Range
In a world where the only certain thing is uncertainty, range - in skills, experiences, way of thinking, openness to new ideas - is the key to adaptability, creative problem solving, and thriving.
“Range: How Generalists Triumph in a Specialized World" by David Epstein is not a manifesto against specialization: there are situations where specialization works remarkably well. And we all have areas where we want to go deep because it matches our abilities and proclivities. But hyper-specialization can backfire, especially when it comes to "wicked", "vuca" environments.
I've read this book not to be convinced of the benefits of range - it happens that on my meandering life & career path I had already embraced range wholeheartedly, - but to better understand theories that underlie the benefits of range and to get ideas and inspiration from multiple stories told in this book.
My Key Takeaways
The advantages of the head start in deliberate practice are overrated. Experience helps in kind learning environments and can backfire badly in wicked ones.
Paradox: complex world increases the average person's IQ and abstract thinking ability, but education pushes for early specialization
Less of the Same is More.
Sticky and flexible learning is slow and hard, not fast and easy.
To solve new, unexpected problems, you need to understand the deep structure of the problem and think outside the box.
You can be an early bloomer, or a late bloomer - whatever the case, it usually takes a fair amount of experimentation and quitting unpromising paths.
In the pursuit of match quality, we should flirt with our possible selves. Instead of working back from a goal, work forward from promising situations.
Difficult problems can benefit from crowdsourcing outsiders' perspectives.
Sometimes the most innovative solutions come from lateral thinking with withered technology.
Hyper-specialists score badly on wicked problems. "Experts" are notoriously bad at predicting the future. They make good TV, but not good judgments. Integrators beat narrow experts, especially on long-term predictions.
In wicked environments, we sometimes need to drop familiar tools and look for data/methods beyond those which we typically use.
Innovation ecosystems need breadth and allow for inefficiency. Unfortunately, driven by efficiency and short-term, current ecosystems encourage hyper-specialization.
Don't feel behind. If you want to compare, compare yourself to yourself yesterday, not to someone else.
The advantages of the head start in deliberate practice are overrated. Whether or not experience leads to expertise depends on the domain in question.
Psychologist Robin Hogarth distinguishes between kind and wicked learning
environments.In kind learning environments patterns repeat over and over, feedback is accurate and rapid (e.g. golf, chess, music, surgery, firefighting, certain video games, etc). In these environments, instinctive pattern recognition (called "naturalistic decision making" by psychologist Gary Klein) works powerfully. The learner improves simply by engaging in the activity and trying to do better.
In wicked learning environments the rules of the game are often unclear or incomplete, there may be no repetitive patterns, and feedback can be delayed or inaccurate. Daniel Kahnemann studied human decision-making from the "heuristics and biases" model and found that in wicked environments experience does not help performance. It frequently bred confidence but not skill. When narrow specialization is combined with an unkind domain, the human tendency to rely on familiar patterns can backfire. Wicked environments require creativity and
flexibility.Moravec's paradox: machines and humans frequently have opposite strengths and weaknesses. Our greatest strength is the exact opposite of specialization. It's our ability to integrate broadly.
Research shows that rather than obsessively focus on a narrow topic, creative achievers tend to have broad interests and keep multiple "career streams"
open even as they pursued a primary specialty.Our world is largely a wicked environment with ill-defined challenges and few rigid rules, and range can be a life hack.
Paradox: complex world increases the average person's IQ and abstract thinking ability, but education pushes for early specialization.
James Flynn’s study - IQ has been increasing with each new generation in the XXth century.
Alexander Luria's research in the 1930s: changing people's work might change their minds. "Eduction" thinking: the ability to work out guiding principles when given facts or materials, even in the absence of instructions. Premodern people miss the forest for the trees; modern people miss the trees for the forest.
The paradox is that while our mind broadens, higher education responds to it by pushing specialization, rather than focusing early on conceptual, transferable knowledge.
Students must be taught to think before being taught what to think about.
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.
Less of the Same is More
Tiger's Mom approach: choose early, focus narrowly, never waver. Underlying assumption: the quantity of deliberate practice determines success.
Reality: eventual elites often undergo a "sampling period", trying many different things and learning about their own abilities and proclivities. Only later do they focus on and ramp up deliberate practice in one area.
Multiple stories confirm this in music ( Figlie del Coro, Jack Cecchini, Django Reinhardt), sport (many examples, incl. Roger Federer).
The breadth of training predicts breadth of transfer: the more contexts in which you learn something, the more you create abstract models, the less you rely on any particular example. You become better at applying your knowledge to a situation you've never seen before - this is the essence of creativity.
Creativity may be difficult to nurture, but it is easy to thwart.
Sticky and flexible learning is slow and hard, not fast and easy.
At school, teachers rely on two types of questions: (1) "using procedures" questions where you practice what you have just learned and (2) "making connections" questions that connect you to a broader concept, rather than just a procedure. "Using procedures" questions are more popular among teachers and in textbooks, they are easier and fast. As a result, students end up viewing various subjects as a set of procedures, and not as systems.
But for learning that is both durable and flexible, fast and easy is a problem.
Instead, learning should incorporate "desirable difficulties", obstacles that make learning more challenging, slower, and more frustrating in the short term, but better in the long term. Struggling to generate an answer enhances subsequent learning.
Repetition is less important than struggle.
Teachers and students must avoid interpreting current performance as learning.
Teachers who, instead of teaching to the test, facilitate a deeper understanding of
underlying concepts, make their classes frustrating and difficult. They may receive negative immediate feedback, but they inspire better student performance later on.If we practice the same thing repeatedly, it can lead to excellent immediate performance, but fleeting long-term knowledge. Flexible knowledge requires interleaving - learning under varied conditions, mixed practice. Interleaving improves the ability to match the right strategy to a problem. The most successful problem solvers spend mental energy figuring out what type of problem they are facing before matching a strategy to it, rather than jumping in with memorized procedures.
To solve new, unexpected problems, you need to understand the deep structure of the problem and think outside the box.
What do we do when we encounter a problem that we have never encountered before? We try to find conceptual analogies that go beyond superficial ones. Famous example: Kepler and his gradual invention of astrophysics.
Deep analogical thinking is the practice of recognizing conceptual similarities in multiple domains or scenarios that may seem to have little in common. Our ability to think relationally is one of the reasons we're running the planet.
Analogical thinking takes new and makes it familiar, or takes familiar and puts it in a new light. It allows us to reason through the problems we have not encountered before. Good example: intranet site of Boston Consulting Group containing collections of material to facilitate wide-ranging analogical thinking.
However, we should be cautious not to draw analogies solely from very similar
situations.Kahneman and Tversky coined the term "inside view": when we make judgments based narrowly on the details of a particular project that is right in front of us. "Outside view", on the contrary, encourages us to go beyond superficial similarities and look for deep structural similarities in a broader context.
Dan Lovallo's experiment with a group of private equity investors who estimated that the return on their own project would be about 50% higher than the outside projects they had identified as conceptually similar.
Ambiguous sorting task experiment proves that successful problem solvers are more able to determine the deep structure of a problem before they proceed to match strategy to it: a problem well-put is half-solved.
Kevin Dunbar's studies on how productive labs work have shown that the most productive labs have members with diverse backgrounds and widely use analogies: faced with an unexpected finding, rather than assuming that the current theory is correct and that the observation must be off, the unexpected became an opportunity to venture somewhere new and analogies served as the wilderness guide.
You can be an early bloomer, or a late bloomer - whatever the case, it will take a fair share of experimentation and quitting
"Match Quality" is the term economists use to describe the degree between the work someone does and who they are - their abilities and proclivities.
Ofer Malamud's study: the benefits to increased match quality outweigh the greater loss in skills. Learning stuff is less important than learning about yourself. Exploration is not just the whimsical luxury of education; it is its central benefit.
If we treated careers like dating, nobody would settle down so quickly. Career
switchers capitalize on experience to find better matches.Switchers are winners: they quit fast and often when they detect that the plan is not the best fit, and they do not feel bad about it.
The barrier to switching: sunk cost mindset.
In the pursuit of match quality, we should flirt with our possible selves.
Frances Hesselbein's story: an extraordinary career that she started in her
mid-fifties.Rose & Ogas's study: virtually every successful person they studied followed an unusually winding career path. People they studied did pursue a long-term goal but only formulated it after a period of discovery.
Gilbert's study measured the preferences, values, and personalities of more than 19,000 people. The only certainty is change. Some personality traits change over time in fairly predictable ways; others - don't. Our work & life preferences do not stay the same, because we don't stay the same. We are all works in progress claiming to be finished. Specializing early is a task of predicting match quality for a person who does not yet exist.
Herminia Ibarra's studies: we maximize match quality throughout life by sampling activities, social groups, contexts, jobs, careers, and then reflecting and adjusting our personal narratives. In the pursuit of match quality, first, act and then think is the right approach. Test and learn rather than plan and implement.
Paul Graham: most of the work I've done in the last ten years didn't exist when I was in high school… In such a world it's not a good idea to have fixed plans.
Instead of working back from a goal, work forward from promising situations.
Difficult problems can benefit from crowdsourcing outsiders' perspectives.
InnoCentive story: initiated by Alph Bingham @ Eli Lilly, this initiative facilitates entities in any field acting as "seekers", paying to post "challenges" and rewards for outside "solvers". The trick: frame the challenge to attract a diverse array of solvers. This is "outside-in" thinking. Examples of problems solved: NASA prediction of solar particle storms; Exxon Valdez oil cleanup.
Another example of crowdsourced innovation: Kaggle.
We benefit from outsiders' perspective because of the Einstellung effect: our own tendency to employ only familiar methods even if better ones are available. Knowledge is a double-edged sword. It allows you to do some things, but it also makes you blind to other things that you could do.
Swanson's studies & Arrowsmith system: the more information specialists create, the more opportunities exist for curious dilettantes to contribute by merging strands of widely available but disparate information - undiscovered public knowledge.
Sometimes the most innovative solutions come from lateral thinking with withered technology.
Nintendo/ Gunpei Yokoi story. Lateral thinking: reimagining the information in new contexts. Withered technology: old enough to be extremely well understood and easily available, so it does not require a specialist's knowledge. Yokoi advised employees not just to play with technology, but play with ideas. Don't be an engineer, be a producer.
Birds and Frogs. Freeman Dyson: we need both focused frogs and visionary birds.
"Birds fly high in the air and survey broad vistas. They delight in concepts that unify our thinking and bring together diverse problems from different parts of the landscape. Frogs live in the mud below and see only the flowers that grow nearby. They delight in the details of particular objects, and they solve problems one at a time". Birds are not better than frogs or vice versa. The world is both broad and deep. "We need birds and frogs working together to explore it".Andy Oudekirk's story & study of inventors. The study uncovered a particular type of inventor that was most likely to succeed: broad with at least one area of depth, and called them "polymaths". Polymaths had depth in a core area but they were not as deep as specialists. They also had breadth, even more than the generalists, having worked across dozens of technology classes.
Jayshree Seth's story (3M): she describes herself as a "T-shaped" person who has breadth, compared to an "I-shaped" person who only goes deep. "My inclination is to attack a problem by building a narrative. I figure out the fundamental questions to ask, and if you ask those questions of the people who actually do know their stuff, you are still exactly where you would be if you had all this other knowledge inherently. It's mosaic building."
If you're working on well-defined and well-understood problems, specialists work
very, very well. As ambiguity and uncertainty increase, breadth becomes increasingly important.Taylor & Greve's study of comic book successes: high repetition workload negatively impacts performance. Years of experience had no impact at all. Broad genre experience made creators better on average and more likely to innovate.
Superman or Fantastic Four? When seeking innovation in knowledge-based industries, it is best to find one 'super' individual. If no individual with the necessary combination of diverse knowledge is available, one should form a fantastic team.
In kind environments where the goal is to recreate prior performance with as
little deviation as possible (surgical teams, golfers, airline crews), teams of specialists work superbly. When the path is unclear, the same routines no longer suffice.Abbie Griffin's research on "serial innovators" uncovered the following qualities: a high tolerance for ambiguity, system thinking, additional technical knowledge from peripheral domains, repurposing what is already available, ability to connect disparate pieces of information in new ways, a broad range of interests, need to communicate with various individuals with technical expertise outside of their own domain. Examples: Charles Darwin, Thomas Edison. They are "π-shaped"
people who dive in and out of multiple specialties.Problem is that the employees that are potential serial innovators would not fit ill-defined, specialized slots of HR policies. A mechanistic approach to hiring reduces the number of high-potential for innovation candidates.
Hyperspecialists score badly on wicked problems. "Experts" are notoriously bad at predicting the future. They make good TV, but not good judgments. Integrators beat narrow experts, especially on long-term predictions.
Paul Ehrlich / Julian Simon narrow, opposing views. As each man amassed more information for his own view, each became more dogmatic.
Philip Tetlock's story & study of expert forecasting. The average expert is a horrific forecaster, both long and short term. There is also a perverse relationship between fame and accuracy. One group that outperforms other forecasters is integrators.
Hedgehogs and foxes (a term from Isaiah Berlin's essay). Narrow-view hedgehogs "know one big thing", integrator foxes "know many little things". Hedgehogs toil devotedly within one tradition and reach for formulaic solutions to ill-defined problems. Foxes draw from an eclectic array of traditions and accept ambiguity and contradiction.
Beneath complexity, hedgehogs tend to see cause-and-effect relationships. 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 repeats, it does not do so precisely.
Advantage of hedgehogs - their single focus makes it easy to fashion compelling stories about anything that occurred. They make great TV.
IARPA's prediction tournament & Good Judgement Project. Tetlock and Mellers recruited not the decorated experts but volunteers and gradually identified the "foxiest" forecasters and weighed the team's forecasts towards them. The best forecasters view their own ideas as hypotheses in need of testing. Their aim is to encourage teammates to falsify their own notions. Interestingly, the aversion to contrary ideas tends to manifest itself even in the most scientifically literate adults.
Jonathan Baron's concept of active open-mindedness: being extremely curious, not only considering contrary ideas but proactively cross disciplines looking for
them. Depth can be inadequate without breadth.
In wicked environments, we sometimes need to drop familiar tools and look for data/methods beyond those which we typically use.
Carter Racing business case / Challenger incident. We often simply use the data people put in front of us to make a decision, without saying: Is this the data that we want to make the decision we want to make?"
Karl Weick's studies: we tend to stick to our tools, even if dropping
them would help us better. Dropping one's tools is a proxy for unlearning, adaptation, flexibility. But under pressure, we tend to become more rigid and regress to what we know best.Feynman: when you don't have any data, you have to use reason.
Studies of cultural congruence show that it does not have any influence on any measure of organizational success.
Von Braun, Gevedon @ NASA: balance formal process culture with a dose of informal individualism. The chain of communication has to be informal, completely different from the chain of command.
A healthy ecosystem needs biodiversity.
Innovation ecosystems need breadth and allow for inefficiency. Unfortunately, driven by efficiency and short-term, current ecosystems encourage hyperspecialization.
Oliver Smithies story of "Saturday morning experiments" that led to the discovery of gel electrophoresis that revolutionized biology and chemistry. To the end of his life, he encouraged students to think laterally, broaden their experience and forge their path in match quality. "Don't end up a clone of your thesis advisor".
To Youyou story. "Professor of 3 No's": no membership in the Chinese Academy of Sciences, no research experience outside of China, no postgraduate degree. She was interested in both modern medicine and history and was inspired by a fourth-century Chinese alchemist's recipe to discover artemisinin that helped avert 146 million cases of malaria in Africa.
Andre Geim story. Friday Night Experiments. "I don't want to carry on studying the same thing from cradle to grave. Sometimes I joke that I am not interested in doing research, only search".
Delbruck: the principle of limited sloppiness: be careful not to be too careful.
Arturo Casadevall's warning that scientific research is in crisis. Young scientists are rushed to specialize before they learn how to think. R3 Initiative (Rigor, Responsibility, Reproducibility) starts with interdisciplinary classes that include philosophy, history, logic, ethics, statistics, communication, and leadership.
"You have people walking around with all the knowledge of humanity on their phone, but they have no idea how to integrate it".
While research funding explodes, discovery slows down.
Innovation ecosystem should intentionally preserve range and inefficiency.
One sentence of advice: Don't feel behind.
Compare yourself to yourself yesterday, not to younger people who aren’t you.