Range
Metadata
- Author: David J. Epstein
Highlights
In reality, the Roger path to sports stardom is far more prevalent than the Tiger path, but those athletes’ stories are much more quietly told, if they are told at all. Some of their names you know, but their backgrounds you probably don’t.
Survivorship bias and easy narrative structures.
One study showed that early career specializers jumped out to an earnings lead after college, but that later specializers made up for the head start by finding work that better fit their skills and personalities. I found a raft of studies that showed how technological inventors increased their creative impact by accumulating experience in different domains, compared to peers who drilled more deeply into one; they actually benefited by proactively sacrificing a modicum of depth for breadth as their careers progressed. There was
Having a deeper pool of experience works in the lobg run. Note that specialitation does pay off qhickly and it is agood early booster - in aviaton, one might see people who specialize become captains quickly, but those who diversify might have better QoL in he long term.
how much of this is measured in terms of money? And how much do indjstry specific compensation rates affect the results?
Their LinkedIn profiles didn’t show the linear progression toward a particular career they had been told employers wanted. They were anxious starting grad school alongside younger (sometimes much younger) students, or changing lanes later than their peers, all because they had been busy accumulating inimitable life and leadership experiences. Somehow, a unique advantage had morphed in their heads into a liability.
Just like Roger, non specialized career paths have difficult narratives, even for ourselves. How does one nswer an jnterview, which hevily reliea on coherent story telling?
But like the others in the room, he had been told, both implicitly and explicitly, that changing directions was dangerous.
I have beeb met with support, but the genral wisdom says not to.
Before each occasion, I read more studies and spoke with more researchers and found more evidence that it takes time—and often forgoing a head start—to develop personal and professional range, but it is worth it.
! takes time and one does nkt get a head start.
psychologists I spoke with led me to an enormous and too often ignored body of work demonstrating that learning itself is best done slowly to accumulate lasting knowledge, even when that means performing poorly on tests of immediate progress. That is, the most effective learning looks inefficient; it looks like falling behind.
This still requires learning and study. How does this relate tl play in learnjng?
Zuckerberg was twenty-two when he said that. It was in his interest to broadcast that message, just as it is in the interest of people who run youth sports leagues to claim that year-round devotion to one activity is necessary for success, never mind evidence to the contrary.
Again the power of efficient narrative faces off with complex experience.
Everyone is digging deeper into their own trench and rarely standing up to look in the next trench over, even though the solution to their problem happens to reside there.
One might argue that at least all sports are generally within thr same domain. Does the secondary trench meet the same requirement, that jt at least be generally related? What avout paths that are orthogonal?
The challenge we all face is how to maintain the benefits of breadth, diverse experience, interdisciplinary thinking, and delayed concentration in a world that increasingly incentivizes, even demands, hyperspecialization.
How indeed. Note that cnentration is still ultimately required.
Klein studied firefighting commanders and estimated that around 80 percent of their decisions are also made instinctively and in seconds. After years of firefighting, they recognize repeating patterns in the behavior of flames and of burning buildings on the verge of collapse. When he studied nonwartime naval commanders who were trying to avoid disasters, like mistaking a commercial flight for an enemy and shooting it down, he saw that they very quickly discerned potential threats. Ninety-five percent of the time, the commanders recognized a common pattern and chose a common course of action that was the first to come to mind.
This seems contrary to the main theses proposed jn Noise by Dan Kahneman.
The domains Klein studied, in which instinctive pattern recognition worked powerfully, are what psychologist Robin Hogarth termed “kind” learning environments. Patterns repeat over and over, and feedback is extremely accurate and usually very rapid. In golf or chess, a ball or piece is moved according to rules and within defined boundaries, a consequence is quickly apparent, and similar challenges occur repeatedly.
Much like in neural nets wth well defined functions. In other words, thisnis the difference between specific machine learning and the concept of general AI.
That is the very definition of deliberate practice, the type identified with both the ten-thousand-hours rule and the rush to early specialization in technical training. The learning environment is kind because a learner improves simply by engaging in the activity and trying to do better.
The 10,000 hour rule posits that working at a specific task for that much time leads to expertise. But just like AlphaGo or AlphaZero, we cN learn and improve at a specialized task with sufficient repetition, but this does not beatly.map to wider contexts or tasks.
In wicked domains, the rules of the game are often unclear or incomplete, there may or may not be repetitive patterns and they may not be obvious, and feedback is often delayed, inaccurate, or both.
How does one address environemtns with incompleyr rules, unestablished patterns, etc? In ML one might suggest forms of ubsupervised learning, but this assumes there are patterns that can be extracted from the data (or that a single, complicated function can address the issue - see neural nets as function approximators).
If Deep Blue’s victory over Kasparov signaled the transfer of chess power from humans to computers, the victory of centaurs over Hydra symbolized something more interesting still: humans empowered to do what they do best without the prerequisite of years of specialized pattern recognition.
The existence of computers and ML shoukd be used to offset the rrquurenent for hyperspecialized pattern matchingin humans. This will allow humans to focus on longer terms or big picture issues.
Cf. The role of automation in aviation. Systems are very capable of habdling specific tasks, but the managenent of all systems is new situations is left to humans.
“What people don’t understand is that freestyle involves an integrated set of skills that in some cases have nothing to do with playing chess.” In traditional chess, Williams was probably at the level of a decent amateur. But he was well versed in computers and adept at integrating streaming information for strategy decisions. As a teenager, he had been outstanding at the video game Command & Conquer, known as a “real time strategy” game because players move simultaneously. In freestyle chess, he had to consider advice from teammates and various chess programs and then very quickly direct the computers to examine particular possibilities in more depth.
A computer can analyse individual patterns very well, and may in fact analyse billions of them. But computation is not unlimited, and therefore a human can offset this hyoerspecializatiom by directing compute efforts to specific areas.
In the end, Kasparov did figure out a way to beat the computer: by outsourcing tactics, the part of human expertise that is most easily replaced, the part that he and the Polgar prodigies spent years honing.
See previous notes. Pattern recognition vs big picture strategy .
A grandmaster repeatedly re-created the entire board after seeing it for only three seconds. A master-level player managed that half as often as the grandmaster. A lesser, city champion player and an average club player were never able to re-create the board accurately. Just like Susan Polgar, grandmasters seemed to have photographic memories. After Susan succeeded in her first test, National Geographic TV turned the truck around to show the other side, which had a diagram with pieces placed at random. When Susan saw that side, even though there were fewer pieces, she could barely re-create anything at all.
Human brains are als very good at pattern matchibg. Thus a hint here and a suggetion therr can be used to generate acomplete model, given familiarity with the subject.
however, this requires the tasks to follow known patterns. In other words, we think in patterns and not in specifics - consider reading with letters reorgabized except for start and end, or finding meaning of new word from.context.
Through repetitive study of game patterns, they had learned to do what Chase and Simon called “chunking.” Rather than struggling to remember the location of every individual pawn, bishop, and rook, the brains of elite players grouped pieces into a smaller number of meaningful chunks based on familiar patterns. Those patterns allow expert players to immediately assess the situation based on experience, which is why Garry Kasparov told me that grandmasters usually know their move within seconds. For Susan Polgar, when the van drove by the first time, the diagram was not twenty-eight items, but five different meaningful chunks
This ia the resukt of the 10,000 hour rule. Expertise generated by the ability tk see recurring subpatterns in a specfic domain.
Those are the same twenty pieces of information, but over the course of your life, you’ve learned patterns of words that allow you to instantly make sense of the second arrangement, and to remember it much more easily. Your restaurant server doesn’t just happen to have a miraculous memory; like musicians and quarterbacks, they’ve learned to group recurring information into chunks.
Is this what we cal “having good memory”? Thse with alleged good memory miht just be recognizing patterns. Aternatively, it is poasible that those with good memory see subjective patterns - consider quacks and conspiracy nuts.
He studied high-powered consultants from top business schools for fifteen years, and saw that they did really well on business school problems that were well defined and quickly assessed. But they employed what Argyris called single-loop learning, the kind that favors the first familiar solution that comes to mind. Whenever those solutions went wrong, the consultant usually got defensive.
not unlik the issue of controlled pracice in chool settings. Whuke there is a llace for t, we mjst emphasize wicked problem solving broadly as part lf a stabdard education. In CS, this js the role of projects; how else might we expand this tk less deterministic systems?
Psychologist Barry Schwartz demonstrated a similar, learned inflexibility among experienced practitioners when he gave college students a logic puzzle that involved hitting switches to turn light bulbs on and off in sequence, and that they could play over and over. It could be solved in seventy different ways, with a tiny money reward for each success. The students were not given any rules, and so had to proceed by trial and error.* If a student found a solution, they repeated it over and over to get more money, even if they had no idea why it worked. Later on, new students were added, and all were now asked to discover the general rule of all solutions. Incredibly, every student who was brand-new to the puzzle discovered the rule for all seventy solutions, while only one of the students who had been getting rewarded for a single solution did. The subtitle of Schwartz’s paper: “How Not to Teach People to Discover Rules”—that is, by providing rewards for repetitive short-term success with a narrow range of solutions.
In teaching, focusing on specific ptterns with clesr rewards early on might be detrimental. Does this cintradict previous note?
Related to.primacy: if a specific initial pattern is to be tught, theb the mkst geberal initial pattern shuld be taught.
out what happens to a patient after their encounter. They have to find ways to learn beyond practice, and to assimilate lessons that might even contradict their direct experience.
Learning beyind practice is the idea that learnung happens outside of active oractice; it is the process of assimilatiom and synthesis.
Connolly’s primary finding was that early in their careers, those who later made successful transitions had broader training and kept multiple “career streams” open even as they pursued a primary specialty. They “traveled on an eight-lane highway,” he wrote, rather than down a single-lane one-way street. They had range. The successful adapters were excellent at taking knowledge from one pursuit and applying it creatively to another, and at avoiding cognitive entrenchment. They employed what Hogarth called a “circuit breaker.” They drew on outside experiences and analogies to interrupt their inclination toward a previous solution that may no longer work. Their skill was in avoiding the same old patterns. In the wicked world, with ill-defined challenges and few rigid rules, range can be a life hack.
While folcusing on estabkished processes is lerhaps a sure way to coexist with the status quo, the very state of existence is ever chabging. Recognizing applications frlm one domain in another kne is therefore essentjal.
performance on more abstract tasks that are never formally taught, like the Raven’s matrices, or “similarities” tests, which require a description of how two things are alike, skyrocketed. A
Something abkut how the world has chabges allos to develop more bstract thinking. Conaider kids whok today surpise us by bejng highky abke to complete abstrct tasks. Is this the result ofnhaving mlre variety in theirnducation and in theirbaccess tk abstract tech?
The farmers and students who had begun to join the modern world were able to practice a kind of thinking called “eduction,” to work out guiding principles when given facts or materials, even in the absence of instructions, and even when they had never seen the material before.
The ability to discern abstrct patterns is what makes us modern . Is it also why we feel unhappy? Cf. Ankther Now: if we could nkt see the hihher patterns, if we saw only that whichbis infront kf us and our ubderstantjve was mkre narrative than abstract, how would our relationship with existebce chabge?
The more they had moved toward modernity, the more powerful their abstract thinking, and the less they had to rely on their concrete experience of the world as a reference point.
Can a “modern” world exist withiut abstractjons? What wuld happen id we never learned to think like tbhis?
We have grown up in a world of classification schemes totally foreign to the remote villagers; we classify some animals as mammals, and inside of that class make more detailed connections based on the similarity of their physiology and DNA.
As the prevjious nkte: what happens if we live in a world bot obsessed with classification? And is this classification obsession a product of European colonialism?
“In the Far North, where there is snow, all bears are white. Novaya Zemlya is in the Far North and there is always snow there. What colors are the bears there?” That time, no amount of pushing could get the remote villagers to answer. They would respond only with principles. “Your words can be answered only by someone who was there,” one man said, even though he had never been to England but had just answered the cotton question.
Reminds men of things people in Mexico have said, individuals who are genjine but sont seem tk abstrsct a lot. This seems like the thingb we often find distasteful in unedcated people.
dles this effect go beyond jist general mldernity? How dles it affect diferet econlmkic classes? Are there any kther factors or kanki just educarion?
a city dweller traveling through the desert will be completely dependent on a nomad to keep him alive. So long as they remain in the desert, the nomad is a genius.
Indeed. A poor, uneducated person is seen as needy or rewuires government assistance. But they are loke a cty dweller kn the desert, outside their element.