Fragments of the book “Range. Why Generalists Triumph in a Specialized World”, by David Epstein.
Learning Slowly
An inventor who stuck to a self-made antispecialization philosophy and turned a small company founded in the nineteenth century into one of the most widely resonant names in the world today.
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.
Overspecialization can lead to collective tragedy even when every individual separately takes the most reasonable course of action.
The learning environment is kind because a learner improves simply by engaging in the activity and trying to do better. Kahneman was focused on the flip side of kind learning environments; Hogarth called them “wicked.” 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. In the most devilishly wicked learning environments, experience will reinforce the exact wrong lessons.
The bigger the picture, the more unique the potential human contribution. Our greatest strength is the exact opposite of narrow specialization. It is the ability to integrate broadly.
AI systems are like savants.” They need stable structures and narrow worlds.
To him who observes them from afar,” said Spanish Nobel laureate Santiago Ramón y Cajal, the father of modern neuroscience, “it appears as though they are scattering and dissipating their energies, while in reality they are channeling and strengthening them
They were perfectly capable of learning from experience, but failed at learning without experience. And that is what a rapidly changing, wicked world demands—conceptual reasoning skills that can connect new ideas and work across contexts.
Generation Effect
One of those desirable difficulties is known as the “generation effect.” Struggling to generate an answer on your own, even a wrong one, enhances subsequent learning. It requires the learner to intentionally sacrifice current performance for future benefit.
The more confident a learner is of their wrong answer, the better the information sticks when they subsequently learn the right answer. Tolerating big mistakes can create the best learning opportunities
Types of Problems
In a study using college math problems, students who learned in blocks all examples of a particular type of problem at once—performed a lot worse come test time than students who studied the exact same problems but all mixed up. The blocked-practice students learned procedures for each type of problem through repetition. The mixed-practice students learned how to differentiate types of problems.
Learning deeply means learning slowly.
Instead of working back from a goal, work forward from promising situations. This is what most successful people actually do anyway. “Test-and-learn,” Ibarra told me, “not plan-and-implement.”
Outsiders
Our intuition might be that only hyperspecialized experts can drive modern innovation, but increasing specialization actually creates new opportunities for outsiders. Big innovation most often happens when an outsider who may be far away from the surface of the problem reframes the problem in a way that unlocks the solution.”
The more information specialists create, the more opportunity exists for curious dilettantes to contribute by merging strands of widely available but disparate information—undiscovered public knowledge, as Don Swanson called it. The larger and more easily accessible the library of human knowledge, the more chances for inquisitive patrons to make connections at the cutting edge.
It isn’t just the increase in new knowledge that generates opportunities for nonspecialists, though. In a race to the forefront, a lot of useful knowledge is simply left behind to molder. That presents another kind of opportunity for those who want to create and invent but who cannot or simply do not want to work at the cutting edge. They can push forward by looking back; they can excavate old knowledge but wield it in a new way
As ambiguity and uncertainty increases, which is the norm with systems problems, breadth becomes increasingly important.
“When seeking innovation in knowledge-based industries,” they wrote, “it is best to find one ‘super’ individual. If no individual with the necessary combination of diverse knowledge is available, one should form a ‘fantastic’ team.” Diverse experience was impactful when created by platoon in teams, and even more impactful when contained within an individual.
Serial Innovators
Their findings aout who these people are should sound familiar by now: “high tolerance for ambiguity”; “systems thinkers”; “additional technical knowledge from peripheral domains”; “repurposing what is already available”; “adept at using analogous domains for finding inputs to the invention process”; “ability to connect disparate pieces of information in new ways”; “synthesizing information from many different sources”; “they appear to flit among ideas”; “broad range of interests”; “they read more (and more broadly) than other technologists and have a wider range of outside interests”; “need to learn significantly across multiple domains”; “Serial innovators also need to communicate with various individuals with technical expertise outside of their own domain.”
Effective problem-solving culture was one that balanced standard practice—whatever it happened to be—with forces that pushed in the opposite direction