Biased Review 01 — “Range”
Book Review of “Range: Why Generalists Triumph in a Specialized World”, by David Epstein
For 2021 I set to myself the goal of reading 60 books. To help me keep myself accountable and retain more of the information, I thought it would be interesting to write “Biased Reviews” of the books I read. The reason why I like the idea of including “Biased” in the title is that I would otherwise feel compelled to do a more detailed summary and approach every single topic the book covers, which would make the experience more burdensome to me. By setting the expectation that these are explicitly biased I feel better about covering the topics that interest me most. Besides that, I believe that everyone is biased even if people like to think they are not, so the title is also me having fun with this.
Without further ado…
Range’s main point is that specialization works well in limited scenarios, which it calls “kind” learning environments, whereas generalization (or “range” if we want to use the book’s title) works better in “wicked” environments and can give you a leg up over specialists. The book is not against specialization per sé, but it advocates towards a late-specialization that favors range to be built instead of specializing too early. According to the author, we live in an era of “hyperspecialization” which makes people want to specialize as soon as possible since they would do it anyways. This would work great if life in general was a kind learning environment: think predictable, repeatable environments — tasks we could optimize a machine to perfect with today’s technology. In one of the earlier chapters we become acquainted with the story of Laszlo Polgar, who raised 3 daughters to become great chess players from a very early age. Chess is a perfect example because it has a defined set of rules and AI’s have been able to beat the best of humans for a few decades now. Giving his daughters a “head start” proved beneficial for the realm of chess due to its “kind” characteristics — studying chess movements and tactics repeatedly and narrowly can bring you to the top. His approach to make his daughters so successful has been tempting for parents aiming to the same with their own children.
A notable case of early specialization is Tiger Woods, who “took an interest in golf at age 6 months” and at age 2 “appeared on The Mike Douglas Show at age 2 putting with Bob Hope”. The book’s introduction draws a comparison between him and another great in the world of sports, Roger Federer. Contrary to Tiger Woods, Federer took much longer to specialize and ventured through several different sports before becoming the worldwide success that he is. Not only specializing later did not impact negatively his career, there is evidence that doing so may be beneficial for a career in sports. A study published by German scientists indicate that the team who won the 2014 World Cup was made of late specializers who did not play more “organized soccer” teams rather than amateur league ones until an average age of 22. Russell Wilson, Patrick Mahomes, and Tom Brady are all Super Bowl winning NFL Quarterbacks who have been drafted to play Baseball before starting their professional careers in Football. And the list goes on, within and without the world of sports. This is because late specialization allows to maximize their “match fit” with their chosen domains, as well as to borrow skills from one field to the other to create new opportunities. Therefore, advice such as “winners never quit” or sticking at a career you do not enjoy for too long is detrimental. The idea of “Grit” can be compelling, but there is such a thing as too much of it. Due to it and the “sunk cost fallacy”, people stick on their paths no matter what and, as a result for example, a survey with two hundred thousand workers in 150 countries reported that 85% were either “not engaged” with their current job or “actively disengaged”.
“The Grit Scale Statement “I have been obsessed with a certain idea or project for a short time but later lost interest” is Van Gogh in a nutshell. (…) He tested options with maniacal intensity and got the maximum information signal about his fit as quickly as possible (…) until he had zizagged his way to a place no one else had ever been, and where he alone excelled.
On the other hand, overspecialization can have negative impacts. First, each specialized person/group sees a smaller picture of a bigger puzzle. Specialization helps build “Expert Intuition”, which according to psychologist Daniel Kahneman’s experiments is often times wrong. Interventional cardiologists, for example, specialize in treating chest pain by placing stents (metal tubes that pry open blood vessels). However, randomized clinical trials show that for patients with stable chest pain, this intervention prevents zero heart attacks, do not prolong life expectancy, and one in fifty patients who do get a stent will suffer serious complication of the implantation procedure. A 2015 study found out that patients with heart failure or cardiac arrest were less likely to die if they were admitted during a national cardiology conference. The level of hyperspecialization nowadays can cause on myopia in which specialists are too close to their domain to see the big picture. The cardiovascular system, businesses, relationships, and countless more aspects of life operate on the “wicked learning environment” — they are uncertain and do not have pre established rules, so one is better off by seeing the big picture and connect the dots from several domains.
“Einstellung effect, a psychology term for the tendency of problem solvers to employ only familiar methods even if better ones are available”
As other books such as Good to Great, Range draws the comparison between Hedgehogs (who “know one big thing”) and Foxes (who “know many little things”). In Good to Great, Jim Collins defines the “Hedgehog Concept”, which essentially advocates for specialization in companies. Great organizations should focus their attention on the intersection of three circles: the intersection of three circles: 1) what you are deeply passionate about, 2) what you can be the best in the world at, and 3) what best drives your economic or resource engine. On the other hand, Range seems to side with the foxes. With some time, I realized that these two views are not mutually exclusive at all. As Jim Collins puts it, the Hedgehog Concept is “an understanding of what you can be the best at”. This understanding can be considered what Range in turn calls “match fit”, which is maximized by late-specialization and experimentation in different fields. Besides, the Good To Great companies usually start with a broad portfolio of products to then choose one or two that they can be the best at.
In a study about the accuracy of forecasts, Phillip Tetlok also got to this foxes/hedgehogs dichotomy. The study consisted in having 284 highly educated experts submit short and long term predictions over 20 years. Within these experts, there was a subgroup of “integrators”, who were able to connect and borrow knowledge from other domains to make their predictions — these were the foxes, whereas the remaining were the hedgehogs. Hedgehogs were deep but narrow, spending most of their careers in one specific field. According to him, they “toil devotedly within one tradition of their specialty and reach for formulaic solutions to ill-defined problems” — similar to the cardiologists discussed before. On the other hand, foxes “draw from an eclectic array of traditions and accept ambiguity and contradiction”. Foxes outperformed hedgehogs in all forecasts, but especially so in the long-term ones and even in the hedgehogs’ field of expertise. In fact, the more credentials and experience they accumulated in their field, the worst they performed as “the more information they had to work with, the more they could fit any story to their worldview”. There was also an interesting inverse-relationship in that the more an expert had their opinions broadcasted in op-eds, the more likely they were to be wrong. Going back to what I said in the beginning of this now surprisingly long article, everyone is biased one way or the other — the difference is that hedgehogs have more ammunition to support these biases. This is not too much different than the sort of political polarization we see nowadays. In essence, with information being so broadly available, we tend to become hedgehogs of our own political views. We are all just one Google search away from having the perfect argument to why we are right. Concomitantly there is not too much incentive to expand our views range. We might be in one specific group that thinks like us and the more we double-down on our views, the more we will feel we belong. We also may become early-specializers and have a head start on whatever worldview we have due to our family’s influence. The problem is not the specialization or the worldview per sé, but the lack of range to empathize and understand other views, and even the ability to combine views and synthesize something different.
“Viewing every world event through their preferred keyhole made it easy to fashing compelling stories about anything that occurred, and to tell the stories with adamant authority. In other words, they make great TV”
I’ll end this longer-than-I-expected review reflecting on education. At some point in the book, Range outlines the benefits of “making connections” education and practice questions over procedural/algorithm ones. Both are important and fill their own roles, but in a study, well-meaning teachers and parents were giving procedural hints to students while they were working on making connection questions — therefore transforming the questions to procedural ones. This is very symptomatic of education nowadays, which emphasizes these algorithms and test mechanics over longer, slower, and more effective learning. In school I thought I did better in exercises, specially in quantitative fields, when I found a creative way of doing them instead of fixing myself to “the right way” of solving them. Being honest I felt like my math skills were diminishing as the years went on because my solution portfolio had become more rigid and algorithmic. In Brazil, the only way of getting into universities is through admission exams — imagine if the only thing between you and college was the SAT, but denser and longer. In a world driven by incentives, it makes sense that education is so focused on test-mechanics/algorithms. That’s how students and schools measure their success, and getting students the best universities end up being a marketing mechanism for schools. Studies show that the only thing standardized test reliably predict is family income. Not only it is an ineffective admissions tool, it drives inequality. A better solution for Brazil in my opinion is to adopt a more holistic approach as it is done in the US, and for US I am liking to see the trend of more universities not requiring standardized tests and driving affirmative action. None of these will be a panacea for education and much more needs to be done, but hopefully it will help align incentives in such a way that schools will become a more systematic way for people to expand their range.
