“Range – How generalists triumph in a specialized world” by David Epstein – provided both great comfort and sparked inspiration on a number of people development initiatives. If you, like me, have sometimes worried that your multiple professions and experiences in numerous industries left you vulnerable to the appeal of narrowly-focused specialists then this is the book for you. Anyone with responsibility for human resources in a people-based industry that values specialisation should read the stories and scientific evidence in this book. And it’s a must read if you fear your job might be automated in the future.
Themes and key messages
An important theme is about accumulating knowledge from different domains. I often talk about how people have a particular mental map of the world – so having knowledge from a variety of areas provides you with more maps. The author says: “Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly” and “Creative achievers tend to have broad interests”. I liked Flynn’s comment about degree education: “everyone needs habits of mind that allow them to dance cross disciplines”.
Another interesting theme was the power of analogies – thinking entirely outside of the domain of study to find similar ideas. “The labs in which scientists had more diverse professional backgrounds were the ones where more and more varied analogies were offered and where breakthroughs were more reliably produced when the unexpected arose”.
Dyson uses the metaphor of focused frogs (down in the detail and the mud) and visionary birds (flying high and surveying the horizon). There’s a similar metaphor mentioned later in the book – the narrow-view hedgehogs and the integrator foxes (especially those with dragon-fly eyes which see things through multiple lenses).
The work on polymaths is explored – that’s people who have broad knowledge with at least one area of depth. Many ideas link to work on T-shaped people – those who have a deep technical knowledge in one area but also a broad appreciation of many other areas.
Sports and chess stories
The book starts with a comparison of Tiger Woods (early specialisation, “deliberate practice”,10,000 hours rule to expertise from “Bounce” by Matthew Syed) and Roger Federer (who enjoyed a wide range of sports when young). The point is well made that being a late starter (who experiences a “sampling period” before they focus) can excel.
There are numerous references to chess-players and how computers can become as expert as humans (although it mentions that human-robot combinations – centaurs – played the highest level of chess). Yet the author notes most situations do not have the narrow rules and limited potential options that occur in real life scenarios. He remarks “the more a task shifts to an open world of big-picture strategy, the more humans have to add”.
Generations are becoming smarter
There’s consideration of the “Flynn effect” – the increase in correct IQ test answers with each new generation in the 20th century. “The huge Raven’s (similarities tests) gains show that today’s children are far better at solving problems on the spot without a previously learned method for doing so”.
By looking at primitive societies (where concrete thinking dominates), it seems that exposure to modern work (where there is more abstract thinking) with self-directed problem solving and non-repetitive challenges was correlated with being “cognitively flexible”. “The ability to move freely, to shift from one category to another, is one of the chief characteristics of ‘abstract thinking’”.
There’s information from Kornell explaining the concept of “desirable difficulties” – obstacles that make learning more challenging, slower and more frustrating in the short term but better in the long term. The “generation effect” is where struggling to generate an answer on your own, even a wrong one, enhances subsequent learning.
There’s also reference to “interleaving” learning – having space between learning and practice sessions. Professors who caused short-term struggle but long-term gains were seen to be facilitating “deep learning” by making connections. And there’s a welcome retort to those people who demand training sessions in very short sessions: “Learning deeply means learning slowly”.
On problem-solving, Epstein says: “Successful problem solvers are more able to determine the deep structure of a problem before they proceed to match a strategy to it”. The concept of reframing was seen to uncover different solutions to previously intractable problems. This is an important idea for creativity and innovation.
Reflecting on the marshmallow test (how long could children resist one marshmallow when offered more if they resisted) they found that the longer a child has been able to wait, the more likely he or she was to be successful socially, academically and financially and the less likely he or she was to abuse drugs. There’s also a consideration of the context principle when looking at personality traits
Success later in life
The US Census Bureau studied new tech companies and showed that among the fastest-growing start-ups, the average age of a founder was 45 when the company was launched.
There’s the story of Vincent Van Gogh’s childhood and his various careers (art dealer, teacher, book seller, prospective pastor). He enrolled at art school at 33 and then tried piano. His works that were “dashed off” in hours as experiments over the final two years of his life became some of the most valuable objects (culturally and financially) that have ever existed.
The author also references J K Rowling who was “set free” by early failure. Michael Crichton started as a doctor before becoming a novelist. Darwin initially considered being a clergyman. Many of his breakthroughs came from tapping into his “network of enterprise” – seeking views from people from over 230 scientific pen pals.
There’s an interesting idea of “match quality” – a term economists use to describe the degree of fit between the work someone does and who they are – their abilities and proclivities. There’s interesting counterintuitive information about the importance of grit (a combination of passion and perseverance – like Finnish Sisu). Seth Godin argued that winners (individuals who reach the apex of their domain) quit fast and often when they detect that a plan is not the best fit. Although he noted that many suffer from “sunk costs fallacy” and stick with something that isn’t suitable.
There’s another review of studies of unusually winding career paths called the “Dark Horse project”. The people studied choose the best career match right now and used short term planning as a common strategy.
Psychologist Dan Gilbert noted that we recognise our desires and motivations changed a lot in the past but believe that they will not change much in the future. He called it “end of history illusion”. Another study of professionals who change careers found that we learn who we are only by living and not before. “We discover the possibilities by doing, by trying new activities, building new networks, finding new role models”.
Big changes in careers were often characterised by feeling unfulfilled in work and then a chance encounter with some world previously invisible to them. These people ask themselves “Which among my various possible selves should I start to explore now?”. Instead of working back from a goal, the suggestion is to work forward from promising situations. The “test and learn” model replaces the “plan and implement” model.
The outsider advantage
There’s an observation from molecular science where the author noticed that the most clever solution always came from a piece of knowledge that was not a part of the normal curriculum.
There’s consideration of the value of “outside-in“ thinking – finding solutions in experiences far outside of focused training for the problem itself. It was interesting to read about InnoCentive where businesses can put up tough problems to gather ideas broadly for solutions.
“Our research shows that a domain-based solution is often inferior” according to Lakhani “Big innovation most often happens when an outsider who may be far away from the surface of the problem frames the problem in a way that unlocks the solution”.
There’s a great story about lateral thinking with withered technology which gave rise to the growth of Nintendo. A PwC study is mentioned that found there was no statistically significant relationship between R&D spending and performance.
The Good Judgement Project considered forecasting accuracy. Volunteers drawn from the general public beat experienced intelligence analysts with access to classified data (about 30% more effective). Small super teams were 50% more accurate in their individual predictions. A core trait of the best forecasters was to be genuinely curious about everything (interesting that curiosity is so important – this was supported by Greg Orme in his book on The Human Edge).
Dangers of over-specialisation
The author said “Highly credentialed experts can become so narrow-minded that they actually get worse with experience, even while becoming more confident”. He reminds us of the saying “If all you have is a hammer, everything looks like a nail”.
Savants who have what appear to be limitless retrieval capacity need patterns and familiar structures for this recall performance. “AI systems are like savants – they need stable structures and narrow worlds”.
Psychologist Gary Klein is a pioneer of the “Naturalistic decision making” (NDM) model of expertise which observes expert performers in their natural course of work to learn how they make high-stakes decisions under time pressure. Experts are similar to chess masters in that they recognise familiar patterns. Klein worked with David Kahneman (of “Thinking fast and slow” fame) who doubted the link between experience and expertise back in 1955. Research showed that in real world scenarios where patterns did not clearly repeat, repetition did not cause learning.
Studying high-powered consultants from top business schools Chris Argyris of Yale School of Management noted the dangers of treating the “wicked world as if it were kind”. They did really work on business school problems that were well defined and quickly assessed but they employed what he called “single-loop” learning – the type which favours the first familiar solution that comes to mind. There is a learned inflexibility among experienced practitioners.
Oxford University business school has shown that around 90% of major infrastructure projects worldwide go over budget (by an average of 28%) in part because managers focus on the details of their project and become overly optimistic.
He mentions where experienced accountants were asked in a study to use a new tax law for deductions that replaced a previous one and they did worse than novices. Eric Dane, a university professor in organizational behaviour, calls this “cognitive entrenchment”.
Later in the book the author says that “a hallmark of interactions on the best teams is what psychological Jonathan Baron terms “active open-mindedness”.
“Skilful forecasters depart from the problem at hand to consider completely unrelated events with structural commonalities rather than relying on intuition based on personal experience or a single area of expertise”.
“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 a smash hit in the library of human knowledge”.
There are times when the detours to different fields (for example, Baroque, jazz and classical music, astronomy, diagnosing muscular dystrophy variations, motor racing) while fascinating detract from the main themes and messages. But overall the book is a thought-provoking and evidence-based challenge to the perceived supremacy of specialist thinking.
Learn more about Epstein