Book Review-The Grieving Brain: The Surprising Science of How We Learn from Love and Loss
Book Review-Range: Why Generalists Triumph in a Specialized World
I don’t know how to stay in my lane. That’s the way I often explain to clients how and why I reach into related areas of the organization to try to support them as well. We may be focused on one technology project, but that doesn’t stop me from supporting human resources, communications, marketing, and other departments by sharing whatever I know about what does – and doesn’t – work in their world. That’s why I was intrigued by Range: Why Generalists Triumph in a Specialized World. I wanted to find out how others like me, who weren’t easily constrained, managed to continue to grow and increase their impact.
Deep and Wide
The more time someone spends in a discipline, the more they tend to view the world from the lens of that discipline. They don’t see options. They fall into the Einstellung effect. That is, they continue trying to solve new problems with the same old approaches, even if better approaches are available. The more we know, the less willing we are to look at things differently and find other, potentially, better approaches.
Often, this is often boiled down to the fox or the hedgehog problem. (See my detailed post Should You Be the Fox or the Hedgehog? for more.) The short version is a hedgehog knows one thing well and a fox knows many things less deeply. The fundamental premise of the book – and the general answer I came to in my post – is that the best answer is to be a “foxy hedgehog.” That is, have an area or areas where you’re very deep, but also have a general awareness of other areas, so you can bring solutions from other industries to bear on the problems your industry faces.
The Medici family brought together very strong artists and thinkers in several different genres in Florence. (See The Medici Effect for more.) They created a safe environment where the artists were allowed and even encouraged to learn about the kinds of art, science, and thinking that was related to – but outside of – their area of expertise. The resulting cross-pollination of experts kicked off the Renaissance period.
What the Medici family managed was to bring together different disciplines in different people and, ultimately, through conversation and dialogue, bring together different disciplines within individuals. (See Dialogue for more on the power of dialogue.) By creating a safe place for diversity of thought and background, they accelerated individuals internalizing several different disciplines. (See The Difference for more on diversity.)
By creating common space – in effect, porous boundaries – where experts can talk and help one another, the traditional silos that drive business today are knocked down. There is no wall to smack into when one artist wants to work with another to learn their craft.
When you know one skill very well, you’re a hedgehog. The metaphor breaks down when you know more than one subject very well. When you are a true master in multiple domains, the metaphor can’t handle you any longer. One of the all-star polymaths was Michelangelo. He was also someone who tested and learned. Three-fifths of his sculptures were never finished. He simply became bored before he ever finished them. Despite the quote about David, that he just removed anything that didn’t seem to be David, he often seemed to change his mind repeatedly about what a sculpture should look like.
There are some people who make up their minds about what they want in life very early. They decide to be a doctor, a vet, a policeperson, a fireperson, or something else and they stick to it. You might call it the “tiger path” after Tiger Woods and his lifelong love affair with golf. In his case, all worked well, and he became quite good at golf. However, sometimes, things don’t work so well out of the gate.
Vincent van Gogh is hailed both as a successful painter whose genius wasn’t well-known in his time and as a sad, mentally-ill artist who took his own life out of despair. However, what is often overlooked is that van Gogh was several things – quite unsuccessfully – before becoming a painter. From art dealer to minister, Vincent tried his hand at many things. He would pour himself into each thing before discovering that no amount of his work or drive would bring him success. Somewhere, deep in the heart of the Protestant work ethic, is the idea that if you work hard, you’ll be successful. (For more on why this doesn’t work, see The Black Swan.) He happened into painting and found his calling, but not before he did a great deal of accidental sampling as he struggled to find his place, passion, and ability to sustain a life.
It turns out that this sampling period is important. Whether it’s trying to figure out what sport to play or what career to go into, the ability to determine what we want allows us more capacity to be successful in the long term.
Changes in Attitude
One of the problems of making choices about our future life too early is making the choice before the person we’ll become has even arrived. We tend to believe that we don’t change much over time, but our cars, haircuts, and way of life betray us. We continue to change throughout our lives, particularly before we’re in our mid-twenties.
When we go to college and select a major, we’re quite literally selecting it for someone who has not yet arrived. The person we’re going to become hasn’t come into existence yet. David Bohm would say that the person we’re going to become hasn’t emerged yet. While we’re the aperture that our future self will enter the world through, we are not that future self at the present. (See On Dialogue for more.)
Going Slow at First
One of the challenges with sampling and trying to find your way is that the appearance is – at least at first – that you’re going slower. After all, those who are blazing their trails are making more money and reaching a career position quicker. However, the challenge is that these early bloomers often find they want to change their careers later in life – which comes with a great cost.
In learning, there’s an idea of “desirable difficulty.” That is, we need a level of difficulty in learning or the learning won’t stick. (See How We Learn for more.) On the surface, training that’s easy looks better. There is a quicker time to completion and sometimes even better scores on assessment exams. However, without a level of difficulty, the information soon becomes inaccessible to our memories and the information is lost for good or must be relearned. So, learners that struggle a bit more may initially score lower, but, over time, their real results will be better.
Those who don’t settle into one career to start often learn more about a wide range of things and ultimately end up doing better because of their breadth of knowledge – and the reality that they don’t need to change careers late in life, because they picked appropriately after sampling many options.
Artificially Intelligent Savants
It’s fashionable today to speak about artificial intelligence and the wonderful things it can do. Artificial intelligence is another way of saying machine learning, which is another way of saying applied statistics. Artificial intelligence can solve some problems that are particularly vexing to our human way of thinking, however, much like savants they have a limited range of usefulness.
If you don’t provide a machine-learning algorithm the right input data or fail to train it at all, the results are not stellar. The reality is that the single-focused, task-specific knowledge is the kind of thing an artificial intelligence solution is very good at. However, artificial intelligence solutions don’t do well outside their range or with problems that aren’t well defined.
I was first exposed to Fermi Estimates in How to Measure Anything. It’s interesting that being given a little bit of knowledge about a set of things can lead to insights that are roughly right. The classic example is finding the number of piano tuners in Chicago based on numbers like population of Chicago, the probability they have a piano, the number of pianos a tuner can do in a year, the frequency of tuning, etc. The answer was strikingly close to the real answer when Fermi asked his students to put together an estimate.
However, Fermi estimates are great for fact-checking, too. If you feed things you know into a rough structure, you can identify when someone is talking bullshit to you. It becomes obvious the numbers can’t be right, because they simply don’t add-up. The ability to make quick, order-of-magnitude-type guesses and know whether ideas are viable or not is a great way to verify your work. The folks who can do this tend to be those with the broadest experience. Being too close and knowing too much causes your estimates to be less right.
Knowing Too Much
For the most part, people believe you can never know too much. While that’s true at some level, the other truth is that the more you think you know, the less inclined you are to listen. The rules don’t apply to you, and that’s the point at which you begin to lose touch with the rest of the world.
When you lose touch with the broader world, your estimates become less accurate. The more you focus in on an area and become an expert, the less likely the prediction you make about the topic will be valid. The myopic vision about an industry, role, or process tends to separate you from considering multiple, alternate possibilities that makes estimates more accurate.
Jazzing it Up
Two jazz musicians are holding a conversation, when the first asks the second, “Can you read music?” Rather than an incredulous response, the second says slyly, “Not enough to hurt my playing.” Jazz is best known for the ability to improvise. Ensembles play together and build off one another. They develop a feel for how to co-create something. Somehow, the knowledge of how music is supposed to be – with its rules and its structure – impairs this. It gets to the point where knowing too much about how music is supposed to be limits your ability to make music that’s memorable.
One of the greatest jazz musicians of all time, Django Reinhardt, couldn’t read at all – either words or music. His genius was in learning how to play music intuitively. In some ways he proved, indirectly, that you didn’t need to know how to read music to be good at it.
A key problem-solving skill is matching the solution to the problem. Students of physics and chemistry are presented with dozens (or hundreds) of equations to solve specific problems. The application of the equation is easy, but figuring out which equation to use can be much more problematic. It turns out that one of the most powerful things about problem solving is the identification of the solution to apply to the problem – second only to being able to define the problem itself.
A key benefit of a broad range of experiences is that the library of known solutions can be quite large – particularly if one doesn’t care which discipline they’re pulling from. A challenge created by specialization is that our ability to select solutions relies upon us learning the various potential solutions in an interleaved form rather than a blocked form. The more we learn solutions sequentially and separately, the less likely we are going to be able to pick out the solution that best fits in our time of need.
Kepler Was Far Out
Long before we understood much about how the planets moved, many people accepted the Copernican heliocentric model, though not all. However, the Copernican model didn’t answer every question. Questions like why the planets further out moved slower than the planets closer to the Sun weren’t answered. Instead of invisible forces, Kepler pondered what might be moving the planets forward. In his wanderings, he tried out many ideas, including that the motion of the planets was powered by light.
One of the key things about Kepler was that he was willing to document his wanderings in his notes, and those notes were preserved. One could say that Kepler was one of the first leaders in John Stepper’s Working Out Loud movement. The fact that this documentation exists shows the kind of diversity of thought that Kepler was willing to entertain and harness to find solutions to understanding our universe.
On the surface, nothing looks like anything else. A mirror reflects your image, and a piece of glass shows you what’s on the other side. However, looking deeper into the structure, you realize that both a mirror and glass are the same. The mirror has simply added a bit of reflective silver on the back side of the glass. At the level of functionality or aesthetics, there is little similarity (they’re both flat), but the more deeply you look, the more similarities there are to be found.
Though they’re constructed almost completely the same, their appearance is quite different. What’s powerful about folks with a wide range of experiences is their ability to identify the deep structure of the world and create solutions based on that deep structure. Successful problem solvers look beyond the shimmering appearance to locate what lies beneath.
In our moisture-indicating IV dressing, we realized the key problem was that nurses couldn’t easily see a dressing needed changed. The small barrier of moisture being difficult to assess meant that many patients weren’t getting dressings changed enough, and they were getting sick. (See Demand for more on small barriers.) Where other manufacturers were trying to find another antimicrobial that the microbes would eventually get around, we focused on changing the behavior to prevent the infections – something that the microbes are powerless to work around.
For all the concern about where the world is headed, when it comes to the kinds of things measured by an intelligence test, we’re moving up. The Flynn effect refers to the progressive increase in our responses to intelligence tests. It seems like we’re getting smarter. Not across eons, in a single generation. Our children, or at the very least our grandchildren, are quite likely going to be more intelligent than us. However, the effect isn’t linearly distributed.
The more abstract the world a person lived in is, their higher their score. The closer they were to agrarian, less-industrialized societies, the lower the effect. We’re becoming better at working in the abstract and forgoing the need to have absolute concrete examples, and our thinking is getting better. While some of this can be attributed to our greater access to better instruction thanks to the work done to improve the efficacy of instruction, some of it is just our ability to think more abstractly. (See Efficiency in Learning for more on improving instruction.)
If you want to be successful, you’ve got to go on a fox hunt. Or rather, you must hunt like a fox. Foxes roam freely – there are no boundaries they adhere to. Foxes listen carefully to their environment and seek to understand the messages it sends. Foxes also consume omnivorously – they’ll consume anything that seems interesting to them. These sorts of characteristics in people make for some of the best problem solvers. They can see the world with a thousand different perspectives and find the best way to view the situation. Sometimes, this is expressed as foxes with dragonfly eyes.
I don’t know where you are on your journey. You may have dedicated your life to be a hedgehog and are finding that you need to be a bit more foxy. You may be a fox who’s looking for areas to become specialized in. Either way, I think you’ll find that you’ll get more out of life with a little Range.