The prepared mind: How to Approach Craft

May 5, 2019

The Prepared Mind: How to approach craft

READING TIME: 8 MINUTES

This past weekend, I attended a conference focused on one of the latest buzz terms “transformative tech.” I’ve been tracking them since they started and it appears to be nice marketing to VCs for—you guessed it—tech, but specifically tech “for mental health, emotional well being, and human flourishing.” For this group, there needs to be electricity, or a battery involved so the work of teachers, coaches, seekers, etc. are not emphasized.

One of the speakers was extrapolating on how one day, we will have tech that “gets us to flow” or “helps us jump quickly to a meditative state.” This is not the first time I’ve heard this idea being discussed and I listened intently, thinking through the potential consequences of what amounts to “skipping the [learning] journey” of learning and moving right into integration.

Learning, the kind that moves us forward in our lives and work, is fraught with struggle. There is struggle to accept a new idea, reason with it, and integrate into a new, broader understanding. Reasoning is a skill that is in precarious decline as reliance on data increases. What depth of experience (and insight) might be lost if technology could help us avoid the ungainliness, awkwardness, anxiety-prone beginner’s mind of a new idea or activity? What would be lost if we were able to skip important phases of developing mastery?

In an age obsessed with tech, repackaged ideas, and Instagram soundbites, some of the great tech thinkers get lost to history. I’d like to share some learning from the great Richard Hamming. Hamming was an American mathematician whose work had many implications for computer engineering and telecommunications. He programmed IBM’s calculating machines. He was involved in nearly all of Bell Laboratories' most prominent achievements. After retiring, Hamming took a position at the Naval Postgraduate School and devoted himself to teaching and writing books. He delivered his last lecture in December 1997, just a few weeks before he died from a heart attack on January 7, 1998.

You can see it here. I call out some of his major ideas as they relate to my research on Master Craftsmen, how they get better at what they do, and what we (in tech and in business) can learn from them.

How to Work With Craft In Whatever You Do

Insight, skill, or the state of consciousness gained by daily, deliberate practice, are rarely handed to you on a silver platter. Einstein argued that genius was 99% perspiration and 1% inspiration. Picasso referred to “inspiration [existing] but needs to find you at work.” While we can acknowledge that luck plays a role, we often use that as a crutch to avoid doing what we can do to intelligently prepare for opportunities. Perspiration and work are, in my opinion, integral to effective integration.

We only get one life, “and it seems to be it is better to do significant things than to just get along through life to its end,” writes Richard Hamming in his 1997 book The Art of Doing Science and Engineering: Learning to Learn. The book and his 1986 lecture, 'Luck favors the prepared mind', You and Your Research, explore how we do great work. Specifically, he refers to “Nobel-Prize type work.” Nobel level work raises the standards of what we thought possible and teaches us to think in new ways. In many ways, Nobel prize winners are the craftsmen in their respective fields.

From cooking to coding, there are methods to engage in order to do something with Craft. That’s how LEAN, DMAIC. 6Sigma and my other methodologies came to be. There are also mental disciplines we can learn for more effective thinking. But, where to start?

Hamming suggests that preparation is what separates the good from the great. This means the way you live your life—the extent to which you intelligently prepare—makes a huge difference in what you can accomplish.

As human beings, we tend fixate on what we can see with our eyes. We think focusing on the concrete is being objective. It’s how we rationalize. When we look at transformations in other people’s lives, we see good luck, natural talent, unfair advantage, or the right connections. We concentrate on the visible signs of opportunity and success. We do this with organizations too — and it’s an illusion.

The key to any change is insanely simple. Stop fixating on the external and focus on smaller, internal changes. It is the difference between grasping at an illusion and immersing yourself in reality. Reality is what transforms you.

The major objection cited by people against striving to do great things is the belief it is all a matter of luck. I have repeatedly cited Pasteur’s remark, “Luck favors the prepared mind”. It both admits there is an element of luck, and yet claims to a great extent it is up to you. You prepare yourself to succeed, or not, as you choose, from moment to moment, by the way you live your life.
Hamming, Richard R. Art of Doing Science and Engineering: Learning to Learn (Page 209)

Luck is always part of the equation. Philosophers, political theorists, and strategists have long acknowledged the large role that luck plays in every aspect of our lives. Even Nicolo Machiavelli, the cataloger of each and every lever that a prince can pull in the pursuit of power, acknowledged that “I believe that it is probably true that fortune is the arbiter of half the things we do, leaving the other half to be controlled by ourselves.” What was true in Italian politics centuries ago is just as true in management today.

Yet if life were all about luck, the same people wouldn’t repeatedly do great things. Galileo Galilei did many great things. So did Newton and Einstein. Bill Gates, Elon Musk, and Jeff Bezos have been successful in multiple sectors. The list goes on.

When someone repeatedly achieves greatness, it is because they prepared in advance to recognize, work on, and fill in the blanks when necessary. This is the essence of intelligent preparation and the foundation of deliberate practice—greatness is a byproduct. So often, in the attempt to optimize and recreate, we forget that.

Intelligence comes in many forms. A lot of the time it’s not easily recognized — a lot of people who repeatedly do great things were poor students. IQ does not ensure academic success. Being smart is nice but it’s better if you know how to apply your knowledge.

Believe that you are capable of doing work that matters.

How you regard yourself and your ability to contribute determines how you experience the people in your life, the work you choose, and the tactics and strategies you choose to solve problems.

With the belief that you can do work that matters, why is that most of us spend time on work that doesn’t matter?

…direct observation, and direct questioning of people, shows most scientists spend most of their time working on things they believe are not important nor are they likely to lead to important things.
Hamming, Richard R.. Art of Doing Science and Engineering: Learning to Learn (Page 210).

If what you are working on is not important and aligned with your values—and a lot of what you are working on, what you are saying and what you are doing isn’t either. Think about that.

Is health important to you? When was the last time you invested in it?

Are relationships important to you? What do you do to invest in them?

Do you value the process, really? When was the last time you valued, really valued the many, many small decisions that enable you to achieve the results you are after?

The question you need to ask yourself if why are you not working on and thinking about the important problems in your area? How can we expect to achieve great things if we are not working on the right problems?

Be willing to be an outlier.

Think of this as confidence meets courage. You might look like an idiot because you are doing something new. You might not be immediately understood by those around you because you are challenging the status quo.

[Claude] Shannon had courage. Who else but a man with almost infinite courage would ever think of averaging over all random codes and expect the average code would be good? He knew what he was doing was important and pursued it intensely. Courage, or confidence, is a property to develop in yourself. Look at your successes, and pay less attention to failures than you are usually advised to do in the expression, “Learn from your mistakes”. While playing chess Shannon would often advance his queen boldly into the fray and say, “I ain’t scaird of nothing”. I learned to repeat it to myself when stuck, and at times it has enabled me to go on to a success. I deliberately copied a part of the style of a great scientist. The courage to continue is essential since great research often has long periods with no success and many discouragements.
Hamming, Richard R.. Art of Doing Science and Engineering: Learning to Learn (Page 211)

Embrace horizonal goals.

You have to see excellence as a pursuit not an outcome. This isn’t as easy as it sounds but it as an essential feature of engaging in Craft.

Without such a goal you will tend to remain three degrees off course. You will be headed in the right direction, almost. Three degrees seems small, but that is when you stay the course and say to yourself, “I’m ok, I can still see the hill I’m headed toward.” It isn’t until twenty years later that you realize something isn’t right. You can no longer see that hill. The cumulative effect of being three degrees off course for a long period of time means that it’s either time to backtrack, make a pivot toward that hill, or try some other approach to get you back on course.

…with the goal of doing significant work, there is tendency for the steps to go in the same direction and thus go a distance proportional to the number of steps taken, which in a lifetime is a large number indeed.
Hamming, Richard R.. Art of Doing Science and Engineering: Learning to Learn (Page 211)

What most people think are the best working conditions, are not—learn to dance between failure and fame.

Constraints can lead to innovation. But constraints is just another word for reality, or the lack of the ideal (budget, resources, environment, or other qualities you are seeking). The feedback of reality in order to keep your feet planted on the ground.

Age seems to have the effect it does. In the first place if you do some good work you will find yourself on all kinds of committees and unable to do any more work. You may find yourself as I saw Brattain when he got a Nobel Prize. The day the prize was announced we all assembled in Arnold Auditorium; all three winners got up and made speeches. The third one, Brattain, practically with tears in his eyes, said, “I know about this Nobel-Prize effect and I am not going to let it affect me; I am going to remain good old Walter Brattain.” Well I said to myself, “That is nice.” But in a few weeks I saw it was affecting him. Now he could only work on great problems.
When you are famous it is hard to work on small problems. This is what did Shannon in. After information theory, what do you do for an encore? The great scientists often make this error. They fail to continue to plant the little acorns from which the mighty oak trees grow. They try to get the big thing right off. And that isn't the way things go. So that is another reason why you find that when you get early recognition it seems to sterilize you. In fact I will give you my favorite quotation of many years. The Institute for Advanced Study in Princeton, in my opinion, has ruined more good scientists than any institution has created, judged by what they did before they came and judged by what they did after. Not that they weren't good afterwards, but they were superb before they got there and were only good afterwards.

Work with your door open.

I notice that if you have the door to your office closed, you get more work done today and tomorrow, and you are more productive than most. But 10 years later somehow you don't know quite know what problems are worth working on; all the hard work you do is sort of tangential in importance. He who works with the door open gets all kinds of interruptions, but he also occasionally gets clues as to what the world is and what might be important. Now I cannot prove the cause and effect sequence because you might say, “The closed door is symbolic of a closed mind.” I don't know. But I can say there is a pretty good correlation between those who work with the doors open and those who ultimately do important things, although people who work with doors closed often work harder. Somehow they seem to work on slightly the wrong thing – not much, but enough that they miss fame.

People who do great things typically have a great drive to do things.

…most great scientists have tremendous drive. I worked for ten years with John Tukey at Bell Labs. He had tremendous drive. One day about three or four years after I joined, I discovered that John Tukey was slightly younger than I was. John was a genius and I clearly was not. Well I went storming into Bode's office and said, “How can anybody my age know as much as John Tukey does?” He leaned back in his chair, put his hands behind his head, grinned slightly, and said, “You would be surprised Hamming, how much you would know if you worked as hard as he did that many years.” I simply slunk out of the office!

Focused investment of only one hour a day can double your lifetime output.

Intelligent preparation is like compound interest, the more you invest, the more situations you can handle, the more you learn how to do, so the more you can do, etc. The advantage of investing in just one hour a day to learning new things is an overlooked gem hiding in plain sight.

This isn’t about who works the hardest but rather who focuses their limited energy on the right things. Learning things that (1) change slowly and (2) apply to a wide variety of situations could be a better use of time than learning something incredibly time-consuming, rapidly changing, and of limited application.

Hamming dedicated his Friday afternoons to “great thoughts.” Setting aside time to think is a common characteristic of people that do great things. Not only does this help you live consciously it helps get your head out of the weeds. The rest of us are too busy with the details to ask if we’re going in the right direction.

Consider that advice against a well-intended behavior not truly lived. Google’s 20% time was eventually abandoned. Only about 10% of Googlers were using it. But that didn’t matter much as long as the idea of it exists, according to Google HR boss Laszlo Bock in his new book, "Work Rules!"

Tolerate ambiguity.

Believe and not believe at the same time. You have to believe that where you work is the best place in the industry, and capable of improving.

It took me a while to discover the importance of ambiguity. Most people like to believe something is or is not true. Great scientists tolerate ambiguity very well. They believe the theory enough to go ahead; they doubt it enough to notice the errors and faults so they can step forward and create the new replacement theory. If you believe too much you'll never notice the flaws; if you doubt too much you won't get started. It requires a lovely balance. But most great scientists are well aware of why their theories are true and they are also well aware of some slight misfits which don't quite fit and they don't forget it. Darwin writes in his autobiography that he found it necessary to write down every piece of evidence which appeared to contradict his beliefs because otherwise they would disappear from his mind. When you find apparent flaws you've got to be sensitive and keep track of those things, and keep an eye out for how they can be explained or how the theory can be changed to fit them. Those are often the great contributions. Great contributions are rarely done by adding another decimal place. It comes down to an emotional commitment. Most great scientists are completely committed to their problem. Those who don't become committed seldom produce outstanding, first-class work.

Still Interested? Check our my Hamming compendium.

Bookshelf: The Black Swan

January 8, 2019

Overview

“Black swans” are highly consequential but unlikely events that are easily explainable –but only in retrospect.
Black swans have shaped the history of technology, science, business and culture.
As the world gets more connected, black swans are becoming more consequential.
The human mind is subject to numerous blind spots, illusions and biases.
One of the most pernicious biases is misusing standard statistical tools, such as the “bell curve,” that ignore black swans.
Other statistical tools, such as the “power-law distribution,” are far better at modeling many important phenomena.
Expert advice is often useless.
Most forecasting is pseudoscience.
You can retrain yourself to overcome your cognitive biases and to appreciate randomness. But it’s not easy.
You can hedge against negative black swans while benefiting from positive ones.

3 Key Points

Highly significant yet unpredictable events, called “black swans,” are under appreciated;
People continually see misleading patterns in data;
We need to embrace randomness and come to terms with black swans.

Recommendation

According to critic Harold Bloom, Hamlet’s predicament is not “that he thinks too much” but rather that “he thinks too well,” being ultimately “unable to rest in illusions of any kind.” The same could be said for philosopher, essayist and trader Nassim Nicholas Taleb, who finds something rotten in misguided yet supremely confident investment gurus, traders, hedge fund managers, Wall Street bankers, M.B.A.s, CEOs, Nobel-winning economists and others who claim that they can predict the future and explain the past. Like everyone else, says Taleb, these so-called “experts” fail to appreciate “black swans”: highly consequential but unlikely events that render predictions and standard explanations worse than worthless. Taleb’s style is personal and literary, but his heterodox insights are rigorous (if sometimes jolted by authorial filigree). This combination makes for a thrilling, disturbing, contentious and unforgettable book on chance and randomness. While Taleb offers strong medicine (even somewhat bitter) it's worth the read for anyone who wants a powerful inoculation against gullibility.

SUMMARY

When All Swans Were White

Before 1697, teachers confidently taught European schoolchildren that all swans were white. They had little reason to think otherwise since every swan ever examined had the same snowy plumage. But then Dutch explorer Willem de Vlamingh landed in Australia. Among the many unlikely creatures down under – odd, hopping marsupials called kangaroos, furry duck-billed platypuses, teddy bear-like koalas – Vlamingh found dark feathered birds that looked remarkably like swans. Black swans? Yes. Once observed, they were as unmistakable as they had been unimaginable, and they forced Europeans to revise forever their concept of “swan.” In time, black swans came to seem ordinary.

This pattern is common. Just because you haven’t seen a black swan, doesn’t mean that there are no black swans. Unlikely events seem impossible when they lie in the unknown or in the future. But after they happen, people assimilate them into their conception of the world. The extraordinary becomes ordinary, and “experts” such as policy pundits and market prognosticators kick themselves because they didn’t predict the (now seemingly obvious) occurrence of the (then) unlikely event. Think of the advent of World Wars I and II, the terrorist attacks of 9/11, the popping of the 1990s Internet stock bubble, or world-changing inventions like the internal combustion engine, the personal computer and the Internet. Cultural fads like the Harry Potter books are the same. These events and inventions came out of nowhere, yet in hindsight, they seem almost inevitable. Why?

The human mind is wonderful at simplifying the onslaught of today’s “booming, buzzing confusion” of data. This makes perfect sense: After all, the brain is the product of evolution, which works with what it has, and so it has not crafted some new, ideal cognitive mechanism. The human brain is a marvel, but it is built for living in hunter-gatherer groups on the African Savannah 200,000 years ago. Then, it just needed to be good enough to allow humans to survive until they reached reproductive age. Simplifications, mental schemas, heuristics, biases, self-deception – these are not “bugs” in the cognitive system, but useful features that allow the human mind to concentrate on the task at hand and not get overwhelmed by a literally infinite amount of data. But human simplifying mechanisms are not without their costs. Take stories, for example.

The Narrative Fallacy

Stories help people remember and make sense of the past. Think of a typical business magazine profile of a successful businessman. The story begins in the present after he has become rich beyond his wildest dreams. The story then cuts back to his humble beginnings. He started with nothing and wanted to get rich (in terms of story structure, his “dramatic need”). He faced obstacle after obstacle (perhaps he had a rival – the “antagonist”). But he made shrewd decisions and flouted the wisdom of the Cassandras who counseled caution (”Idiots!”). As success built on success, he amassed a fortune. He retired early, married a model and now has brilliant children who play Chopin blindfolded and will all attend Ivy League colleges. His virtues will be extolled in a B-School case study. Wide-eyed M.B.A. students will sit rapt at his feet when he visits their schools on a lecture tour promoting his latest book. He is a superman, an inspiration.

Now consider an alternative hypothesis: He got lucky. His putative “virtues” had nothing to do with his success. He is, essentially, a lottery winner. The public looks at his life and concocts a story about how brilliant he was, when, in fact, he was merely at the right place at the right time. This is the “ludic fallacy” (ludus means game in Latin): People underestimate luck in life – though they ironically overestimate it in certain games of “chance.” Even the businessman himself falls victim to flawed thinking through the self-sampling bias. He looks at himself, a sample of one, and draws a sweeping conclusion, such as, “If I can do it, anyone can!” Notice that the same reasoning would apply had he merely bought a winning lottery ticket. “I’m a genius for picking 3293927! Those long odds didn’t mean a darn thing. I mean, after all, I won didn’t I!”

Not all success is luck. In some professions, skill matters (for example, if you are a dentist), but luck dominates in others. In the case of the inspiring businessman, consider his population cohort. Where are all the similarly situated people who started out like him and have the same attributes? Are they also rich? Or homeless? Usually, you can’t find this sort of “silent” disconfirming evidence. Artistic success provides a perfect illustration. While Balzac is famous now, perhaps countless other equally talented writers were producing comparable work at the same time. Yet their writings are lost to posterity because they did not succeed. Their “failure” hides the evidence that would undercut Balzac’s “success” as a uniquely great writer. The evidence is silent, lost in the graveyard of history.

The mind uses many more simplifying schemas that can lead to error. Once people have theories, they seek confirming evidence; this is called “confirmation bias.” They fall victim to “epistemic arrogance,” becoming overconfident about their ideas and failing to account for randomness. To make their theories work, people “smooth out” the “jumps” in a time series or historical sequence, looking for and finding patterns that are not there. Their conceptual categories will limit what they see; this is called “tunneling.” They turn to “experts” for help, but often these expert opinions are no better – and often they are worse – than the “insights” gained from flipping a coin or hiring a trained chimp to throw darts at the stock listings. Worst of all, people steadily fail to consider “black swans,” the highly consequential rare events that drive history.

“Mediocristan” or “Extremistan?”

So the human mind tends to smooth away the rough features of reality. Does this matter? It can matter, and a lot, depending on whether you’re in “Mediocristan” or “Extremistan.” Where are these strange places? Nowhere. They are actually memorable metaphors for remembering two wildly different classes of natural phenomena. Mediocristan refers to phenomena you could describe with standard statistical concepts, like the Gaussian distribution, known as the “bell curve.” Extremistan refers to phenomena where a single, curve-distorting event or person can radically skew the distribution. Imagine citing Bill Gates in a comparison of executive incomes.

To understand the difference, think about human height versus movie ticket sales. While a sample of human beings may contain some very tall people (perhaps someone eight feet tall) and some very short people (perhaps someone two feet tall), you wouldn’t find anyone 3,000 feet tall or an inch tall. Nature limits the heights in the sample. Now consider movie ticket sales. One hit movie can have sales that exceed the median value by such a radical extent that modeling the sample with a Gaussian curve is misleading – thereby rendering the notion of “median value” meaningless. You’d be better off using a different kind of curve for such data, for instance, the “power law” curve from the work of Vilfredo Pareto (of 80/20 “law” fame). In a power law-modeled distribution, extreme events are not treated as outliers. In fact, they determine the shape of the curve.

Social phenomena are impossible to model with the Gaussian normal distribution because these phenomena exhibit “social contagion,” that is, abundant feedback loops. For instance, one reason you want to see a hit movie is that everyone else has seen it and is talking about it. It becomes a cultural event that you don’t want to miss. And neither does anyone else. In these situations, the “rich get richer”: The hit film gets increasingly popular because of its popularity until some arbitrarily large number of people have seen it. And speaking of rich, wealth follows this pattern, too. The extremely wealthy are not just a little bit wealthier than normal rich people; they are so much wealthier that they skew the distribution. If you and Bill Gates share a cab, the average wealth in the cab can be north of $25 billion dollars. But the distribution is not bell-shaped. When this happens, odds are you’re no longer in Kansas. You’re in Extremistan.

Phony Forecasting (or Nerds and Herds)

Extremistan might not be so bad if you could predict when outliers would occur and what their magnitude might be. But no one can do this precisely. Consider hit movies. Screenwriter William Goldman is famous for describing the “secret” of Hollywood hits: Nobody can predict one. Similarly, no one knew whether a book by a mother on welfare about a boy magician with an odd birthmark would flop or make the author a billionaire. Stock prices are the same way. Anyone who claims to be able to predict the price of a stock or commodity years in the future is a charlatan. Yet the magazines are filled with the latest “insider” advice about what the market will do. Ditto for technology. Do you know what the “next big thing” will be? No. No one does. Prognosticators generally miss the big important events – the black swans that impel history.

Chalk these errors up to “nerds and herds.” Nerds are people who can only think in terms of the tools they have been taught to use. When all you have is a hammer, everything becomes a nail. If all you have is Gaussian curves, sigma (standard deviation), and mild, ordinary randomness, you’ll see bell curves everywhere and will explain away disconfirming data as “outliers,” “noise” or “exogenous shocks.” (The proliferation of Excel spreadsheets allowing every user to fit a regression line to any messy series of data doesn’t help.) Further, humans follow the herd and look to “experts” for guidance. Yet, some domains can’t have experts because the phenomena the expert is supposed to know are inherently and wildly random. Of course, this discomforting thought requires a palliative, which is to think that the world is much more orderly and uniform than it often is. This soothing belief usually serves people well. Then comes a stock market drop or 9/11 (on the downside), or Star Wars and the Internet (on the upside), and the curve is shot.

Befriending Black Swans

Even given these grim facts, the world need not become, in Hamlet’s words, “a sterile promontory,” nor need a beautiful sky appear “a foul and pestilent congregation of vapors.” You can tame, if not befriend, the black swan by cultivating some “epistemic virtues:”

Keep your eyes open for black swans – Look around and realize when you are in
Extremistan rather than Mediocristan. Social contagion and rich-get-richer phenomena are clues that you’ve just gotten off the bus in Extremistan.
Beliefs are “sticky,” but don’t get glued to them – Revise your beliefs when confronted with contrary evidence. Dare to say, “I don’t know,” “I was wrong” or “It didn’t work.”
Know where you can be a fool and where you can’t – Are you trying to predict what sort of birthday cake your daughter wants? Or the price of oil in 17 years after investing your life’s savings in oil futures? You can’t help being foolish – no one can. But sometimes foolishness is dangerous, and sometimes it is benign.
Know that in many cases, you cannot know – Think outside your usual, customary conceptual categories. Eliminate alternatives that you know are wrong rather than always trying to find out what is right.
As a forecasting period lengthens, prediction errors grow exponentially – Suspend judgment where evidence is lacking and be wary of overly precise predictions. “Fuzzy” thinking can be more useful. Often you should focus only on consequences, not overly precise probabilities.
Expose yourself to “positive black swans” – And, at the same time, hedge against negative ones. “Bet pennies to win dollars.” Look for asymmetries where favorable consequences are greater than unfavorable ones. Maximize the possibilities of serendipity by, say, living in a city, and having a wide circle of diverse friends and business associates.
Look for the non obvious – Seek out disconfirming evidence for pet theories. Think, “What event would refute this theory?” rather than just stacking up confirming evidence for the sake of consistency, and turning out any evidence that contradicts your notion. In other words: Amassing confirming evidence doesn’t prove a theory or a mental model.
Avoid dogmatism – “De-narrate” the past and remember that stories mislead. That’s the whole point: They are psychological armor against the “slings and arrows of outrageous fortune.” Think for yourself. Avoid nerds and herds.This universe, this planet and your life were highly unlikely. But they happened. Enjoy your good fortune and remember that you are a black swan.

Quotes

“We humans are an extremely lucky species, and...we got the genes of the risk takers. The foolish risk takers, that is.”

“We like stories, we like to summarize, and we like to simplify, i.e., to reduce the dimension of matters.”

“Now, I do not disagree with those recommending the use of a narrative to get attention...It is just that narrative can be lethal when used in the wrong places.”

“Notice that close to two centuries ago people had an idealized opinion of their own past, just as we have an idealized opinion of today's past.”

“I know that history is going to be dominated by an improbable event, I just don't know what that event will be.”

“Prediction, not narration, is the real test of our understanding of the world.”

“I find it scandalous that in spite of the empirical record we continue to project into the future as if we were good at it, using tools and methods that exclude rare events.”

“What matters is not how often you are right, but how large your cumulative errors are.”

“Put yourself in situations where favorable consequences are much larger than unfavorable ones.”

“We misunderstand the logic of large deviations from the norm.”

“Every morning the world appears to me more random than it did the day before, and humans seem to be even more fooled by it than they were the previous day.”

About The Author

Nassim Nicholas Taleb, a former derivatives trader, is Dean’s Professor in the Sciences of Uncertainty at the University of Massachusetts and teaches at New York University’s Courant Institute of Mathematical Sciences. He also wrote Fooled by Randomness.

Mental Models: Story

September 9, 2015

ALL OUR DECISIONS ARE FILTERED THROUGH A STORY—REAL OR IMAGINED—THAT WE BELIEVE.

How might this apply to great teams and cultures?We tell a lot of stories in organizations:

How might this apply to your business?Are you creating a story that includes your stakeholders? Stories can be explicit—simple, episodic narratives. Or a story can be implied, using words that suggest conflict, a hero or other narrative elements. The most powerful stories are well-crafted visions that give significance to mundane tasks.

Consider

What story did you tell yourself about the last person you just met or came into contact with?

See Also

Commitment & Consistency, Autonomy, Authority, Affect Heuristic, Conceptual Metaphor, Priming, Framing, Periodic Events, Task Significance

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In the whirl of our day-to-day interactions, it’s all too easy to forget the nuances that distinguish great teams, great cultures, and great products/services.

Mental Model Flash Cards bring together insights from psychology into an easy reference and brainstorming tool. Each card describes one insight into human behavior and suggests ways to apply this to your teams as well as the design of your products and services.

Mental Models: The Power of Reframing Problems Through Inversion

September 8, 2015

An approach to problem-solving that starts with imagining worst-case scenarios – and then using those scenarios as the basis for developing solutions.

How might this apply to great teams and cultures?
One of the methods used in creative ideation sessions is reverse thinking. Instead of following the ‘normal, logical’ direction of a challenge, you turn it around (or an important element in the challenge) and look for opposite ideas.

How might this apply to great products?
For instance, when designing a chair, you can list the assumptions of a chair (it needs to have legs) and think its opposite (no legs?!) to trigger additional ideas: what if chairs were hanging from the ceiling? or be built as part of the table? or….

OVERVIEW

The concept of Inversion is often interpreted in two different ways, both are valuable to consider.

The first is the idea of considering the opposite. In particular, envision the negative things that could happen in life. The Stoic philosophers like Marcus Aurelius, Seneca, and Epictetus regularly conducted an exercise known as a premeditatio malorum, which translates to a “premeditation of evils.”

The second is the idea of working with the end in mind. German mathematician Carl Gustav Jacob Jacobi was famous for some work on elliptic functions that eludes me. Jacobi often solved difficult problems by following a simple strategy: “man muss immer umkehren” (or loosely translated, “invert, always invert.”).

Both approaches look at the end result uniquely. Considering an opposite asks you to hold your ideal result loosely, and to consider the opposite of your desired result. Working with the end in mind assumes you are keeping the same goal but approaching the solution from a different direction, by backing into it.

Very few problems can be solved directly. The most wicked, intractable problems must be dealt with indirectly. As such, the Inversion model is one of the most powerful mental models in our toolkit as human beings.

If you are always inverting a problem, like the way you play with a Rubik’s cube, you experience them from multiple perspectives. Multiple vantage points challenges your certainty. It can shake your beliefs.

EXAMPLES

Let’s start with the positive-negative notion. When I coach clients, I get many people at major points of transition in their lives and careers. Some are facing big promotions, others are considering leaving their jobs for a second chapter.

Often I’ll ask: what is it you want? Seems like a simple enough question, but it’s really hard to answer. Specifically, what do you really want to happen?

Many people have a very hard time imagining the life, career, or outcome we want because we’ve been conditioned for such long periods of our life (at home, in school, at work) to think a certain way or to embrace a certain idea of success.

However, when asked to consider what would guarantee our unhappiness…and few are at a lack of words.

Let’s move on to the positive-negative notion. I teach an EMBA class called Managing Innovation. Most people take the class to learn how to improve and manage innovation in their organizations.

The course is guided by the central question: What can be done to foster innovation? The answers are pretty standard: engage small teams, enable autonomy, consider the tension of deliberate and emergent strategies, etc. And, by the way, implementing any one of those things in a culture that doesn’t naturally gravitate toward those qualities is really hard.

But if we invert the problem to: How do we avoid becoming traditional or unoriginal? we consider all the things we can do to discourage innovation: reduce feedback loops, increase top-down decision making, enable homogeneous thinking, foster resistance to risk. Generally speaking, we would want to avoid these things, right?

Application

Sounds so easy, doesn’t it? If we were to follow our own council we would have to take our own advice: “Just stop doing these things and do less of these other things instead.” Behavior change of any kind is no small thing.

Moving indirectly gains more ground than directly.

Thinking forward/backward or negative/positive about a problem results in some action, you can also think of adding vs. subtracting.

Despite our best intentions, thinking forward increases the odds that you’ll cause harm (through unintended consequences). For example, drugs designed eradicate one disease might also have adverse effects, become harmful if overused, or cause antibiotic resistance in bacteria.

Thinking backward, call it subtractive avoidance or inversion, you are less likely to cause harm. Inverting the problem won’t always solve it, but it will help you avoid trouble and thinking through some of the undesirable and unintended consequences. You can think of it as the avoiding negativity filter. It’s not sexy but it’s a very easy way to improve.

So what does this mean in practice?

Thinking about what you don’t want isn’t necessarily inspiring, but it does bring clarity and can aid decision making to a problem or question that brings nothing but overwhelm. Many of the smartest people in history have done this naturally.

Inversion helps improve understanding of the problem on which you are focused. By using this method, you are forcing yourself toward doing the work of having an opinion that considers multiple perspectives.

The key takeaway: Spend less time trying seeking the right answer and more time avoiding the wrong answer. Avoiding loss is an easier starting point than seeking gain.

Inversion is part of the network of mental models for good humaning. It contributes to creative thinking and problem solving. Alongside technical skills, people who can master a range of subjective skills are better able to influence, deal with ambiguity, bounce back from setbacks, think creatively, and manage themselves successfully in their pursuit of mastery. Learn more about the 25 Skills.

Mental Models: An Overview

August 18, 2015

increasing the quality of our thinking

A mental model is an explanation of someone’s thought process about how something works in the real world. It is a representation of the surrounding world, the relationships between its various parts and a person’s intuitive perception about his or her own acts and their consequences.

If used responsibly, mental models can inform marketing, product design, and influence technology. If left unchecked, mental models can turn into foibles or minor weaknesses or eccentricities in our character.

Artificial intelligence and predictive data will continue to advance, enabling exponential growth. In AI and machine learning programs, discrimination is caused by data. This “algorithmic bias” occurs when AI and computing systems act not in objective fairness, but according to the prejudices that exist with the people who formulated, cleaned and structured their data. This is not inherently harmful – human bias can be as simple as preferring red to blue – but warning signs have started to appear.

A research team at the University of California Berkeley distinguished pre-existing biases in training data from the technical biases that arise from the tools and algorithms that power these AI systems and from the emergent biases that result from human interactions with them.

Ultimately, the solutions we embrace (whether technically or process-oriented) are only as good as the data it is trained to analyze. How we assess problems includes pre-existing (human) biases. These impact us on an individual and societal level. This kind of bias was found in a risk assessment software known as COMPAS. Courtroom judges used it to forecast which criminals were most likely to offend. When news organization ProPublica compared COMPAS risk assessments for 10,000 people arrested in one county in Florida with data showing which ones went on to re-offend, it discovered that when the algorithm was right, its decision making was fair. But when the algorithm was wrong, people of color were almost twice as likely to be labeled a higher risk, yet they did not re-offend.

Gaining insight to our mental models are how we understand the world. Not only do they shape what we think and how we understand but they shape the connections and opportunities that we see. Mental models help make the complex simple. complexity, why we consider some things more relevant than others, and how we reason.

A mental model is just that…a model. It’s a tool that enables us to make an abstract representation of a complex issue. Models help our brains filter the details of the world so we can focus on the relevant details of an issue.

A path toward better thinking

The quality of our thinking is proportional to the models we are aware of, and our ability to apply them correctly in a situation. The more models you know, the bigger your toolbox. The more models you apply, the more likely you are to see reality with greater clarity and make better decisions. When it comes to improving your ability to make decisions variety (and volume) matters.

Most of us, however, are specialists. Instead of a latticework of mental models, we have a few from our discipline–a few “rules of thumb.” Each specialist sees something different.

When you look at a forest, do you focus on:

the ecosystem? You might be a botanist.
the impact of climate change? You might be an environmentalist.
the state of the tree growth? You might be forestry engineer.
the value of the land? You might be a business person.

None of these perspectives are wrong. And, none of them see the forest in its entirety. That is the value of cross-disciplinary thinking. Understanding the basics of the other perspectives leads to a more well-rounded understanding of the forest allowing for better initial decisions about managing it. That’s latticework.

By putting these disciplines together in our head, we can gain greater proximity to the problem at hand by seeing it in a three dimensional way. If consider the problem merely from one angle, we’ve got a blind spot. And blind spots can kill you.

A Network of Mental Models for “good humaning”

Building your repertoire of mental models will help you make better decisions. Once you know a few, you will start to make connections between them, helping you create a networked understanding of how you operate as a human being. I’ve collected and summarized the ones I’ve found the most useful. You can use them almost like a deck of cards.

One of the reasons I refer to them as “Foibles” is because these biases are universal to us all. They are what make us human. Succumbing to them clouds our view of the world and contributes to making costly mistakes in our relationships, our businesses, and as a society.

I refer to “good humaning” because between learning and integration lies “the journey”, “the struggle”, “the gap.” Part of our work is learning and re-learning what it means to be a good human or to do “humaning” well, by making better decisions in our relationships, business, and society at large.

Remember: Developing this level of self-awareness about how you and others operate is a lifelong project. Stick with it, and you’ll find that you will see reality more clearly, make better decisions more consistently, and help those you love and care with greater your increased presence.

Mental Models Explained

The Map is not the Territory metaphorically illustrates the differences between belief and reality. The phrase was coined by Alfred Korzybski. Our perception of the world is being generated by our brain and can be considered as a ‘map’ of reality written in neural patterns. Reality exists outside our mind but we can construct models of this ‘territory’ based on what we glimpse through our senses.
Higher Order Thinking moves from the easier and safer anticipation of the immediate results of our actions, to thinking farther ahead and thinking holistically. The first approach ensures we get the same results as everyone else. Second-order thinking requires us to not only consider our actions and their immediate consequences but consider the long game. Failing to think through long term effects can invite crisis and disaster.
Inversion is a common method used in creative ideation sessions, also known as reverse thinking. Instead of following the ‘normal, logical’ direction of a challenge, you turn it around (or an important element in the challenge) and look for opposite ideas.
Insider/Outsider Thinking

More coming!

Mental Models: The Map is not the Territory

August 13, 2015

A METAPHOR ILLUSTRATING THE DIFFERENCES BETWEEN BELIEF AND REALITY.

How might this apply to great teams and cultures?

Our perception of the world is being generated by our brain and can be considered as a 'map' of reality written in neural patterns. Reality exists outside our mind but we can construct models of this 'territory' based on what we glimpse through our senses.

How might this apply to great products?

There are times when an old map, one that worked in a particular context, does not apply to a new context.

Consider

Even the best maps are imperfect because they are reductions of what they represent. If a map were to represent the territory with perfect fidelity, it would no longer be a reduction and thus would no longer be useful to us. Scribbling on the map does not change the territory: If you change what you believe about an object, that is a change in the pattern of neurons in your brain. The real object will not change because of this edit. A map can also be a snapshot of a point in time, representing something that no longer exists. This is important to keep in mind as we think through problems and make better decisions.

A DEEPER LOOK…

Case Study: J.C. Penny

Every day, leaders make decisions about maximizing current cash flow and profits or reinvesting and building for the long term. But if decisions were as easy as moving money around from budget A to budget B, there would be a lot more successful businesses. A substantial portion of business failures–from the costly to the catastrophic–can be attributed to not paying attention to the right balance between maximizing current performance and building future potential.

Photo: Andrew Burton/Reuters Ron Johnson, chief executive of J.C. Penney, says the store renovation plan is a success.

Apple’s Ron Johnson made the radar in 2011. Handpicked by Steve Jobs to build the Apple Stores, he is also credited with playing a major role in turning Target from a K-Mart look-alike into the trendy-but-cheap Tar-zhey by the late 1990s and early 2000s.

By 2011, Apple stores the most productive retail per-square-foot basis, leaving Tiffany’s in the dust. The gleaming glass cube on Fifth Avenue became a more popular tourist attraction than the Statue of Liberty.

Asked to apply his success from Apply and Target to J.C.Penny’s, Johnson was hired by Bill Ackman, Steven Roth, and other luminaries to turn around the tired old department store. The chain was attempting to reinvent themselves, leaving behind the core customer in an attempt to gain new ones. This was a much different proposition.

Johnson pitched his idea in with standard Apple suspense and fanfare. JC Penney’s stock price went from $26 in the summer of 2011 to $42 in early 2012 on the strength of the pitch.

The idea failed almost immediately. His new pricing model (eliminating discounting) was a flop. The coupon-hunters rebelled. Much of his new product was deemed too trendy. His new store model was wildly expensive for a middling department store chain – including operating losses purposefully endured, he’d spent several billion dollars trying to affect the physical transformation of the stores. JC Penney customers had no idea what was going on, and by 2013, Johnson was sacked. The stock price sank into the single digits, where it remains two years later.

What went wrong in the quest to build America’s Favorite Store? It turned out that Johnson was using a map of Portland Maine to navigate Portland Oregon. Apple’s products, customers, and history had far too little in common with JC Penney’s. Apple had a rabid, young, affluent fan-base before they built stores; JC Penney’s was not associated with youth or affluence. Apple had shiny products, and needed a shiny store; JC Penney was known for its affordable sweaters. Apple had never relied on discounting in the first place; JC Penney was taking away discounts given prior, triggering massive deprival super-reaction.

“All models are wrong but some are useful.”

— George Box

In other words, the old map was not very useful. Even his success at Target, which seems like a closer analog, was misleading in the context of JC Penney. Target had made small, incremental changes over many years, to which Johnson had made a meaningful contribution. JC Penney was attempting to reinvent the concept of the department store in a year or two, leaving behind the core customer in an attempt to gain new ones. This was a much different proposition.

The main issue was not that Johnson was incompetent. He wasn’t. He wouldn’t have gotten the job if he was. He was extremely competent. But it was exactly his competence and past success that got him into trouble. He was like a great swimmer that tried to tackle a grand rapid, and the model he used successfully in the past, the map that had navigated a lot of difficult terrain, was not the map he needed anymore. He had an excellent theory about retailing that applied in some circumstances, but not in others. The terrain had changed, but the old idea stuck.

Relevant Books:

The Map is not the Territory is part of the network of mental models for good humaning.

Mental Models: Higher Order Thinking

August 5, 2015

Howard Marks, the chairman and co-founder of Oaktree Capital Management, is renowned for his insightful assessments of market opportunity and risk. After four decades spent ascending to the top of the investment management profession, today he is sought out by the world's leading value investors, and his client memos brim with insightful commentary and a time-tested, fundamental philosophy.In his book, The Most Important Thing,

Howard Marks explains the concept of first and second-order thinking, which he calls second-level thinking.

First-level thinking is simplistic and superficial, and just about everyone can do it (a bad sign for anything involving an attempt at superiority). All the first-level thinker needs is an opinion about the future, as in “The outlook for the company is favorable, meaning the stock will go up.” Second-level thinking is deep, complex and convoluted.First-level thinking says, “I think the company’s earnings will fall; sell.” Second-level thinking says, “I think the company’s earnings will fall less than people expect, and the pleasant surprise will lift the stock; buy.”

First level thinking is simplistic. It occurs when we want a fast fix. It does not take consequences into consideration. If you are hungry, you eat.

Second level thinking is more considered. It considers context, alternatives, and unintended consequences. Second order thinkers ask themselves the question "Why?" or "Then what?" to deepen their understanding of an issue. If they are hungry, they consider the consequences of eating when they are hungry versus the time of day or circumstance in which their hunger strikes. They make connections, draw correlations, and notice nuance. Doing this, they are likely to make healthier choices when addressing their hunger.Remember here that consequences are not always negative. Positive outcomes are consequences too.

Short term bias and negativity bias are actually first-order-thinking biases

Humans don’t have a negativity bias. We miss long term trends regardless of whether they’re good or bad. The root cause that makes us miss them is not that we are biased towards short-term choices (that’s a byproduct). Our negativity bias and short-term biases are byproducts of a first-order-thinking bias. Our brain is wired to be unable to perform second-order-thinking while thinking intuitively: therefore, the bias. --via Luca Dellana

With first-level thinking, everyone reaches the same answer. This is where quick fixes and easy answers abound. With second-order thinking, we are all deeply interpreting the same situation uniquely. We apply our unique perspective. We see what others cannot.

Example excerpt from “It’s Not Easy: Memo To Oaktree Clients” by Howard Marks

Improve Your Thinking, as a skill

Now that you know this kind of thinking exists, what will you do differently today? Here's how you can practice it now:

Ask yourself "Why?" "What's It For?" or "Then What?
Think through time — What will the situation be in 1 hour, 1 day, 1 month, 1 year? How will that impact the consequences?
Identify your problem-solution-decision. Consider the pros-cons. Think through the consequences. Reviewing these on a regular basis will help you calibrate your thinking more nimbly.
(Bonus) If you’re using this concept to think about business decisions, consider how it impacts other areas of your ecosystem. How will key players respond? What meaning will employees make of it? How will stakeholders deal with it? How will competitors respond? How will suppliers react? What regulations are we likely to incur?

Often the answer will be little to no impact, but you want to understand the immediate and second-order consequences before you make the decision.The difference of a second-level thinker is effort:

The difference in workload between first-level and second-level thinking is clearly massive, and the number of people capable of the latter is tiny compared to the number capable of the former…First-level thinkers think the same way other first-level thinkers do about the same things, and they generally reach the same conclusions…To outperform the average investor, you have to be able to outthink the consensus.

Getting to the next level, in anything, is the result of things that are first-order negative, second order positive. A situation might appear to have no immediate benefit or payoff, but that doesn't mean that's the case. What it indicates is that there is less competition if deeper thinking yields positive outcomes because everyone who simplistically won’t think things through.Second-order thinking takes effort, effort most people will not invest. It takes effort to think in terms of systems, interactions, and time. But investing this kind of effort sets you apart from everyone else.

Higher Order Thinking is part of the network of mental models for good humaning.