely tied to the theme of ‘range’.

The first idea is introduced via the story of Nintendo. Gunpei Yokoi was a non-technical generalist who is credited with the Game & Watch and the Game Boy; his thing was that he took old technology and found new uses for them (hence his saying: ‘lateral thinking with withered technology’). This is still Nintendo’s strategy today. With the Switch, the company deliberately opts out of the technological arms race that its competitors  pursue. It releases cardboard kits and exercise peripherals to extend the functionality of its console; it develops games that accept subpar graphical performance as a constraint.

The general conclusion that Epstein is trying to push with this story (and I have no confirmation because — again! — Epstein doesn’t actually step out to articulate it!) is that in industries where there is a race for new technology, there is potential space for a player to follow behind and recombine leftover tech for new uses. This approach requires strong generalists working alongside technical specialists — and indeed this is what you see within Nintendo. In fact, Nintendo has had to fight against the organisational tendency to reward specialists and to push generalists out. Yokoi himself grew 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.

You may or may not agree with this conclusion (I have my doubts), but the story is undeniably fantastic.

A second story is about Andy Ouderkirk, a ‘corporate scientist’ at 3M. (‘Corporate scientist’ is the highest research rank in 3M’s R&D organisation; a simpler name for Ouderkirk is ‘inventor’). A couple of years ago, Ouderkirk thought it was possible to layer hundreds of polymer sheets to create an insanely sparkly material. The optics specialists he consulted told him it couldn’t be done. But Ouderkirk was confident it was doable: he knew this because he knew that the iridescent blue morpho butterfly had no blue pigment whatsoever; its wings glow from thin layers of scales that refract and reflect particular wavelengths of blue light.

Ouderkirk formed a small team and created this material; 3M sold it as glitter. Today, the material — called ‘multilayer optical film’ — is used in LCD monitors, projectors, LED lights and cell phones to reflect and recycle light, reducing the energy required to power a bright display. Ouderkirk won the the 2013 R&D Magazine’s Innovator of the Year.

Epstein tells this story to give us three ‘conclusions’. First, Ouderkirk says “If you’re working on well-defined and well-understood problems, specialists work very, very well. As ambiguity and uncertainty increases, which is the norm with systems problems, breadth becomes increasingly important.”

Second, Ouderkirk worked with Roberto Evaristo of 3M and Boh Wai Fong at Nanyang Technological University to categorise and evaluate the patent records of specialists and generalist inventors in 3M. They concluded that neither the inventor’s breadth nor depth predicted the likelihood of winning the company’s Carlton Award (the ‘Nobel Prize of 3M’). Instead, the strongest predictor of success was a specialist who repeatedly took their expertise in one core domain and applied it to a different domain. Over time, these specialists would accrue more breadth, and gain increasing levels of technical knowledge in the adjacent domains they worked in, which would then lead them to more adjacent domains, and so on. These types of researchers were most likely to win the Carlton Award.

Third, Ouderkirk wrote a program to categorise ten million patents over the last century. His conclusion: “Specialists peaked about 1985 and then declined pretty dramatically, levelling off about 2007, and the most recent data show it’s declining again.” He doesn’t know why it’s declining. His hypothesis? Perhaps organisations 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.” Epstein editorialises: “(Ouderkirk) is suggesting that communication technology has limited the number of hyperspecialists required to work on a particular narrow problem, because their breakthroughs can be communicated quickly and widely to others—the Yokois of the world—who work on clever applications.”

What do we make of Ouderkirk’s findings? I find them interesting, but we have to do more work before we accept it as a compelling case. I wanted Epstein to dive into the literature and to address the most obvious objections to the research. For instance, does this apply outside 3M? Or is this some unique artefact of 3M’s corporate and research culture? On Ouderkirk’s findings on patents, are there ways we can verify this? Are there possible alternative explanations? Can we tease them out?

Alas, Epstein just tells us these stories, editorialises a little, and then shoos us off to the next one.

A third story is that comic books creators who have worked in more genres turn out more valuable work. This is especially surprising when compared with teams that consist of members from different comic book genres; in comics, it seems the generalist individual beats diverse teams.

What do all of these stories add up to? Why do they belong to the same chapter? I have no idea. Presumably they are all anecdotes where range leads to advantages. But the way Epstein whisks you from one to the other leaves you feeling like a tourist trapped on a tour bus; sealed away from the culture of the foreign land you’re visiting.

10. Fooled by Expertise

This entire chapter is about the work of Phillip Tetlock. I’ve written extensively about his work on Superforecasting here; you should read that if you want a summary of the research.

The gist of it is that when it comes to geopolitical forecasting, ‘foxes’ that know many things do better than ‘hedgehogs’ who know one thing. Why? The short argument is that ‘foxes’ are able to fold viewpoints from different domains into their analyses, which allows them to more accurately calibrate their judgments of the world.

Epstein presents this story as a way of saying “Look! Yet another domain where generalists win!”. But it’s worth asking: is the superforecasting example broadly generalisable? Does it indicate performance in things outside of forecasting tournaments?

I think it does, to a point. Analysis is better when done the superforecasting way. We can, and should, steal as much as possible from the superforecasters.

But we shouldn’t conflate forecasting performance (or superior analytical ability) with overall effectiveness in business or in life. As I’ve written elsewhere, forecasting is extremely difficult, and a more common, more effective strategy in business appears to be to just ignore forecasting entirely and to build extremely adaptive organisations. Epstein doesn’t go into any of this; he presents Tetlocks’s research as an example of ‘range-y’ individuals outperforming specialists, and then moves on.

11: Learning to Drop Your Familiar Tools

This is the worst chapter in Range.

The core idea here is that it is useful to drop your familiar tools in unfamiliar situations. Generalists are more able to drop familiar tools. Therefore being a generalist is better.

Of course, when I state it like that, this is a stupid, simplistic argument — Really?! Maybe what’s needed is better critical thinking; maybe the problem is functional fixedness; maybe this has nothing to do with generalists and specialists, so why conflate things unnecessarily? Epstein’s way around this is to give us a bunch of stories that telegraphs the argument above, but never explicitly states it.

The stories are:

• The Carter Racing business case study as an example. A bunch of business students are given the Carter Racing business case study to analyse. The professor tells them they’re free to ask him for extra information multiple times during class. They do not.
• Firefighters must be trained to drop their tools and flee when a fire is a lost cause. But many do not; they regard their equipment as part of their bodies and are bogged down by the gear. Many firefighters perish this way.
• The NASA Challenger disaster is an example where an entire organisation is trained to think only quantitatively. They ignore qualitative evidence. The Challenger space shuttle blows up because they ignore qualitative evidence from the field.
• Epstein cites research that show organisations do better when there is a formal chain of command but an informal chain of communication. How this is related to range is unclear.
• Epstein interviews Captain Tony Lesmes, who commands a team of Air Force pararescue jumpers. Lesmes faces a difficult decision during a deployment in Afghanistan, and then opts to stay behind in order to make space for more people to rescue. He manages to drop a familiar tool — himself — in order to solve a tricky, high-risk high-uncertainty problem. Lesmes is not ‘range-y’, it is unclear what the point of this story is.

I think this was this chapter was where I set the book aside and sighed.

If you’re reading for entertainment, the stories in Chapter 11 are nice. But this is non-fiction, and I think most of us read non-fiction for education. We want arguments that challenge us, arguments that illuminate things we’d thought about but not had the right words to say; arguments that change our minds. I remember setting down The Better Angels of Our Nature by Steven Pinker and feeling deeply exhausted — not because it was a bad book; but because it was a good one. Better Angels was a first rate argument executed over 800 pages of precisely tuned prose, written in a sure hand by a first-rate intelligence. I still do not want to agree with the conclusions, but I couldn’t find a single flaw in Pinker’s argument. It has challenged me to my core.

By comparison, I agree with Range’s core thesis. I believe generalists have advantages that specialists do not, and that these advantages have compounded in the age of the Internet. But Range’s argument is sloppy, even as it is wrapped up in pitch-perfect narrative. I wanted it to be better, but I was consistently disappointed.

12. Deliberate Amateurs

I shall skip the stories here and state the telegraphed argument plainly: Epstein observes that creative thinkers often have an enthusiastic, playful streak. They are willing to play outside the boundaries of their field. Therefore you should cultivate an enthusiastic, playful streak and act as a deliberate amateur in order to be creative and to achieve success in science, just like the researchers profiled in this chapter.

(Are there creative, successful researchers who are serious and non-playful? Is this really a determining factor? Of the people profiled, are they creative and successful because they are different types of people, or is their success from the different things they do? WE. HAVE. NO. IDEA).

What To Take Away?

Range is less than the sum of its parts. Good non-fiction books develop an argument systematically over the course of the book.  Range does no such a thing. It is a collection of anecdotes that are strung together with the thinnest of connective tissue, and the arguments it develops has you no more convinced of its original thesis than when you began. It is simply not a good book.

So what is valuable about Range? I think that several individual ideas that Epstein covers in the book are interesting, and worth investigating further. These are, in no particular order:

• The idea that ‘slow’ learning is what is valuable when it comes to developing foundational skills.
• The whole notion of ‘match quality’ as a counterpoint to ‘grit’.
• The idea that analogical thinking is powerful, and that it depends on a large-enough set of experiences and ideas to perform well. (This one is so interesting that I’m fairly certain I’ll write about it down the line).

The bit that irks me is that Epstein gets it. If you read his newsletter (aptly titled The Range Report) Epstein regularly cites primary research, throws up graphs and charts with aplomb, and displays a grasp of the underlying science that’s second to none. It just doesn’t seem to come through in Range. I don’t know why.

At any rate, my recommendation, made at the top of this summary, makes more sense now: read this essay, skip the book, and subscribe to Epstein’s newsletter. The Range Report is great. It’s just a pity that Range is not.

Originally published 19 August 2020, last updated 15 October 2023.