Range: Why Generalists Triumph in a Specialized World by David Epstein
The Book in Three Sentences
Epstein argues creative achievers tend to have broad interests in adjacent domains and take expertise accrued in one area and apply it to a completely new one (innovation). Instead of working backward from a goal, work forward from promising situations, reflect, and adjust your personal narrative. By struggling to generate an answer on your own — even a wrong one — you significantly enhance long-term learning.
Abstract Thinking
Rather than using 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. This is a relatively new idea. (p44)
Classifying things into categories like materials (wood, glass, metal), size (large, small), and colour (light, dark), is one of the chief characteristics of abstract thinking (the ability to move freely, and shift from one category to the other). (p47)
Long-term Gains Over Short-term Wins
Parents want their kids doing what Olympians are doing right now, not that they were doing when they were twelve or thirteen. Children should develop general athleticism and allow be allowed to probe their interest before focusing on a narrowly on technical skills. (p64)
Creativity is difficult to nurture and easy to thwart. Households with typical children have an average of six rules, while households with the most creative children have only one. The trick is not to be prescriptive about rules; instead, make corrections after as bad behaviour arises. (p77)
Generation Effect is a form of desirable difficulty that prioritizes long-term learning over short-term gains. Struggling to generate an answer on your own, even a wrong one, significantly enhances subsequent learning. (p85)
Distributed practice, or space between practice sessions, forces the participant to recall information which actually enhances learning. Struggling to hold onto information and recall it is what transfers information from short-term memory to long-term memory, or “deep learning” by making connections. (p88–91)
Block Learning vs Interleaving information
Block learning — practicing the same problem over and over using the same procedure — leads to immediate performance improvements. However, this kind of learned makes for inflexible knowledge that is often forgotten in the long run. (p94)
For knowledge to be flexible, it should be learned under varied conditions using an approach called varied or mixed practice, referred to as “interleaving.” Interleaving improves inductive reasoning. (p94)
Interleaving will without a doubt be more frustrating in the moment, but the struggle pays off in the long-term. (p95)
For example, students who learned in blocks — examples of a particular problem at once — performed a lot worse in the long run than those who studied the exact same problems mixed with other types. The take away: block-practice students learned procedures for each type of problem through repetition, while mixed-practice students learned how to differentiate types of problems. (p94–5)
Whether the task is mental or physical, interleaving improves the ability to match the right strategy to a problem. Successful problem solvers spend mental energy figuring out what type of problem they are facing before matching a strategy to it. (p96)
Successful problem solvers are more able to determine the deep structure of a problem before they proceed to match a strategy to it. As John Dewey put it in Logic, The Theory of Inquiry, “a problem well put is half-solved. (p115)
Kind vs Wicked Learning Environments
Narrow experience makes for better firefighters and chess players, but not for predicting political or financial trends. Instinctive pattern recognition where feedback is extremely accurate and rapid makes for kind learning environments. (pp20–21)
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. (p21)
In kind learning environments, chess grandmasters use the technique of chunking groups of pieces into meaningful sequences through the repetitive study of game patterns. (Not a photographic memory!) Chunking helps explain instances of apparently miraculous domain-specific memory, like that of chess masters and long musical solos. (p25)
Thinking in Analogies
Dedre Gentner studies Johannes Kepler to understand analogical thinking. 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. Thinking in analogies takes a new idea and puts it in a new light, allowing us to reason through problems that we have never seen. For example, the principles of electricity can be understood with analogies to water flowing through plumbing. (p102–3)
Kepler was unlike Galileo and Newton, the documented his confusion and reasoning.
Specialist vs Polymath
Insist on having one foot outside your world. The most successful experts also belong to the wider world. Rather than obsessively focusing on a narrow topic, creative achievers tend to have broad interests that support insights that cannot be attributed to domain-specific expertise alone. “It just happened that no one else was familiar with both those fields at the same time.” (pp32-34)
People tend to utilize familiar methods even if there are better ones available. This is especially true with most specialists. (p177)
As specialization becomes more prevalent, thinking and working inside the box actually looks more like a Russian nesting doll situation: even thinking outside the box only gets the super-specialist to the next, slightly larger one. (p177)
Polymaths in Epstein’s definition have even more breath than generalists and might work across dozens of domains. They are able to take expertise accrued in one domain and apply it to a completely new one, constantly learning new technologies. Over the course of their careers, they learn about “the adjacent stuff” and become highly innovative. (p204–5)
If you’re working on well-defined and well-understood problems, specialists work very well. However, in the age of ambiguity, uncertainty, and knowledge work, breath becomes increasingly important. (p207)
T-shaped learners and polymaths use networks of people and ideas to find solutions to complex problems. Build a narrative to figure out the fundamental questions to ask, and ask the people who know the answers — ultimately you’ll be in the same place as actually having that knowledge in the first place. You are building a mosaic. (p207)
Kind learning environments are best for specialization. The American National Transportation Safety Board found that 73% of major flight incidents occurred on a flight crew’s first day of working together. Repeatable, procedural work like flights and surgeries are best when practiced. (p211)
The best serial innovators, on the other hand, have characteristics like:
high tolerance for ambiguity
systems thinking
additional technical knowledge from peripheral domains
the ability to synthesize information
broad range of interests
read more, and very broadly
the need to learn significantly across multiple domains
(p211–2 with references to Serial Innovators by Abbie Griffin et all)
Narrow experts are an invaluable resource, but you must understand they may have blinders on. What you need to do is take the facts, not opinions, and integrate them. (p225)
The average expert is a terrible forecaster. One who becomes entrenched in their single big idea of how the world works make poorer predictions, even in the face of contrary facts. (p218)
The best forecasters practice active open-mindedness and view their own ideas as hypotheses in need of testing. Next time you’re trying to find the answer trying looking for why you might be wrong first! (p227)
Match Quality
If we treated careers more like dating, nobody would settle down so quickly. Adults are more likely to get divorced from their careers they invested in if they settle down too early. For professionals who do switch, you do lose a fraction of your skills, but you achieve a higher growth rate after switching. (p130–1)
Quitting a job takes a lot of guts after having invested time or money into something. Your time and money are already gone, so avoid what Seth Godin calls the “sunk cost fallacy.” (p143)
Maximize match quality throughout your life by sampling activities, social groups, contexts, jobs, careers, and then reflecting and adjusting your personal narrative. (p161)
Instead of working back from a goal, work forward from promising situations.
Focus on a test-and-learn model of life. Forget the typical plan-and-implement model of life, where you first make a long-term plan and execute without deviation. (p164)
Your proactive pursuit of match quality should include small experiments—Tim Ferriss suggests two week increments—where you try something you’re interested in and adjust as you go. (p290, sourcing Herminia Ibarra)
Brace Yourself
Work that builds bridges between disparate pieces of knowledge is less likely to be funded, less likely to appear in famous journals, and more likely to be ignored upon publication. However, studies that make multiple new knowledge combinations have been proven to be in the top percentile of most-cited papers 5-15 years down the road. (p282)
(Enrico) Fermi Problems
“How many piano tuners are in New York City?” can be solved by deductive reasoning, like knowing the population of New York and estimating how many households (this how many pianos), how many homes can a tuner do in a day, how often, how long, etc. These are called “Fermi problems” where the lesson is that detailed prior knowledge is less important than a way of thinking. (p52)