Why Generalists Win

13 min readJun 30, 2019

A Review of David Epstein’s Range

A recent Brookings Study found that almost a quarter of American jobs are at high risk of being automated in the coming decades. In the next ten years, the labor market is going to make major shifts that will likely catch many workers, especially those with relatively routine-natured jobs, off-guard. How exactly should today’s graduates or even those further along in their career paths pivot towards skills that will be rewarded in the coming future?

It’s a difficult question. We don’t know with certainty what skills and traits will be needed that far down the line, or what automation will be capable of taking over — it wasn’t too long ago that some experts thought driving to be too complex a task to be automated. But generally, we do know what isn’t going to cut it: rote memorization, highly constrained problem-solving, and routines that are largely repetitive and easily replicated. As Epstein notes in chapter 2:

“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.”

One of Epstein’s arguments here is that as humans, our greatest skill isn’t narrow specialization, but rather the ability to integrate broadly across disparate fields. Whereas artificial intelligence is extremely good (or rather, far better than us) at making decisions in stable environments that simply require raw calculation ability, humans tends to be better at adapting to fluid environments where the rules may change over time. Applying narrow specialization and specific pattern recognition to problems in an ever-changing environment (think of fields like investing) can lead to disastrous results.

This idea that we need to be able to better handle complexity by learning an array of mental models from different fields isn’t so new. Charlie Munger has been preaching about the benefits of being an inter-disciplinarian for years. In Poor Charlie’s Almanack, Munger claims that learning different mental models is essential for avoiding the man-with-a-hammer tendency — that is, to a guy with a hammer, every problem looks like a nail. If you only have one tool to solve an issue, you’re going to use it every time even though the nuances of the situation require a different tool or skill-set. Breadth of training and learning is extremely useful in building a conceptual foundation from which you connect ideas from different fields. By taking in various abstract models, you’ll become better at applying knowledge in situations you’ve never seen before, which Epstein calls “the essence of creativity.” It’s no wonder that at the end of Munger’s book, he recommends books ranging from history to biology to economics.

Following the author’s unfavorable view of “fast and easy” pattern recognition, Epstein gives us plenty of practical tips on how we should approach learning new material. I found a lot of overlap with his tips and a Coursera class I took on meta-learning, which was super useful. Most of the suggestions involve slowing down and, not surprisingly, making things a bit more difficult:

Use “making connections” questions, which connect to broader concepts rather than just simple procedures
Struggling to generate an answer on your own, even when you’re wrong, enhances subsequent learning (aka, don’t use hints to help yourself)
Testing and struggling to retrieve information primes the brain for future learning (similar to #2)
Use spaced repetition — rather than intensely focusing on the material rather and moving on to the next topic, come back and re-test that topic a few days later
For a given amount of material, learning is most efficient in the long-run when really inefficient in the short-run; if you’re doing too well when you test yourself, wait longer before practicing the same material again
Use interleaving (mixed practice) — for example, rather than focus solely on one topic during a study session, focus on 2 or more topics. This will better simulate your test experience as well as improve abstract generalizations. I also take interleaving to mean using different mediums of practice — so maybe using flashcards, video, reading, or practice tests in the same session.

Beyond using anecdotes and research studies to display the benefits of being an inter-disciplinarian, Epstein also heavily references Philip Tetlock’s Superforecasting, which is a fantastic book on how people can get better at making predictions. The main dichotomy in the book is that between hedgehogs — people who use one overarching ideology to make decisions — and foxes — people who draw on an eclectic array of disciplines to make decisions. It’s not hard to guess which one Epstein (and the research) views as superior. He states:

“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.”

How does one become more fox-like in their thinking and predicting? A few pointers:

Be highly open-minded to both the range of possibilities and to dissenting views
Be curious and draw on best ideas across disciplines
Generate a list of separate events with deep structural similarities — rather than focusing only on the internal details of the specific event
Dissect prediction results in search of lessons — create a rigorous feedback system for difficult learning environments (or as Annie Duke recommends, conduct pre- and post-mortems)

If you can combine Epstein’s advice on learning from different disciplines, regularly conducting deep learning/practice, making conceptual connections between fields, and becoming more fox-like in your thinking, you’ll already be far ahead of the pack. I really enjoyed how far-reaching and broad the author’s source material and studies were, as it fits perfectly with his arguments. There are so many nuggets of wisdom here about learning, choosing a career, and how to approach the daunting future we all face. My biggest takeaway here is that it’s okay to dabble and experiment. You don’t need to be the best in the world at whichever things you choose to do. Merely picking up a different perspective and another tool in your mental toolbox can yield far better rewards than simply being really good at one thing. That being said, we should be willing to learn and adjust as we go — at times abandoning goals altogether and changing directions if we have to.

Overall, this was a fantastic book that I will probably revisit next year.

Score: 9/10

Notes:

Chapter 1

The bigger the picture, the more unique the potential human contribution. Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.
AI systems need stable structures and narrow worlds in order to function effectively
When we know the rules/answers and they don’t change over time, then specialized + deliberate practice from day one makes a lot of sense
But when narrow specialization is combined with an unkind (changing or undefined rules) domain, the human tendency to rely on experience of familiar patterns can backfire horribly
To avoid cognitive entrenchment — vary challenges within a domain drastically + have one foot outside your world
The most successful experts belong to a wider world, usually as amateur artists/musicians/craftsmen etc.
Adapters are good at taking knowledge from one pursuit and applying it creatively to another

Chapter 2

Our most fundamental thought processes have changed to accommodate increasing complexity and the need to derive new patterns rather than rely only on familiar ones.
Our conceptual classification schemes provide a scaffolding for connecting knowledge, making it accessible and flexible.
Modern life now requires range, making those connections across far-flung domains/ideas
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

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.

Chapter 4

For learning that is durable and flexible (broadly applied), fast and easy pattern recognition is usually the problem
“Desirable difficulties”
Using “making connections” questions, which connect to broader concepts rather than just a procedure
Struggling to generate an answer on your own, even if wrong, enhances subsequent learning (hints are not useful)
Testing and struggling to retrieve information primes the brain for subsequent learning, even when retrieval is unsuccessful
Practice should be distributed — need spaced repetition of the same material rather than intensely focusing on a subject for an amount of time and then moving on
For a given amount of material, learning is most efficient in the long run when it is really inefficient in the short run. If you are doing too well when you test yourself, the simple antidote is to wait longer before practicing the same material again, so that the test will be more difficult when you do. Frustration is not a sign you are not learning, but ease is.
Interleaving, or mixed practice, can improve inductive reasoning and better abstract generalizations versus blocked repetition
Learning deeply means learning slowly — so often we take visible progress as feedback for doing the wrong thing

Chapter 5

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.
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.
Using a broad reference class of analogies (rather than a single best one) performs much better for constructing an outside view

Chapter 6

“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.
Career switchers, although they suffer a hit to their applicable skills, tend to have higher growth rates after switching (as they capitalize on experience to identify better matches) — and tend to be happier
In the wider world of work, finding a goal with high match quality in the first place is the greater challenge, and persistence for the sake of persistence can get in the way.

Chapter 7

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.
Because personality changes more than we expect with time, experience, and different contexts, we are ill-equipped to make ironclad long-term goals when our past consists of little time, few experiences, and a narrow range of contexts.
“We discover the possibilities by doing, by trying new activities, building new networks, finding new role models.” We learn who we are in practice, not in theory. [test-and-learn versus plan-and-implement]

Chapter 8

Outside-in thinking — finding solutions in experiences far outside of focused training for the problem itself
Einstellung effect — a psychology term for the tendency of problem solvers to employ only familiar methods even if better ones are available.
It isn’t just the increase in new knowledge that generates opportunities for nonspecialists, though. In a race to the forefront, a lot of useful knowledge is simply left behind to molder. That presents another kind of opportunity for those who want to create and invent but who cannot or simply do not want to work at the cutting edge. They can push forward by looking back; they can excavate old knowledge but wield it in a new way.

Chapter 9

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.
Individual creators started out with lower innovativeness than teams — they were less likely to produce a smash hit — but as their experience broadened they actually surpassed teams: an individual creator who had worked in four or more genres was more innovative than a team whose members had collective experience across the same number of genres.
Facing uncertain environments and wicked problems, breadth of experience is invaluable. Facing kind problems, narrow specialization can be remarkably efficient. The problem is that we often expect the hyperspecialist, because of their expertise in a narrow area, to magically be able to extend their skill to wicked problems. The results can be disastrous.

Chapter 10

The foxes, meanwhile, “draw from an eclectic array of traditions, and accept ambiguity and contradiction,” Tetlock wrote. Where hedgehogs represented narrowness, foxes ranged outside a single discipline or theory and embodied breadth.
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.
On average, forecasters on the small superteams became 50 percent more accurate in their individual predictions. Superteams beat the wisdom of much larger crowds — in which the predictions of a large group of people are averaged — and they also beat prediction markets, where forecasters “trade” the outcomes of future events like stocks, and the market price represents the crowd prediction.
Narrow experts are a valuable resource, but may be blind to certain perspectives/possibilities
The best forecasters view their own ideas as hypotheses in need of testing; they try to get teammates to help them falsify their own notions so they can move closer to the truth
The best forecasters are high in active open-mindedness. They are also extremely curious, and don’t merely consider contrary ideas, they proactively cross disciplines looking for them
Depth can be inadequate without breadth
Fox habits
Generate a list of separate events with deep structural similarities — rather than focusing only on internal details of the specific event
Dissect prediction results in search of lessons — need rigorous feedback for a more difficult learning environment

Chapter 11

Overlearned behavior — they have done the same thing in response to the same challenges over and over until the behavior has become so automatic that they no longer even recognize it as a situation-specific tool
There are no tools that cannot be dropped, reimagined, or repurposed in order to navigate an unfamiliar challenge.
The experiments showed that an effective problem-solving culture was one that balanced standard practice — whatever it happened to be — with forces that pushed in the opposite direction.
If managers were used to process conformity, encouraging individualism helped them to employ “ambidextrous thought,” and learn what worked in each situation.
If they were used to improvising, encouraging a sense of loyalty and cohesion did the job.
The trick was expanding the organization’s range by identifying the dominant culture and then diversifying it by pushing in the opposite direction.
Individuals who live by historian Arnold Toynbee’s words that “no tool is omnicompetent. There is no such thing as a master-key that will unlock all doors.” Rather than wielding a single tool, they have managed to collect and protect an entire toolshed, and they show the power of range in a hyperspecialized world.

Chapter 12

“Take your skills to a place that’s not doing the same sort of thing. Take your skills and apply them to a new problem, or take your problem and try completely new skills.”
The deliberate amateur goes out of their way to find domains tangential or completely outside of their expertise to continue learning in different subjects — even if that learning is only shallow
The point is to not restrict yourself in your exploration and being able to glean takeaways from disparate fields
Mental meandering can lead to broader conceptual skills that will apply to your domain
In professional networks that acted as fertile soil for successful groups, individuals moved easily among teams, crossing organizational and disciplinary boundaries and finding new collaborators. Networks that spawned unsuccessful teams, conversely, were broken into small, isolated clusters in which the same people collaborated over and over.

Conclusion

The popular notion of the Tiger path minimizes the role of detours, breadth, and experimentation. It is attractive because it is a tidy prescription, low on uncertainty and high on efficiency. After all, who doesn’t like a head start? Experimentation is not a tidy prescription, but it is common, and it has advantages, and it requires more than the typical motivational-poster lip service to a tolerance for failure. Breakthroughs are high variance.
Don’t feel behind — compare yourself to yourself yesterday, not to younger people who aren’t you; everyone progresses at a different rate
Be willing to learn and adjust as you go — even abandoning goals altogether and changing directions if needed