I got over the “I’m not directly using my degree” panic many years ago, but this book helped to put additional stories and studies around why coming to your current work with a different background can be beneficial in many ways. I think this book will be particularly useful for people who try to excel at multiple disciplines, plan on moving to a new discipline, or if you are someone who hires and manages people. On top of the key concepts I’ll be reviewing below, this is a very readable book. David Epstein does a wonderful job combining studies with compelling stories from a wide array of characters spanning the start-up of Nintendo, the come-up story of Fances Hesselbein (former CEO of Girl scouts of America), and Vincent van Gogh to name a few.
Here are some of the big ideas I am taking away from Range:
- Many of the best innovations are found at the adjacent-possible. Put another way, at the cutting edge – combined with something lateral thinking.
- Nintendo has never been known for having the most advances technologies in their systems, however in their early days they often combined technologies that have existed for a long time with others in a unique way. The “game boy” for example was hardly the most advance piece of technology, but with the older technologies they combined it was at a price point that allowed it to spread.
- If the only tool you know how to use is a hammer, everything looks like a nail. Know when to put down your familiar tools.
- David tells quite a few stories that solidify this point. One is of prediction groups that the US government has studies. These groups are given proposed world problems and asked to predict outcomes and solutions. It turns out it is not the group of specialists that perform best, rather the group of generalists that have a broad range of knowledge across multiple disciplines. Another example is how a chemist who dabbled in construction work was able to use a construction tool to figure out how to clean up and Alaskan oil-spill that was stumping the industry.
- The generalist approach is not as immediately successful in the short-run but can be in the long-run.
- Two of the most prominent examples of this from the book come from both medicine and athletics. In both cases the highly-specialized approach seems like the only way to make it far, but David draws examples from medicines greatest innovations to showcase how important it is to have doctors with Range.
All the above notes were taken from memory to solidify the concepts and retain the information. Below are general notes taken directly from the book.
- In Totality, the picture is in line with a classic research finding that is not specific to music: breadth of training predicts breadth of transfer. That is, the more contexts in which something is learned, the more the learner creates abstract models, and the less they rely on any particular example. Learners become better at applying their knowledge to a situation they have never seen before, which is the essence of creativity.
- Some people argue that part of the reason U.S. students don’t do as well on international measures of high school knowledge is that they are doing too well in class. We need to make it easy to make it hard. Desirable difficulties make learning more challenging, slower and more frustrating in the short term, but better in the long term.
- Desirable difficulties like testing and spacing make knowledge stick. It becomes durable. Desirable difficulties like making connections and interleaving make knowledge flexible, useful for problems that never appeared in training. All slow down learning and make performance suffer in the short term. That can be a problem, because we all assess our progress by how we are doing right now.
- The research team recommended that if programs want to impart lasting academic benefits they should focus instead on “open” skills that scaffold later knowledge. Teaching kids to read a little early is not a lasting advantage. Teaching them how to hunt for and connect contextual clues to understand what they can be. As with all desirable difficulties, the trouble is that a head start comes fast, but deep learning is slow. The slowest growth is for the most complex skills.
- Kepler’s short Mars assignment, which he thought would take 8 days, turned into 5 years of research. His analogous thinking brought on from other disciplines finally helped him to solve the problem.
- College graduates in England and Wales were consistently more likely to lead out of their career fields than their later specializing Scottish peers. The Scotts didn’t have to specialize until later which gave them greater sampling opportunities.
- We discover the possibilities by doing, by trying new activities, building new networks, and finding new models. We learn who we are in practice, not in theory.
- We need people who go deep and those who can see far (Think Woz & Jobs).
- If you’re working on well-defined problems specialists work very well, as ambiguity increases breadth becomes more important.