We often hear of the Polgar sisters and Tiger Woods and their extraordinary success, which is why many of us believe in early deliberate practice
However, we must also realize that the majority of things that humans want to do are not like chess or tennis; hard to measure and not objective
’Kind’ learning environment where the same patterns happen over and over again and feedback is rapid. This is where narrow domain specialization dominates
Chunking is important and requires people to start practicing early and excessively, but it is based on past patterns
This is exactly where AI rules supreme
’Wicked’ learning environment: rules are unclear, feedback and patterns might not exist or are incomplete. This is majority of tasks
Experience may reinforce the exact wrong lessons
People that narrow in have traded mastery for flexibility, creating cognitive entrenchment
Successful adapters always had multiple streams open to specialize
Many masters in their field have a ‘circuit breaker’ or an escape activity to help them not get entrenched into one style of thinking
How the Wicked World Was Made
Experiments found where modernity actually increased IQ because education gives us general first principles that can apply in several different circumstances
Exposure to modern work and non-repetitive work has made us smarter than before
Abstract work is based on the concept of range.
Specialization in universities has left us out of broad transferable skills that would be useful in life and in other domains
Example of this broad skill: Fermi problem solving
Example of villagers in Uzbekistan who could barely comprehend logic questions because it was from a completely different world. Similarly, many of us flounder when presented with a problem from a different domain
When Less of the Same is More
Ex: Figlie in Venice were able to learn multiple instruments, coming from an orphaned background, raised in ospedali (orphanages) where they had a lot of other work to do
Right now, many view music in the lens of focus narrowly and do well. However, the sampling period of trying different instruments is integral and allows the best musicians to choose an instrument that they like (eg. Yo-Yo Ma played piano violin before cello)
Studies have shown that some of the most musically talented youth have had much less practice and focus on a particular instrument than their peers; in fact, the studies showed that too many lessons and structured activities had a negative impact
Studies also showed that a sampling period was extremely important for prodigies
Many improv masters in music experimented a lot and never got formal education on how to read notes or sheet music; they imitated their way to the top
By switching between instruments, musicians are able to develop abstract models that helps them in mastering any instrument/improvising if they so choose
Learning, Fast and Slow
In math classes, there are two types of questions: making connections and making procedures
Unfortunately, many Western nations start off with both questions when learning their concepts but only use procedural problems for evaluation
For learning that is deep, durable and flexible, fast and easy procedures is not the way
’Desirable difficulties’: obstacles in learning that make it harder to understand but makes learning much better in the long term
Supported by a lot of education research
Example is generation, where you are forced to generate an answer even if you have no idea what the answer is
Tolerating big mistakes creates a great learning environment
Training with hints did not produce any lasting learning
Testing is another desirable difficulty, but giving hints or answers does not make it effective
Spacing is another important pedagogical practice: space out learning and testing to encourage actual learning
Repetition is less important than struggle
Studies have also found that teachers who made students struggle more and gave them lower scores were the ones with the most successful students in the long run
Students tend to evaluate professor based on current performance, but learning is a much more long-term process. Good performance is actually just indexing fast and fleeting progress
Interleaving: procedural practice under varied and random conditions
Allows students to differentiate different types of problems, also known as mixed practice
Due to short-term pain and slow gains in performance, it tricks students into believing that they aren’t learning much
Early learning programs also fall into this same pool: the skill that they learn are repetitive and can be easily replicated. The head start vanishes. Skills should be more open-ended
Far transfer: knowledge structure flexible enough to be applied in broad, novel domains
We should be aiming for this
Thinking Outside Experience
Analogical thinking: use conceptual similarities across multiple domains
Ex: Kepler used analogical thinking to the extreme to understand what caused the orbits of planets
Analogies allow us to think through novel problems
If problems are never seen before, then we have no experience database to rely on. Instead, we need to consider concepts that we have seen in other domains
If problems are seen before, we can apply surface analogy, which is kind learning
Human intuition is not very good at analogical thinking; we are used to kind learning enviornments where problems and solutions repeat
We have to encourage using analogies from vastly different domains, not just the surface level domains. This is how we come up with novel solutions
Problem is that we are often tempted to just look at the inside view, a cognitive bias
We need to defeat this impulse by looking at analogies from outside world
The more internal details we are given to a problem, the more extreme our judgements
A full reference class of analogies is often better than a single analogy
For wicked problems, we need to evaluate the problem before jumping in, so analogical thinking is a big boon
Best people that solve problems are those that are able to classify the type of problem
Labs that often have the most breakthrough are ones that are Kepler in nature: they use a wide variety of analogies from different domains and their members are from different domains
Biggest problem is that there is no entrenched benefit of going broad vs narrow
The Trouble with Too Much Grit
J.K. Rowling, Vincent van Gogh, Gaugin are all examples of late starts
Trade off between late starts and early start is between domain skills vs. knowledge of what type of work you liked best
Regardless of when switching occured, it boosts growth rate significantly. Switchers win
Quitting is advantageous when you know that it is not good for you. Winners often quit fast
Career finding is a multi-armed bandit problem, try to get as much information as possible about all possibilities and then invest in one
Flirting with Your Possible Selves
Ex: Frances Hesselbein. Spent majority of her life trying out different things but got CEO of multiple organizations by mid-fifties. Propelled her forwards
Majority of people actually have a circuitious path to where they are now
These ‘dark horses’ practice something called short-term planning
Many of us fall for the ‘end of history solution’: we recognize that we took a winding path to get where we are now but we don’t admit that this will continue in the future
Personality traits are subject of our context: we need to understand which contexts give us the maximum pleasure in our work, i.e. match quality
We need to try out some of the things that we think is best and then specialize. Long-term plans < short-term experiments
Flirt with your possibles selves by designing small scale experiments and see if you like it
The Outsider Advantage
Several stories of new inventions and creations from people that look at problem from outside and apply techniques from a completely different domain
Ex:// invention of canned food, InnoCentive, NASA solar flare prediction
InnoCentive actually found that the farther away a person’s specialization and domain expertise is to the problem, the more likely they were to solve it
Specialized organizations tend to go for a local search, which is suboptimal
By widely distributing knowledge, you allow curious people to use their broad range of knowledge to construct amazing connections.
Lateral Thinking with Withered Technology
Lateral thinking is the use of knowledge from adjacent domains, withered technology is old technology. Nintendo first started off by combining old technology using adjacent domain knowledge
In other words, Nintendo started off by making cheap, simple toys in ways no one else considered to do
People tend to only think in a narrow domain, known as functional fixedness
Nintendo succeeded because it paired up vertical and lateral thinkers together
The most likely to succeed in 3M inventor awards are polymaths: super in-depth in one area but relatively broad everywhere else
With increasing ambiguity in our careers and a lack of well-defined problems, the utility of T-shaped people will increase in the near future
Studies on comic book creators found that certain individuals far surpassed teams because they had extraordinary breadth, not length of experience or interesting skills
Diverse individual > diverse teams > specialized teams > specialized individuas
Many breadth individuals simply correspond to specialists to confirm/exchange ideas. Broad network as well (eg. Charles Darwin)
Fooled by Expertise
People that dig into certain ideas and refuse to let go despite contrary evidence are often wrong
In the book Superforecasters, this same pattern repeated: best forecasters were those who knew little about everything and integrated together; worst were narrow people
These superforecasters actively tried to limit confirmation bias and looked for evidence to falsify their claims and assertions
Learning to Drop Your Familiar Tools
Ex:// Challenger disaster. Engineers never asked whether the data that they have is the right data or if they could get more data
One problem was NASA’s incredibly quantitative culture. Everything needed to be backed up by data. Engineers could only come up with qualitative data
Many people become rigid under pressure and increasingly rely on tools that don’t work
Some of the most successful organizations are ones where there is no congruent culture, where no one methodology is considered supreme.
Introduce incongruence and introduce actions that goes against org culture. This helps people understand what actually works
Create circular hierarchies where people at the top get input from people at the bottom
Deliberate Amateurs
Ex:// gel electrophoresis, Southern blot, graphene and malaria medications all created by people that weren’t specialized
System is meant for specializers, not for breadth people
Most successful networks allowed people to easily among teams, preventing silos
Work that appeared to be connecting different knowledge networks are often not rewarded in the start but gain massive appeal in the scientific community
We need to build systems where range and breadth are important aspects