By David Epstein August 01, 2021 ⋅ 11 min read ⋅ Books
Introduction: Roger vs. Tiger
Roger Federer is a professional tennis player and Tiger Woods is a professional golfer.
Tiger symbolizes the idea that the quantity of deliberate practice determines success.
Roger symbolizes the idea of trying out many fields and taking ideas from all fields to specialize and push a field.
In every field, the response to a ballooning library of human knowledge and an interconnected world has been to specialize and narrow our focus.
Eventual elites typically devoted less time early on to deliberate practice in their field of expertise and instead undergo a sampling period.
Only later do they focus in and ramp up technical practice in one area.
We know that early sampling is key as is diversity.
We’re often told that changing directions is dangerous and a waste of time compared to people that have been working at it for longer.
Some research has shown that highly credentialed experts can become so narrow-minded that they actually get worse with experience.
Studies show that among the fastest-growing startups, the average age of a founder was 45 when the company was launched.
There are advantages in breadth and in delaying specialization.
While we need more Tigers of the world that specialize and see only a small part of the world, we also need more Rogers that start broad and embrace diverse experiences and perspectives while they progress.
We need people with range.
Chapter 1: The Cult of the Head Start
Review of Laszlo Polgar’s experiment on his three daughters.
Like the Tiger Woods story, the Polgar story entered mainstream culture about the power of an early start.
Research has shown that experts in a variety of fields are remarkably similar to chess masters in that they instinctively recognize familiar patterns built up by experience.
E.g. Chess, firefighting, and naval commanders.
However, work by Daniel Kahneman has shown the opposite, that experience develops confidence but not skill.
One literature review showed that experience simply didn’t create skill in a wide range of real-world scenarios.
E.g. College administrators assessing student potential, psychiatrists predicting patient performance, and HR professionals predicting job candidates.
In these domains where human behaviour is involved and where patterns don’t repeat, repetition and experience didn’t cause learning.
Chess, golf, and firefighting are exceptions because they’re fields with well defined goals and quantitative performance, but they aren’t the rule.
Do specialists get better with experience or not?
Whether experience inevitably leads to expertise depends entirely on the domain in question.
Certain domains where instinctive pattern recognition works powerfully are called “kind” learning environments.
In kind environments, patterns repeat over and over, feedback is extremely accurate and fast, the rules and limits stay constant, and performance is easily measured.
Deliberate practice does extremely well in kind environments.
However, not all environments are kind, the rest are called “wicked” domains.
In wicked environments, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns that are obvious, and feedback is often delayed, inaccurate, or both.
In the most devilishly wicked learning environments, experience will reinforce the wrong lessons.
E.g. A physician physically examining a patient and getting the correct diagnosis only because the physician is spreading said diagnosis through physical examination.
If the rules of the game change, experience becomes less valuable.
E.g. How elders in a community provide less valuable information if the world changes to quickly because the knowledge they have isn’t relevant for the current world.
Moravec’s paradox: that machines and humans frequently have opposite strengths and weaknesses.
In chess, computers are very good at tactics (short-term actions) but lack in strategy (long-term planning). Humans are the opposite.
By combining both humans and computers, we get the best tactics and strategy.
If chess experts are given a chess board with pieces to memorize and recreate, the level of expertise matches the accuracy of the recreation.
E.g. Grandmasters could accurately recreate the entire board, masters a little less so, amateurs almost not at all.
However, if the chess pieces are place in a configuration that would never actually occur in a game, suddenly the experts perform as well as the amateur players.
Instead of remembering the position of every piece, experts use chunking to group familiar patterns thus enabling them to recreate the board more accurately.
Chunking helps explain instances of apparently miraculous, domain-specific memory.
E.g. Musicians playing long pieces by heart, quarterbacks recognizing where to throw in a split second, and the superhuman reflexes of elite athletes.
We all intuitively chunk words into sentences.
Chunking can seem like magic but it comes from extensive, deliberate practice.
This results in islands of genius where certain skills in experts are extraordinary but other skills are average or below average.
Patterns and familiar structures were critical to the savant’s extraordinary recall ability or any expert.
The bigger the picture and the more complex it is, the more unique the potential human contribution.
Our greatest strength is the exact opposite of narrow specialization. It’s our ability to integrate and to generalize.
When a domain has rules and answers that are stable over time, like chess, golf, and music, an argument can be made for savant-like hyperspecialized practice from day one.
But not everything, and most domains, aren’t stable and have answers.
E.g. Scientific research, business, and hospitals.
We’ve been using the wrong stories to teach success. The Tiger and Polgar stories give the false impression that human skill is always developed in an extremely kind learning environment.
If the amount of early, specialized practice in a narrow area was key to performance, then savant and child prodigies would dominate every domain.
However no savant has ever been known to become a significant creator who changed their field.
When the rules of a domain are altered slightly, it makes experts appear to have traded flexibility for narrow skill.
E.g. If the rules of chess are changed, then expert chess players do as well as or worse than amateurs.
Experts perform worse than novices when the rules change, a phenomenon called cognitive entrenchment, because it’s difficult for experts to unlearn their previous knowledge.
One solution is to vary challenges within a domain drastically or to always have one foot outside your world.
E.g. Compared to other scientists, Nobel laureates are at least 22 times more likely to partake as an amateur in a nonscientific field.
Rather than obsessively focusing on a narrow topic, creative achievers tend to have broad interests.
“This breadth often supports insights that can’t be attributed to domain-specific expertise alone.” - Dean Simonton
E.g. Steve Jobs combined calligraphy class with design aesthetics. Claude Shannon combined philosophy logic with telephone call-routing technology.
“It just happened that no one else was familiar with both of those fields at the same time.” - Claude Shannon
The successful adapters were excellent at taking knowledge from one pursuit and applying it creatively to another, avoiding cognitive entrenchment.
The skill is to avoid the same old patterns by breaking them with outside experiences and analogies.
In the wicked world, range is an advantage.
Chapter 2: How the Wicked World Was Made
Flynn effect: the increase in IQ score with each new generation.
Premodern people miss the forest for the trees; modern people miss the trees for the forest.
The more modern people become, the more powerful their abstract thinking and the less they rely on concrete experience.
E.g. Premodern people from tribes and hunter-gatherer societies do poorly on abstract-reasoning tests due to relying heavily on concrete experiences.
Our most fundamental thought processes have changed to accommodate for the increasing complexity of the modern world and the need to derive new patterns.
Premodern villagers are capable of learning from experience, but fail to learn without experience.
And that’s what a rapidly changing, wicked world demands, conceptual reasoning skills that can connect new ideas and work across domains.
When faced with a new problem, the villagers were completely lost. That isn’t an option for us.
The ability to apply knowledge broadly comes from broad training.
Chapter 3: When Less of the Same Is More
Whether it’s music or golf, the message to become an expert is the same: choose early, focus narrowly, and never waver.
However, there are many routes to expertise.
The sampling period before specializing isn’t useless, it’s necessary to the process of developing expertise.
No notes of the musicians studied in this chapter.
E.g. Tiger Mother and Jack Cecchini.
The more contexts in which something is learned, the more the learner creates abstract modules, and the less they rely on any specific example.
“It’s strange that some of the greatest musicians were self-taught or never learned to read music. I think when you’re self-taught, you experiment more, you learn how to solve problems.” - Jack Cecchini
Chapter 4: Learning, Fast and Slow
For learning that’s both durable and flexible, fast and easy is the problem.
We often learn in school the using-procedure way of solving problems instead of the making-connections way.
E.g. Shortcuts and rules instead of actual understanding and connecting it to the real world.
Desirable difficulties are obstacles that make learning harder and longer in the short term, but better in the long term.
Generation effect: being asked to generate the answer on your own without hints.
Being forced to generate answers improves learning, even if the generated answer is wrong.
Learning with hints didn’t produce any lasting learning.
Testing is a very desirable difficulty.
Struggling to retrieve information primes the brain for subsequent learning, even when the retrieval itself is unsuccessful.
Space between practice sessions creates the hardness that enhances learning.
Learning is most efficient in the long run when its really inefficient in the short run.
“Teachers and students must avoid interpreting current performance as learning. Good performance on a test during the learning process can indicate mastery, but we should be aware that such performance will often index fast but fleeting progress. ” - Robert Bjork
Chapter 5: Thinking Outside Experience
Recounting the story behind Johannes Kepler’s discovery of how planets move.
Kepler experimented with many analogies before settling on push-pull forces for explaining how planets move.
Deep analogical thinking is recognizing conceptual similarities in multiple domains or scenarios that may seem to have little in common on the surface.
Analogical thinking is a powerful tool for solving wicked problems.
Successful problem solvers are more able to determine the deep structure of a problem before they proceed to match a strategy to it.
When all members of a group have the same knowledge and a problem comes up, the group acts more like a single individual and doesn’t make analogies to bridge distant fields.
Chapter 6: The Trouble with Too Much Grit
Review of Vincent van Gogh’s life.
Review of grit by Angela Duckworth.
The important decision in quitting is to know whether it’s due to a failure to persevere or the realization that better matches are available.
Chapter 7: Flirting with Your Possible Selves
Review of Frances Hesselbein’s career.
Our work and life preferences changes because we change.
People tend to predict the future will be similar to the present and that the past is different to the present.
Predictors expect that they would change very little in the next decade, while reflectors reported having changed a lot in the previous decade.
The precise person you are now is fleeting, just like all the other people you’ve been.
The greatest personality changes happen between age eighteen and late twenties, so specializing early doesn’t make sense for a person that may change.
We learn who we are only be living, and not before.
Test and learn, not plan and implement.
Chapter 8: The Outsider Advantage
Most established companies tend to approach problems with a local search using specialists from a single domain and trying solutions that worked before.
However, some problems require an outside-in view to solve.
The trick is to frame the challenge so that it attracts a diverse array of solvers. The more people it attracts, the more likely it is to be solved.
We may think that only hyperspecialized experts can drive modern innovation, but increasing specialization actually creates new opportunities for outsiders.
Knowledge is a double-edged sword. It allows you to do some things but also makes you blind to other things that you could do.
How can frontiers be pushed if one day it’ll take a lifetime just to reach them in each specialized domain?
Sometimes, a problem’s home field can be so constrained that a curious outsider is truly the only one who can see the solution.
The larger and more easily accessible the library of human knowledge, the more chances for people to make connections at the cutting edge.
One way of pushing knowledge is by looking back; by excavating old knowledge but wielding it in a new way.
Chapter 9: Lateral Thinking with Withered Technology
Lateral thinking: reimaging of information in new contexts.
Withered technology: old but extremely well-understood technology and easily available.
Review of Gunpei Yokoi’s story at Nintendo.
Unquestionably, Yokoi needed narrow specialists.
Yokoi was concerned that as companies grew and technology progressed, vertical-thinking hyperspecialists would be valued but lateral-thinking generalists would not.
He felt that both lateral and vertical thinkers were best together.
The world is both broad and deep.
We’re concerned that science is increasingly overflowing with narrow specialists.
Review of the development of multilayer optical film.
If specialists say ‘It’s a great idea, go for it, makes sense’, what’s the chance you’re the first person to come up with it? Probably zero.
There are specialized inventors that focused on a single technology, and generalist inventors who weren’t leading experts in anything, but had works across many domains.
Both types are equally valuable, but inventors who had neither significant depth nor breadth rarely made an impact.
Generalists add value by integrating domains, while specialists add value by working on difficult problems for a long time.
Polymaths: an inventor that’s broad with at least one area of depth.
Specialization is obvious: just keep going straight. But generalization is trickier to grow.
I-shaped people are specialists while T-shaped people are polymaths.
If you’re working on well-defined and well-understood problems, specialists work very well. But as uncertainty and ambiguity increase, breadth becomes increasingly important.
In comic book creation, length of experience of the author didn’t differentiate creators, but breadth of experience did.
Broad genre experience made creators better on average and more likely to innovate.
Chapter 10: Fooled by Expertise
Many experts never admit to systematic flaws in their judgment, even in the face of results.
Review of Philip Tetlock’s research.
People are more likely to seek information that matches and confirms their beliefs rather than seek out contrary arguments.
None of this is to say that experts are unnecessary because they also produce vital knowledge.
E.g. Albert Einstein and Alan Turing.
In wicked domains that lack automatic feedback, experience alone doesn’t improve performance.
We often don’t do a good job of asking “Is this the data that we want to make the decision we need to make?”
There’s danger in reaching conclusions from incomplete data and basing it only on the data in front of you.
Focusing too much on quantitative and data-driven arguments can lead to bad decisions.
E.g. Challenger shuttle tragedy.
Experienced groups become rigid under pressure and often regress to their habits.
E.g. The case of wildland firefighters that could’ve survived if they’d dropped their tools.
Experienced professionals rely on overlearned behaviour and therefore aren’t as flexible in new situations.
There are no tools that can’t be dropped, reimagined, or repurposed to navigate an unfamiliar challenge.
An effective problem-solving culture is one that balances standard practices with exploration.
Teams need both elements of hierarchy and individualism to both excel and survive.
Chapter 12: Deliberate Amateurs
We should encourage students to think laterally, broaden their experience, and forge their own path in search of match quality.
“Don’t end up as a clone of your thesis advisor.” - Smithies
There is no standard relationship between experience and contribution in researchers’ careers.
Work that builds bridges between disparate pieces of knowledge is less likely to be funded, less likely to appear in famous journals, more likely to be ignored upon publication, and then more likely in the long run to be impactful.
In research, it’s ok to be inefficient and to play around. Not everything must be hyperoptimized and have immediate applications.
When you push the boundaries, a lot of it is just probing which is inherently inefficient.
Conclusion: Expanding Your Range
The popular notion of the Tiger path minimizes the role of detours, breadth, and experimentation.
It’s attractive because it’s low on uncertainty and high on efficiency.
The main advice from this book: Don’t feel behind.
But also remember that there’s nothing inherently wrong with specialization.
We all specialize at some point, but we don’t have to rush to get there.