I went back to reading ‘Range: Why Generalists Triumph in a Specialised World’ by David Epstein last week. The author followed up his first book ‘The Sports Gene’ with this compelling book that suggests pursuing breadth over depth is not a zero-sum game, and can in fact be beneficial in developing well-roundedness. Our suitability to a pursuit and subsequent mastery ultimately comes down to the nature of learning environments – specialists benefit from ‘kind’ learning environments, and generalists thrive in ‘unkind’ learning environments.
One of the popular examples from the book is about the contrasting early years of Tiger Woods and Roger Federer. Tiger Woods’ career exemplifies starting early (at the age of 2) and a tenacious, single-minded approach to master a specialised sport. Roger Federer’s early years were spent ‘sampling’ different sports from football to skiing to basketball and badminton, alongside tennis. His junior pro tennis years started at the age of 12.
While the robustness of the example reads a little tenuous to me, the author goes on to suggest that Roger benefitted from the experience acquired in his formative years because of the ‘dynamic’ nature of the sport of tennis. Tiger’s single-minded focus helped him in the apparently ‘less-dynamic’ sport of golf. The relatively lesser dynamism in golf comes from a narrower set of patterns with instantaneous feedback that rewards repetitive practice (and therefore, a kind learning environment). This is compared to an unkind learning environment for Roger with a lot more variation and a general difficulty in discerning feedback that is attributable to individual mastery.
The book is strewn with examples of different learning environments from various fields such as classical music, sciences, and further examples from sport, and is very generous with data and evidence to support the positions taken. It is a brilliant read that efficiently communicates the central message, and takes a reassuring tone to some of the challenges that people of all ages have towards learning. (Exhibit A – “Head starts are fast. Deep learning is slow.” It is difficult not to feel good about one’s pace of learning, regardless of how slow it is. Nicely done, David.)
One of the chapters in the book stood out to me.
It begins with the example of a math teacher introducing algebra to school children. Naturally, the teacher relies on relatable, real-world examples like buying hot dogs at a baseball game. If the unit cost is $3, “how much would N hot dogs cost?” etc. A variety of answers emanate from the class. Variations of the question (different variations to construct algebraic equations) again result in an echo of multiple choices, and the teacher moves to the next variation when the right answer is bellowed by one of the students. This sounds like a familiar classroom setup.
This is a very mundane example but one that is extremely pertinent to our day to day lives. To understand the fallacy (yes, there is a fallacy here), we must first understand the students’ approach to learning. Students aren’t productively exploring answers, they are unintentionally hacking the right answer in order to meet an immediate requirement (to appease the teacher).
The students achieve mastery at understanding rules to solve problems, but do not make any headway in understanding the underlying system. In short, they get good at solving algebraic problems, and make little progress in algebra. While this bears a semblance of a kind learning environment with narrow scope and instant feedback, it is one that prioritises procedural mastery over systemic mastery.
It is not just these students, from the example, who are guilty of this. We do it with almost every problem we encounter daily. Unfortunately, we are hard-coded to optimise for a solution, with evolution being the perpetrator. (This time around as well. My other posts touch upon this)
This is not a post that is an exhaustive, long-form on approaches to learning, although I would strongly recommend reading the book to understand our own blindspots. There are a couple of things that we can do that will set us in the right direction. I don’t think simulating learning environments (kind or unkind) is achievable for every activity that we set out to do. What we can however do is to look at how we frame the problem so that we can allow our natural instinct to optimise for the best solution for an accurately framed problem.
Framing the problem in a manner that is objectively accurate means looking at the problem in its fundamental form (first principles). Otherwise, we will find ourselves chipping away at the periphery of the solution (also known as bike shed/ bicycle shed effect), which we do aplenty of. (Bonus – this is from the same man that proposed the infamous Parkinson’s Law)
It takes deliberate effort to not optimise for the wrong problem frame – to not defer to search engines or quick hacks, nature of the problem notwithstanding. We find ourselves looking for sample business plans if we want to draft one. Therein lies the problem – that we ‘find ourselves’ looking for one.
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