Tag: Nassim Taleb

Standing on the Shoulders of Giants

Reading Time: 11 minutes

Innovation doesn’t occur in a vacuum. Doers and thinkers from Shakespeare to Jobs, liberally “stole” inspiration from the doers and thinkers who came before. Here’s how to do it right.

***

“If I have seen further,” Isaac Newton wrote in a 1675 letter to fellow scientist Robert Hooke, “it is by standing on the shoulders of giants.”

It can be easy to look at great geniuses like Newton and imagine that their ideas and work came solely out of their minds, that they spun it from their own thoughts—that they were true originals. But that is rarely the case.

Innovative ideas have to come from somewhere. No matter how unique or unprecedented a work seems, dig a little deeper and you will always find that the creator stood on someone else’s shoulders. They mastered the best of what other people had already figured out, then made that expertise their own. With each iteration, they could see a little further, and they were content in the knowledge that future generations would, in turn, stand on their shoulders.

Standing on the shoulders of giants is a necessary part of creativity, innovation, and development. It doesn’t make what you do less valuable. Embrace it.

Everyone gets a lift up

Ironically, Newton’s turn of phrase wasn’t even entirely his own. The phrase can be traced back to the twelfth century, when the author John of Salisbury wrote that philosopher Bernard of Chartres compared people to dwarves perched on the shoulders of giants and said that “we see more and farther than our predecessors, not because we have keener vision or greater height, but because we are lifted up and borne aloft on their gigantic stature.”

Mary Shelley put it this way in the nineteenth century, in a preface for Frankenstein: “Invention, it must be humbly admitted, does not consist in creating out of void but out of chaos.”

There are giants in every field. Don’t be intimidated by them. They offer an exciting perspective. As the film director Jim Jarmusch advised, “Nothing is original. Steal from anywhere that resonates with inspiration or fuels your imagination. Devour old films, new films, music, books, paintings, photographs, poems, dreams, random conversations, architecture, bridges, street signs, trees, clouds, bodies of water, light, and shadows. Select only things to steal from that speak directly to your soul. If you do this, your work (and theft) will be authentic. Authenticity is invaluable; originality is non-existent. And don’t bother concealing your thievery—celebrate it if you feel like it. In any case, always remember what Jean-Luc Godard said: ‘It’s not where you take things from—it’s where you take them to.’”

That might sound demoralizing. Some might think, “My song, my book, my blog post, my startup, my app, my creation—surely they are original? Surely no one has done this before!” But that’s likely not the case. It’s also not a bad thing. Filmmaker Kirby Ferguson states in his TED Talk: “Admitting this to ourselves is not an embrace of mediocrity and derivativeness—it’s a liberation from our misconceptions, and it’s an incentive to not expect so much from ourselves and to simply begin.”

There lies the important fact. Standing on the shoulders of giants enables us to see further, not merely as far as before. When we build upon prior work, we often improve upon it and take humanity in new directions. However original your work seems to be, the influences are there—they might just be uncredited or not obvious. As we know from social proof, copying is a natural human tendency. It’s how we learn and figure out how to behave.

In Antifragile: Things That Gain from Disorder, Nassim Taleb describes the type of antifragile inventions and ideas that have lasted throughout history. He describes himself heading to a restaurant (the likes of which have been around for at least 2,500 years), in shoes similar to those worn at least 5,300 years ago, to use silverware designed by the Mesopotamians. During the evening, he drinks wine based on a 6,000-year-old recipe, from glasses invented 2,900 years ago, followed by cheese unchanged through the centuries. The dinner is prepared with one of our oldest tools, fire, and using utensils much like those the Romans developed.

Much about our societies and cultures has undeniably changed and continues to change at an ever-faster rate. But we continue to stand on the shoulders of those who came before in our everyday life, using their inventions and ideas, and sometimes building upon them.

Not invented here syndrome

When we discredit what came before or try to reinvent the wheel or refuse to learn from history, we hold ourselves back. After all, many of the best ideas are the oldest. “Not Invented Here Syndrome” is a term for situations when we avoid using ideas, products, or data created by someone else, preferring instead to develop our own (even if it is more expensive, time-consuming, and of lower quality.)

The syndrome can also manifest as reluctance to outsource or delegate work. People might think their output is intrinsically better if they do it themselves, becoming overconfident in their own abilities. After all, who likes getting told what to do, even by someone who knows better? Who wouldn’t want to be known as the genius who (re)invented the wheel?

Developing a new solution for a problem is more exciting than using someone else’s ideas. But new solutions, in turn, create new problems. Some people joke that, for example, the largest Silicon Valley companies are in fact just impromptu incubators for people who will eventually set up their own business, firm in the belief that what they create themselves will be better.

The syndrome is also a case of the sunk cost fallacy. If a company has spent a lot of time and money getting a square wheel to work, they may be resistant to buying the round ones that someone else comes out with. The opportunity costs can be tremendous. Not Invented Here Syndrome detracts from an organization or individual’s core competency, and results in wasting time and talent on what are ultimately distractions. Better to use someone else’s idea and be a giant for someone else.

Why Steve Jobs stole his ideas

“Creativity is just connecting things. When you ask creative people how they did something, they feel a little guilty because they didn’t really do it. They just saw something. It seemed obvious to them after a while; that’s because they were able to connect experiences they’ve had and synthesize new things.”

— Steve Jobs

In The Runaway Species: How Human Creativity Remakes the World, Anthony Brandt and David Eagleman trace the path that led to the creation of the iPhone and track down the giants upon whose shoulders Steve Jobs perched. We often hail Jobs as a revolutionary figure who changed how we use technology. Few who were around in 2007 could have failed to notice the buzz created by the release of the iPhone. It seemed so new, a total departure from anything that had come before. The truth is a little messier.

The first touchscreen came about almost half a century before the iPhone, developed by E.A. Johnson for air traffic control. Other engineers built upon his work and developed usable models, filing a patent in 1975. Around the same time, the University of Illinois was developing touchscreen terminals for students. Prior to touchscreens, light pens used similar technology. The first commercial touchscreen computer came out in 1983, soon followed by graphics boards, tablets, watches, and video game consoles. Casio released a touchscreen pocket computer in 1987 (remember, this is still a full twenty years before the iPhone.)

However, early touchscreen devices were frustrating to use, with very limited functionality, often short battery lives, and minimal use cases for the average person. As touchscreen devices developed in complexity and usability, they laid down the groundwork for the iPhone.

Likewise, the iPod built upon the work of Kane Kramer, who took inspiration from the Sony Walkman. Kramer designed a small portable music player in the 1970s. The IXI, as he called it, looked similar to the iPod but arrived too early for a market to exist, and Kramer lacked the marketing skills to create one. When pitching to investors, Kramer described the potential for immediate delivery, digital inventory, taped live performances, back catalog availability, and the promotion of new artists and microtransactions. Sound familiar?

Steve Jobs stood on the shoulders of the many unseen engineers, students, and scientists who worked for decades to build the technology he drew upon. Although Apple has a long history of merciless lawsuits against those they consider to have stolen their ideas, many were not truly their own in the first place. Brandt and Eagleman conclude that “human creativity does not emerge from a vacuum. We draw on our experience and the raw materials around us to refashion the world. Knowing where we’ve been, and where we are, points the way to the next big industries.”

How Shakespeare got his ideas

“Nothing will come of nothing.”

— William Shakespeare, King Lear

Most, if not all, of Shakespeare’s plays draw heavily upon prior works—so much so that some question whether he would have survived today’s copyright laws.

Hamlet took inspiration from Gesta Danorum, a twelfth-century work on Danish history by Saxo Grammaticus, consisting of sixteen Latin books. Although it is doubtful whether Shakespeare had access to the original text, scholars find the parallels undeniable and believe he may have read another play based on it, from which he drew inspiration. In particular, the accounts of the plight of Prince Amleth (which has the same letters as Hamlet) involves similar events.

Holinshed’s Chronicles, a co-authored account of British history from the late sixteenth century, tells stories that mimic the plot of Macbeth, including the three witches. Holinshed’s Chronicles itself was a mélange of earlier texts, which transferred their biases and fabrications to Shakespeare. It also likely inspired King Lear.

Parts of Antony and Cleopatra are copied verbatim from Plutarch’s Life of Mark Anthony. Arthur Brooke’s 1562 poem The Tragicall Historye of Romeus and Juliet was an undisguised template for Romeo and Juliet. Once again, there are more giants behind the scenes—Brooke copied a 1559 poem by Pierre Boaistuau, who in turn drew from a 1554 story by Matteo Bandello, who in turn drew inspiration from a 1530 work by Luigi da Porto. The list continues, with Plutarch, Chaucer, and the Bible acting as inspirations for many major literary, theatrical, and cultural works.

Yet what Shakespeare did with the works he sometimes copied, sometimes learned from, is remarkable. Take a look at any of the original texts and, despite the mimicry, you will find that they cannot compare to his plays. Many of the originals were dry, unengaging, and lacking any sort of poetic language. J.J. Munro wrote in 1908 that The Tragicall Historye of Romeus and Juliet “meanders on like a listless stream in a strange and impossible land; Shakespeare’s sweeps on like a broad and rushing river, singing and foaming, flashing in sunlight and darkening in cloud, carrying all things irresistibly to where it plunges over the precipice into a waste of waters below.”

Despite bordering on plagiarism at times, he overhauled the stories with an exceptional use of the English language, bringing drama and emotion to dreary chronicles or poems. He had a keen sense for the changes required to restructure plots, creating suspense and intensity in their stories. Shakespeare saw far further than those who wrote before him, and with their help, he ushered in a new era of the English language.

Of course, it’s not just Newton, Jobs, and Shakespeare who found a (sometimes willing, sometimes not) shoulder to stand upon. Facebook is presumed to have built upon Friendster. Cormac McCarthy’s books often replicate older history texts, with one character coming straight from Samuel Chamberlain’s My Confessions. John Lennon borrowed from diverse musicians, once writing in a letter to the New York Times that though the Beatles copied black musicians, “it wasn’t a rip off. It was a love in.”

In The Ecstasy of Influence, Jonathan Lethem points to many other instances of influences in classic works. In 1916, journalist Heinz von Lichberg published a story of a man who falls in love with his landlady’s daughter and begins a love affair, culminating in her death and his lasting loneliness. The title? Lolita. It’s hard to question that Nabokov must have read it, but aside from the plot and name, the style of language in his version is absent from the original.

The list continues. The point is not to be flippant about plagiarism but to cultivate sensitivity to the elements of value in a previous work, as well as the ability to build upon those elements. If we restrict the flow of ideas, everyone loses out.

The adjacent possible

What’s this about? Why can’t people come up with their own ideas? Why do so many people come up with a brilliant idea but never profit from it? The answer lies in what scientist Stuart Kauffman calls “the adjacent possible.” Quite simply, each new innovation or idea opens up the possibility of additional innovations and ideas. At any time, there are limits to what is possible, yet those limits are constantly expanding.

In Where Good Ideas Come From: The Natural History of Innovation, Steven Johnson compares this process to being in a house where opening a door creates new rooms. Each time we open the door to a new room, new doors appear and the house grows. Johnson compares it to the formation of life, beginning with basic fatty acids. The first fatty acids to form were not capable of turning into living creatures. When they self-organized into spheres, the groundwork formed for cell membranes, and a new door opened to genetic codes, chloroplasts, and mitochondria. When dinosaurs evolved a new bone that meant they had more manual dexterity, they opened a new door to flight. When our distant ancestors evolved opposable thumbs, dozens of new doors opened to the use of tools, writing, and warfare. According to Johnson, the history of innovation has been about exploring new wings of the adjacent possible and expanding what we are capable of.

A new idea—like those of Newton, Jobs, and Shakespeare—is only possible because a previous giant opened a new door and made their work possible. They in turn opened new doors and expanded the realm of possibility. Technology, art, and other advances are only possible if someone else has laid the groundwork; nothing comes from nothing. Shakespeare could write his plays because other people had developed the structures and language that formed his tools. Newton could advance science because of the preliminary discoveries that others had made. Jobs built Apple out of the debris of many prior devices and technological advances.

The questions we all have to ask ourselves are these: What new doors can I open, based on the work of the giants that came before me? What opportunities can I spot that they couldn’t? Where can I take the adjacent possible? If you think all the good ideas have already been found, you are very wrong. Other people’s good ideas open new possibilities, rather than restricting them.

As time passes, the giants just keep getting taller and more willing to let us hop onto their shoulders. Their expertise is out there in books and blog posts, open-source software and TED talks, podcast interviews, and academic papers. Whatever we are trying to do, we have the option to find a suitable giant and see what can be learned from them. In the process, knowledge compounds, and everyone gets to see further as we open new doors to the adjacent possible.

Tagged: Antifragile, Creativity, Development, Innovation, Nassim Taleb, Shakespeare, Steve Jobs

The Best of Farnam Street 2018

Reading Time: 3 minutes

We read for the same reasons we have conversations — to enrich our lives.

Reading helps us to think, feel, and reflect — not only upon ourselves and others but upon our ideas, and our relationship with the world. Reading deepens our understanding and helps us live consciously.

Of the 46 articles we published on FS this year, here are the top ten as measured by a combination of page views, responses, and feeling.

Smarter, Not Harder: How to Succeed at Work — We each have 96 energy blocks each day to spend however we’d like. Using this energy blocking system will ensure you’re spending each block wisely.
Your First Thought Is Rarely Your Best Thought: Lessons on Thinking — Most people have no time to think. They schedule themselves like lawyers. They work in five- to eight-minute increments, scheduled back to back. They think only in first thoughts never in second thoughts.
The Pygmalion Effect: Proving Them Right — The Pygmalion Effect is a powerful secret weapon. Without even realizing it, we can nudge others towards success. In this article, discover how expectations can influence performance for better or worse.
First Principles: The Building Blocks of True Knowledge — First Principles thinking breaks down true understanding into building blocks we can reassemble. It turns out most of us don’t know as much as we think we do.
Understanding Speed and Velocity: Saying “NO” to the Non-Essential — It’s tempting to think that in order to be a valuable team player, you should say “yes” to every request and task that is asked of you. People who say yes to everything have a lot of speed. They’re always doing stuff but never getting anything done. Why? Because they don’t think in terms of velocity. Understanding the difference between speed and velocity will change how you work.
The Surprising Power of The Long Game — In everything we do, we play the long or the short game. The short game is easy, pleasurable, and offers visible and immediate benefits. But it almost never leads to success. Here’s how to play the long game.
Double Loop Learning: Download New Skills and Information into Your Brain — We’re taught single loop learning from the time we are in grade school, but there’s a better way. Double loop learning is the quickest and most efficient way to learn anything that you want to “stick.”
Complexity Bias: Why We Prefer Complicated to Simple — Complexity bias is a logical fallacy that leads us to give undue credence to complex concepts. Faced with two competing hypotheses, we are likely to choose the most complex one.
Deductive vs Inductive Reasoning: Make Smarter Arguments, Better Decisions, and Stronger Conclusions — You can’t prove the truth, but using deductive and inductive reasoning, you can get close. Learn the difference between the two types of reasoning and how to use them when evaluating facts and arguments.
The Decision Matrix: How to Prioritize What Matters — The decision matrix is a powerful tool to help you prioritize which decisions deserve your attention as a leader, and which should be delegated. Here’s how you can start using it today.

More interesting things, you might have missed

We published mini-profiles on Seneca, Nassim Taleb, Charlie Munger, Marcus Aurelius, Peter Bevelin, Garrett Hardin, Richard Feynman, and Robert Cialdini
We published our principles
We published a helpful guide to making smart decisions and avoiding bad ones
We updated our list of mental models, expanding to defining 109 of them
A complete list of the ~75 books I read in 2018

Thank you

As we touched on in the annual letter, it’s been a wonderful year at FS. While the frequency of our articles decreased in 2018, the words published actually increased. As longtime readers know, we are not bound to frequency or length constraints, our only mission is quality. Next year will see a more eclectic mix of content as we get back to our roots.

Thank you for an amazing 2018 and I’m looking forward to learning new things with you in 2019.

Still curious? You can find the top five podcast episodes in 2018 here.

Tagged: Best Of Farnam Street, Charlie Munger, Garrett Hardin, Lucius Annaeus Seneca, Marcus Aurelius, Nassim Taleb, Richard Feynman

Winner Takes it All: How Markets Favor the Few at the Expense of the Many

Reading Time: 14 minutes

Markets tend to favor unequal distributions of market share and profits, with a few leaders emerging in any industry. Winner-take-all markets are hard to disrupt and suppress the entry of new players by locking in market share for leading players.

***

In almost any market, crowds of competitors fight for business within their niche. But over time, with few exceptions, a small number of companies come to dominate the industry.

These are the names we all know. The logos we see every day. The brands which shape the world with every decision they make. Even those which are not household names have a great influence on our lives. Operating behind the scenes, they quietly grow more powerful each year, often sowing the seeds of their own destruction in the process.

A winner-take-all market doesn’t mean there is only one company in the market. Rather, when we say a winner takes all, what we mean is that a single company receives the majority of available profits. A few others have at best a modest share. The rest fight over a miniscule remnant, and tend not to survive long.

In a winner-take-all market, the winners have tremendous power to dictate outcomes. Winner-take-all markets occur in many different areas. We can apply the concept to all situations which involve unequal distributions.

Unequal Distribution

As a general rule, resources are never distributed evenly among people. In almost every situation, a small number of people or organizations are the winners.

Most of the books sold each year are written by a handful of authors. Most internet traffic is to a few websites. The top 100 websites get more traffic than ranks 100-999 combined (welcome to power laws). Most citations in any field refer to the same few papers and researchers. Most clicks on Google searches are on the first result. Each of these is an instance of a winner-take-all market.

Wealth is a prime example of this type of market. The Pareto Principle states that in a given nation, 20% of the people own 80% of the wealth (the actual figures are 15% and 85%.) However, the Pareto Principle goes deeper than that. We can look at the richest 20%, then calculate the wealth of the richest 20% of that group. Once again, the Pareto principle applies. So roughly 4% own 64% of the wealth. Keep repeating that calculation and we end up with about 9 people. By some estimates, this tiny group has as much as wealth as the poorest half of the world.

“With limited time or opportunity to experiment, we intentionally narrow our choices to those at the top.”

— Seth Godin

The Perks of Being the Best

There are tremendous benefits to being the best in any particular area. Top performers might be only slightly more skilled than the people one level below them, yet they receive an exponential payoff. A small difference in relative performance—an athlete who can run 100 meters a few microseconds faster, a leader who can make better decisions, an opera singer who can go a little higher—can mean the difference between a lucrative career and relative obscurity. The people at the tops of their fields get it all. They are the winners in that particular market. And once someone is regarded as the best, they tend to retain that status. It takes a monumental effort for a newcomer to rise to such a position. Every day new people do make it to the top, but it’s a lot easier to stay there than to get there.

Top performers don’t just earn the most. They also tend to receive the majority of media coverage and win most awards. They have the most leverage when it comes to choosing their work. These benefits are exponential, following a power law distribution. A silver medalist might get 10 times the benefits the bronze medalist does. But the gold medalist will receive 10 times the benefits of the silver. If a company is risking millions over a lawsuit, they will want the best possible lawyer no matter the cost. And a surgeon who is 10% better than average can charge more than 10% higher fees. When someone or something is the best, we hear about it. The winners take all the attention. It’s one reason why the careers of Nobel Prize winners tend to go downhill after receiving the award. It becomes too lucrative for them to devote their time to the media, giving talks or writing books. Producing more original research falls by the wayside.

Leverage

One reason the best are rewarded more now than ever is leverage. Up until recently, if you were a nanosecond faster than someone else, there was no real advantage. Now there is. Small differences in performance translate into large differences in real-world benefits. A gold medallist in the Olympics, even one that wins by a nanosecond, is disproportionately rewarded for a very small edge.

Now we all live in a world of leverage, through capital, technology, and productivity. Leveraged workers can outperform unleveraged ones by orders of magnitude. When you’re leveraged, judgment becomes far more important. That small difference in ability can be put to better use. Software engineers can create billions of dollars of value through code. Ten coders working 10 times harder but slightly less effective in their thinking will have nothing to show for it. Just as with winner-take-all markets, the inputs don’t match the outputs.

Feedback Loops

Economist Sherwin Rosen looked at unequal distribution in The Economics of Superstars. Rosen found that the demand for classical music and live comedy is high and continues to grow. Yet each area only employs about two hundred full-time performers. These top-performing comedians and musicians take most of the market. Meanwhile, thousands of others struggle for any recognition. Performers regarded as second best within a field earn considerably less than the top performers, even though the average person cannot discern any difference.

In Success and Luck, Robert H. Frank explains the self-perpetuating nature of winner take all markets:

Is the Mona Lisa special? Is Kim Kardashian? They’re both famous, but sometimes things are famous just for being famous. Although we often try to explain their success by scrutinising their objective qualities, they are in fact often no more special than many of their less renowned counterparts…Success often results from positive feedback loops that amplify tiny initial variations into enormous differences in final outcomes.

Winner-take-all markets are increasingly dictated by feedback loops. Feedback loops develop when the output becomes the input. Consider books. More people will buy a best-selling book because it’s a best-selling book. More people will listen to a song that tops charts. More people will go to see an Oscar winning film. These feedback loops serve to magnify initial luck or manipulation. Some writers will purchase thousands of copies of their own book to push it onto best seller lists. Once it makes it onto the list, the feedback loop will begin and possibly keep it there longer than it merits.¹

It’s hard to establish what sets off these feedback loops. In many cases, the answer is simple: luck. Although many people and organizations create narratives to explain their achievements, luck plays a large role. This is a combination of hindsight bias and the narrative fallacy. In retrospect, becoming the winner in the market seems inevitable. In truth, luck plays a substantial role in the creation of winner-take-all markets. A combination of timing, location and connections serves to create winners. Their status is never inevitable, no matter what they might tell those who ask.

In some cases, governments deliberately strive to create positive feedback loops. Drug patents are one example. These create a powerful incentive for companies to invest in research and development. Releasing a new, copyrighted drug is a lucrative enterprise. As the only company in that particular market, a company can set the price to whatever it wishes. Until the patent runs out, that company is the winner. This is exactly how the market plays out. In 2016, the highest grossing drug company earned $71 billion. The three runners up each earned around $50 billion. From there on, the other drug companies have a comparatively small share of the market.

Profit enables companies to invest in more research and development, pay employees more, and invest in their communities. A positive feedback loop forms. Talented researchers join successful teams. They gather valuable data. Developing new drugs becomes easier. Drug companies gain greater and greater market power over time. A few winners end up with almost total control. They become the names we trust and hold their position, absorbing any risks or scandals. New effective drugs benefit society on the whole, improving our well-being. This winner-take-all market has its upsides. Issues emerge when patent holders set prices above the means of the people who need the drugs most.

Once the patent runs out on a drug (generally after 12 years) any other firm can produce an identical product. Prices soon fall as other companies enter the market. The feedback loop breaks, and the winner no longer takes all. Even so, the former winner will retain a large share of the market. People tend to be unwilling to switch to a new brand of drug, even if it has the same effects.

Ironically, winner-take-all markets tend to perpetuate themselves by attracting more losers. When we look at founders in Silicon Valley or actors in LA, we don’t see the failures. Survivorship bias means we only see those who succeed. Attracted by the thought of winning, growing numbers of people flock to try their luck in the market. Most fail, overconfident and misled. The rewards become even more concentrated. More people are attracted and the cycle continues.

DeBeers Diamonds

In the market for diamonds, there is one main winner: DeBeers. This international corporation controls most of the global diamond market, including mining, trading and retail. For around a century, DeBeers had a complete monopoly. Diamonds are a scarce Veblen good with minimal practical use. The value depends on our perception.

Prior to the late 19th century, the global production of diamonds totaled a couple of pounds a year. Demand barely existed, so no one had much interest in supplying it. However, the discovery of several large mines increased production from pounds to tons. Those who stood to profit recognized that diamonds have no intrinsic value. They needed to create a perception of scarcity. DeBeers began taking control of the market in 1888, quickly forming a monopoly. It had an ambitious vision for the diamond market. DeBeers wanted to promote the stones as irreplaceable. Other gemstones have basically the same properties—hard, shiny rocks which make nice jewelry. As Edward Jay Epstein wrote in 1982:

The diamond invention is far more than a monopoly for fixing diamond prices; it is a mechanism for converting tiny crystals of carbon into universally recognized tokens of wealth, power, and romance. To achieve this goal, De Beers had to control demand as well as supply. Both women and men had to be made to perceive diamonds not as marketable precious stones but as an inseparable part of courtship and married life.

Their ensuing role as winners in the diamond market is all down to clever marketing. Slogans such as “diamonds are forever” have cemented the monopoly. Note that the slogan applies to all diamonds, not their particular brand. Imagine if Apple made adverts declaring “phones are forever”. Or if McDonald’s made adverts saying ”fast food is forever.” That’s how powerful DeBeers is. It can promote the entire market, knowing it will be the one to benefit. Throughout the twentieth century, DeBeer gave famous actresses diamond rings, pitched stories featuring the stones to magazines and incorporated their products into images of the British royal family. As their advertising agency, N. W. Ayer, explained, “There was no direct sale to be made. There was no brand name to be impressed on the public mind. There was simply an idea—the eternal emotional value surrounding the diamond…. The substantial diamond gift can be made a more widely sought symbol of personal and family success—an expression of socioeconomic achievement.”

The Impact of Technology

In our interconnected, globalized world, a few large firms continue to grow in power. Modern technology enables firms like Walmart to open branches all over the world. Without the barriers once associated with communication and supply networks, large firms can take over the local market anywhere they open. Small businesses have a very hard time competing.

When a new market appears, entrepreneurs rush to create products, services or technology. There is a flurry of activity for a few months or year. With time, customers gravitate toward the two or three companies they prefer. Starved of revenue, the other competitors shut down. Technology has exacerbated the growth of winner-take-all markets.

We are seeing this at the moment with ride-hailing services. In a once-crowded marketplace, two giant winners remain to take all the profits. It’s hard to say exactly why Uber and Lyft triumphed over numerous similar services. But it’s unlikely they will lose their market share anytime soon.

The same occurred with search engines. Google has now eliminated any meaningful competition. As their profits soar each year, even their nearest competitors—Yahoo, Bing—struggle. We can see from the example of Google how winner-take-all markets can self-perpetuate. Google is on top, so it gets the best employees, and has high research and development budgets. Google can afford to take risks and accumulate growing mountains of user data. Any losses or failures get absorbed. Consistent growth holds the trust of shareholders. Google essentially uses a form of Nassim Taleb’s barbell strategy. As Taleb writes in The Black Swan:

True, the Web produces acute concentration. A large number of users visit just a few sites, such as Google, which, at the time of this writing, has total market dominance. At no time in history has a company grown so dominant so quickly—Google can service people from Nicaragua to southwestern Mongolia to the American West Coast, without having to worry about phone operators, shipping, delivery, and manufacturing. This is the ultimate winner-take-all case study. People forget, though, that before Google, Alta Vista dominated the search-engine market. I am prepared to revise the Google metaphor by replacing it with a new name for future editions of this book.

The role of data is particularly important. The more data a company has on its customers, the better equipped it is to release new products and market existing ones. Facebook has a terrifying amount of information about its users, so it can keep updating the social network to make it addictive and to lock people in. Newer or less popular social networks are working with less data and cannot compete for attention. A positive feedback loop forms for the entrenched companies. Facebook has a lot of data, and it can use that data to make the site more appealing. In turn, this more attractive Facebook leads people to spend more time clicking and generates even more data.²

Winner-take-all markets can be the result of lock-in. When the costs of switching between one supplier and another are too high to be worthwhile, consumers become locked in. Microsoft is a winner in the software market because most of the world is locked in to their products. As it stands, it would be nearly impossible for anyone to erode the market share Windows possesses. As Windows is copyrighted, no one can replicate it. Threatened by inconvenience, we become loyal to avoid incurring switching costs.

Marc Andreessen described the emergence of winner-take-all technology markets in 2013:

In normal markets, you can have Pepsi and Coke. In technology markets, in the long run, you tend to only have one…. The big companies, though, in technology tend to have 90 percent market share. So we think that generally, these are winner-take-all markets. Generally, number one is going to get like 90 percent of the profits. Number two is going to get like 10 percent of the profits, and numbers three through 10 are going to get nothing.

Leaders in certain areas are becoming winners and taking all because they can leverage small advantages, thanks to technology. In the past, an amazing teacher, singer, accountant, artist or stock broker could only reach a small number of people in their community. As their status grew, they would often charge more and choose to see fewer people, meaning their expertise became even more scarce. Now, however, those same top performers can reach a limitless audience through blogs, podcasts, videos, online courses and so on.

Think of it another way. For most of history we were limited to learning from the people in our community. Say you wanted to learn how to draw. You had access to your community art teacher. The odds they were the best art teacher in the world were extremely slim. Now, however, you can go on the internet and access the best teacher in the world.

For most of history, comedians (or rather, their predecessors such as vaudeville performers) and musicians performed live. There was a distinct limit to how many shows they could do a year and how many people could attend each. So, there were many people at the top of each field, as many as needed to meet audience demand for performers. Now that we are no longer confined to live performances, we gravitate towards a few exceptional entertainers. Or consider the example of sports. Athletes were paid far more modest wages until TV allowed them to leverage their skills and reach millions of homes.

Having more information available offers us further incentives to pay attention only to the winners. Online, we can filter by popularity, look at aggregate reviews, select the first search option, or go with other people’s preferences. With too many options, we google ‘best Chinese restaurant near me’ or ‘best horror film 2016.’ Sorting through all the options is too time-consuming, so the best stay as the best.

“In order to win, you must first survive.”

— Warren Buffett

The Downsides of Winner-Take-All Markets

There are some serious downsides to winner-take-all markets. Economic growth and innovation rely on the emergence of new startups and entrepreneurs with disruptive ideas. When the gale of creative destruction stops blowing, industries stagnate. When a handful of winners control a market, they may discourage newcomers who cannot compete with established giants’ budgets and power over the industry. According to some estimates, startups are failing faster and more frequently than in the past. Investors prefer established companies with secure short-term returns. Even when a startup succeeds, it tends to get acquired by a larger company. Apple, Amazon, Facebook and others acquire hundreds of companies each year.

Winner-take-all markets tend to discourage collaboration and cooperation. The winners have incentive to keep their knowledge and new data to themselves. Patents and copyright are liberally used to suppress any serious competition. Skilled workers are snapped up the second they leave education, and have powerful inducements to stay working for the winners. The result is a prisoner’s dilemma-style situation. Although collaboration may be best for everyone, each individual organization benefits from being selfish. As a result, no one collaborates, they just compete.

The result is what Warren Buffett calls a “moat’—a substantial barrier against competition. Business moats come in many forms. Apple’s superior brand identity is a moat, for example. It has taken enormous investments of resources to build and newer companies cannot compete. No number of Facebook adverts or billboards could replicate the kind of importance Apple has in our cultural consciousness. For other winners, the moat could be the ability to provide a product or service at a lower price than competitors, as with Amazon and Alibaba. Each of these has a great deal of market power and can influence prices. If Amazon drops their prices, competitors have no choice but to do the same and make less profit. If Apple decides to raise their prices, we are unlikely to buy our phones and laptops elsewhere and will pay a premium. As Greg Mankiw writes in Principles of Microeconomics, “Market power can cause markets to be inefficient because it keeps the price and quantity away from the equilibrium of supply and demand.”

Luckily for us, winners tend to sow the seeds of their own destruction—but we’ll save that for another article.

Members of the Farnam Street Learning Community can discuss this on the member forum.

Tagged: Edward Jay Epstein, Marc Andreessen, Nassim Taleb, Robert H. Frank, Sherwin Rosen

Footnotes

1
For related thoughts see activation energy and escape velocity.
2
An argument could be made, that data should be anonymized and available to the public as a means to ensure competition.

Thought Experiment: How Einstein Solved Difficult Problems

Reading Time: 16 minutes

Thought experiments are a classic tool used by many great thinkers, which enable us to explore often impossible situations and predict their implications and outcomes. Mastering thought experiments can help you to stretch your mind by confronting difficult questions.

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The Great Mental Models Volumes One and Two are out.
Learn more about the project here.

The Basics of Thought Experiment

Imagine a small town with a hard-working barber. The barber shaves everyone in the town who does not shave themselves. He does not shave anyone who shaves themselves. So, who shaves the barber?

The ‘impossible barber’ is one classic example of a thought experiment — a means of exploring a concept, hypothesis or idea through extensive thought. When finding empirical evidence is impossible, we turn to thought experiments to unspool complex concepts.

In the case of the impossible barber, setting up an experiment to figure out who shaves him would not be feasible or even desirable. After all, the barber cannot exist. Thought experiments are usually rhetorical. No particular answer can or should be found.

The purpose is to encourage speculation, logical thinking and to change paradigms. Thought experiments push us outside our comfort zone by forcing us to confront questions we cannot answer with ease. They reveal that we do not know everything and some things cannot be known.

“All truly wise thoughts have been thought already thousands of times; but to make them truly ours, we must think them over again honestly, until they take root in our personal experience.”

— Johann Wolfgang von Goethe

In a paper entitled Thought Experimentation of Presocratic Philosophy, Nicholas Rescher writes:

Homo sapiens is an amphibian who can live and function in two very different realms- the domain of actual facts which we can investigate in observational inquiry, and the domain of the imaginative projection which we can explore in thought through reasoning…A thought experiment is an attempt to draw instruction from a process of hypothetical reasoning that proceeding by eliciting the consequences of a hypothesis which, for anything that one actually knows to the contrary, may be false. It consists in reasoning from a supposition that is not accepted as true- perhaps even known to be false but is assumed provisionally in the interests of making a point or resolving a conclusion.

As we know from the narrative fallacy, complex information is best digested in the form of narratives and analogies. Many thought experiments make use of this fact to make them more accessible. Even those who are not knowledgeable about a particular field can build an understanding through thought experiments. The aim is to condense first principles into a form which can be understood through analysis and reflection. Some incorporate empirical evidence, looking at it from an alternative perspective.

The benefit of thought experiments (as opposed to aimless rumination) is their structure. In an organized manner, thought experiments allow us to challenge intellectual norms, move beyond the boundaries of ingrained facts, comprehend history, make logical decisions, foster innovative ideas, and widen our sphere of reference.

Despite being improbable or impractical, thought experiments should be possible, in theory.

The History of Thought Experiments

Thought experiments have a rich and complex history, stretching back to the ancient Greeks and Romans. As a mental model, they have enriched many of our greatest intellectual advances, from philosophy to quantum mechanics.

An early example of a thought experiment is Zeno’s narrative of Achilles and the tortoise, dating to around 430 BC. Zeno’s thought experiments aimed to deduce first principles through the elimination of untrue concepts.

In one instance, the Greek philosopher used it to ‘prove’ motion is an illusion. Known as the dichotomy paradox, it involves Achilles racing a tortoise. Out of generosity, Achilles gives the tortoise a 100m head start. Once Achilles begins running, he soon catches up on the head start. However, by that point, the tortoise has moved another 10m. By the time he catches up again, the tortoise will have moved further. Zeno claimed Achilles could never win the race as the distance between the pair would constantly increase.

In the 17th century, Galileo further developed the concept by using thought experiments to affirm his theories. One example is his thought experiment involving two balls (one heavy, one light) which are dropped from the Leaning Tower of Pisa. Prior philosophers had theorized the heavy ball would land first. Galileo claimed this was untrue, as mass does not influence acceleration. We will look at Galileo’s thought experiments in more detail later on in this post.

In 1814, Pierre Laplace explored determinism through ‘Laplace’s demon.’ This is a theoretical ‘demon’ which has an acute awareness of the location and movement of every single particle in existence. Would Laplace’s demon know the future? If the answer is yes, the universe must be linear and deterministic. If no, the universe is nonlinear and free will exists.

In 1897, the German term ‘Gedankenexperiment’ passed into English and a cohesive picture of how thought experiments are used worldwide began to form.

Albert Einstein used thought experiments for some of his most important discoveries. The most famous of this thought experiments was on a beam of light, which was made into a brilliant children’s book. What would happen if you could catch up to a beam of light as it moved he asked himself? The answers led him down a different path toward time, which led to the special theory of relativity.

In On Thought Experiments, 19th-century Philosopher and physicist Ernst Mach writes that curiosity is an inherent human quality. We see this in babies, as they test the world around them and learn the principle of cause and effect. With time, our exploration of the world becomes more and more in depth. We reach a point where we can no longer experiment through our hands alone. At that point, we move into the realm of thought experiments.

Thought experiments are a structured manifestation of our natural curiosity about the world.

Mach writes:

Our own ideas are more easily and readily at our disposal than physical facts. We experiment with thought, so as to say, at little expense. This it shouldn’t surprise us that, oftentime, the thought experiment precedes the physical experiment and prepares the way for it… A thought experiment is also a necessary precondition for a physical experiment. Every inventor and every experimenter must have in his mind the detailed order before he actualizes it. Even if Stephenson knew the train, the rails and the steam engine from experience, he must have, nonetheless, have preconceived in his thoughts the combination of a train on wheels, driven by a steam engine, before he could have proceeded to the realization. No less did Galileo have to envisage, in his imagination, the arrangements for the investigation of gravity, before these were actualized. Even the beginner learns in experimenting than as insufficient preliminary estimate, or nonobservance of sources of error has for him no less tragic comic results than the proverbial ‘look before you leap’ does in practical life.

Mach compares thought experiments to the plans and images we form in our minds before commencing an endeavor. We all do this — rehearsing a conversation before having it, planning a piece of work before starting it, figuring out every detail of a meal before cooking it. Mach views this as an integral part of our ability to engage in complex tasks and to innovate creatively.

According to Mach, the results of some thought experiments can be so certain that it is unnecessary to physically perform it. Regardless of the accuracy of the result, the desired purpose has been achieved.

We will look at some key examples of thought experiments throughout this post, which will show why Mach’s words are so important. He adds:

It can be seen that the basic method of the thought experiment is just like that of a physical experiment, namely, the method of variation. By varying the circumstances (continuously, if possible) the range of validity of an idea (expectation) related to these circumstances is increased.

Although some people view thought experiments as pseudo-science, Mach saw them as valid and important for experimentation.

“Can’t you give me brains?” asked the Scarecrow.
You do not need them. You are learning something every day. A baby has brains, but it does not know much. Experience is the only thing that brings knowledge, and the longer you are on earth the more experience you are sure to get.”

— L. Frank Baum, The Wonderful Wizard of Oz

Types of Thought Experiment

Several key types of thought experiment have been identified:

Prefactual – Involving potential future outcomes. E.g. ‘What will X cause to happen?’
Counterfactual – Contradicting known facts. E.g. ‘If Y happened instead of X, what would be the outcome?’
Semi-factual – Contemplating how a different past could have lead to the same present. E.g. ‘If Y had happened instead of X, would the outcome be the same?’
Prediction– Theorising future outcomes based on existing data. Predictions may involve mental or computational models. E.g. ‘If X continues to happen, what will the outcome be in one year?’
Hindcasting– Running a prediction in reverse to see if it forecasts an event which has already happened. E.g. ‘X happened, could Y have predicted it?’
Retrodiction– Moving backwards from an event to discover the root cause. Retrodiction is often used for problem solving and prevention purposes. E.g. ‘What caused X? How can we prevent it from happening again?’
Backcasting – Considering a specific future outcome, then working forwards from the present to deduce its causes. E.g. ‘If X happens in one year, what would have caused it?’

“With our limited senses and consciousness, we only glimpse a small portion of reality. Furthermore, everything in the universe is in a state of constant flux. Simple words and thoughts cannot capture this flux or complexity. The only solution for an enlightened person is to let the mind absorb itself in what it experiences, without having to form a judgment on what it all means. The mind must be able to feel doubt and uncertainty for as long as possible. As it remains in this state and probes deeply into the mysteries of the universe, ideas will come that are more dimensional and real than if we had jumped to conclusions and formed judgments early on.”

— Robert Greene, Mastery

Thought Experiments in Philosophy

Thoughts experiments have been an integral part of philosophy since ancient times. This is in part due to philosophical hypotheses often being subjective and impossible to prove through empirical evidence.

Philosophers use thought experiments to convey theories in an accessible manner. With the aim of illustrating a particular concept (such as free will or mortality), philosophers explore imagined scenarios. The goal is not to uncover a ‘correct’ answer, but to spark new ideas.

An early example of a philosophical thought experiment is Plato’s Allegory of the Cave, which centers around a dialogue between Socrates and Glaucon (Plato’s brother.)

A group of people are born and live within a dark cave. Having spent their entire lives seeing nothing but shadows on the wall, they lack a conception of the world outside. Knowing nothing different, they do not even wish to leave the cave. At some point, they are lead outside and see a world consisting of much more than shadows.

“The frog in the well knows nothing of the mighty ocean.”

— Japanese Proverb

Plato used this to illustrate the incomplete view of reality most of us have. Only by learning philosophy, Plato claimed, can we see more than shadows.

Upon leaving the cave, the people realize the outside world is far more interesting and fulfilling. If a solitary person left, they would want others to do the same. However, if they return to the cave, their old life will seem unsatisfactory. This discomfort would become misplaced, leading them to resent the outside world. Plato used this to convey his (almost compulsively) deep appreciation for the power of educating ourselves. To take up the mantle of your own education and begin seeking to understand the world is the first step on the way out of the cave.

Moving from caves to insects, let’s take a look at a fascinating thought experiment from 20th-century philosopher Ludwig Wittgenstein. Imagine

Imagine a world where each person has a beetle in a box. In this world, the only time anyone can see a beetle is when they look in their own box. As a consequence, the conception of a beetle each individual has is based on their own. It could be that everyone has something different, or that the boxes are empty, or even that the contents are amorphous.

Wittgenstein uses the ‘Beetle in a Box’ thought experiment to convey his work on the subjective nature of pain. We can each only know what pain is to us, and we cannot feel another person’s agony. If people in the hypothetical world were to have a discussion on the topic of beetles, each would only be able to share their individual perspective. The conversation would have little purpose because each person can only convey what they see as a beetle. In the same way, it is useless for us to describe our pain using analogies (‘it feels like a red hot poker is stabbing me in the back’) or scales (‘the pain is 7/10.’)

Thought Experiments in Science

Although empirical evidence is usually necessary for science, thought experiments may be used to develop a hypothesis or to prepare for experimentation. Some hypotheses cannot be tested (e.g string theory) – at least, not given our current capabilities.

Theoretical scientists may turn to thought experiments to develop a provisional answer, often informed by Occam’s razor.

Nicholas Rescher writes:

In natural science, thought experiments are common. Think, for example, of Einstein’s pondering the question of what the world would look like if one were to travel along a ray of light. Think too of physicists’ assumption of a frictionlessly rolling body or the economists’ assumption of a perfectly efficient market in the interests of establishing the laws of descent or the principles of exchange, respectively…Ernst Mach [mentioned in the introduction] made the sound point that any sensibly designed real experiment should be preceded by a thought experiment that anticipates at any rate the possibility of its outcome.

In a paper entitled Thought Experiments in Scientific Reasoning, Andrew D. Irvine explains that thought experiments are a key part of science. They are in the same realm as physical experiments. Thought experiments require all assumptions to be supported by empirical evidence. The context must be believable, and it must provide useful answers to complex questions. A thought experiment must have the potential to be falsified.

Irvine writes:

Just as a physical experiment often has repercussions for its background theory in terms of confirmation, falsification or the like, so too will a thought experiment. Of course, the parallel is not exact; thought experiments…no do not include actual interventions within the physical environment.

In Do All Rational Folks Think As We Do? Barbara D. Massey writes:

Often critique of thought experiments demands the fleshing out or concretizing of descriptions so that what would happen in a given situation becomes less a matter of guesswork or pontification. In thought experiments we tend to elaborate descriptions with the latest scientific models in mind…The thought experiment seems to be a close relative of the scientist’s laboratory experiment with the vital difference that observations may be made from perspectives which are in reality impossible, for example, from the perspective of moving at the speed of light…The thought experiment seems to discover facts about how things work within the laboratory of the mind.

One key example of a scientific thought experiment is Schrodinger’s cat.

Developed in 1935 by Edwin Schrodinger, Schrodinger’s cat seeks to illustrate the counterintuitive nature of quantum mechanics in a more understandable manner.

Although difficult to present in a simplified manner, the idea is that of a cat which is neither alive nor dead, encased within a box. Inside the box is a Geiger counter and a small quantity of decaying radioactive material. The amount of radioactive material is small, and over a period time, it is equally probable it will decay or not. If it does decay, a tube of acid will smash and poison the cat. Without opening the box, it is impossible to know if the cat is alive or not.

Let’s ignore the ethical implications and the fact that, if this were performed, the angry meowing of the cat would be a clue. Like most thought experiments, the details are arbitrary – it is irrelevant what animal it is, what kills it, or the time frame.

Schrodinger’s point was that quantum mechanics are indeterminate. When does a quantum system switch from one state to a different one? Can the cat be both alive and dead, and is that conditional on it being observed? What about the cat’s own observation of itself?

In Search of Schrodinger’s Cat, John Gribbin writes:

Nothing is real unless it is observed…there is no underlying reality to the world. “Reality,” in the everyday sense, is not a good way to think about the behavior of the fundamental particles that make up the universe; yet at the same time those particles seem to be inseparably connected into some invisible whole, each aware of what happens to the others.

Schrodinger himself wrote in Nature and The Greeks:

We do not belong to this material world that science constructs for us. We are not in it; we are outside. We are only spectators. The reason why we believe that we are in it that we belong to the picture, is that our bodies are in the picture. Our bodies belong to it. Not only my own body, but those of my friends, also of my dog and cat and horse, and of all the other people and animals. And this is my only means of communicating with them.

Another important early example of a scientific thought experiment is Galileo’s Leaning Tower of Pisa Experiment.

Galileo sought to disprove the prevailing belief that gravity is influenced by the mass of an object. Since the time of Aristotle, people had assumed that a 10g object would fall at 1/10th the speed of a 100g object. Oddly, no one is recorded as having tested this.

According to Galileo’s early biography (written in 1654), he dropped two objects from the Leaning Tower of Pisa to disprove the gravitational mass relation hypothesis. Both landed at the same time, ushering in a new understanding of gravity. It is unknown if Galileo performed the experiment itself, so it is regarded as a thought experiment, not a physical one. Galileo reached his conclusion through the use of other thought experiments.

“We live not only in a world of thoughts, but also in a world of things. Words without experience are meaningless.”

— Vladimir Nabokov

Biologists use thought experiments, often of the counterfactual variety. In particular, evolutionary biologists question why organisms exist as they do today. For example, why are sheep not green? As surreal as the question is, it is a valid one. A green sheep would be better camouflaged from predators. Another thought experiment involves asking: why don’t organisms (aside from certain bacteria) have wheels? Again, the question is surreal but is still a serious one. We know from our vehicles that wheels are more efficient for moving at speed than legs, so why do they not naturally exist beyond the microscopic level?

Psychology and Ethics — The Trolley Problem

Picture the scene. You are a lone passerby in a street where a tram is running along a track. The driver has lost control of it. If the tram continues along its current path, the five passengers will die in the ensuing crash. You notice a switch which would allow the tram to move to a different track, where a man is standing. The collision would kill him but would save the five passengers. Do you press the switch?

This thought experiment has been discussed in various forms since the early 1900s. Psychologists and ethicists have discussed the trolley problem at length, often using it in research. It raises many questions, such as:

Is a casual observer required to intervene?
Is there a measurable value to human life? I.e. is one life less valuable than five?
How would the situation differ if the observer were required to actively push a man onto the tracks rather than pressing the switch?
What if the man being pushed were a ‘villain’? Or a loved one of the observer? How would this change the ethical implications?
Can an observer make this choice without the consent of the people involved?

Research has shown most people are far more willing to press a switch than to push someone onto the tracks. This changes if the man is a ‘villain’- people are then far more willing to push him. Likewise, they are reluctant if the person being pushed is a loved one.

In Incognito: The Secret Lives of The Brain, David Eagleman writes that our brains have a distinctly different response to the idea of pushing someone and the idea of pushing a switch. When confronted with a switch, brain scans show that our rational thinking areas are activated. Changing pushing a switch to pushing a person and our emotional areas activate. Eagleman summarizes that:

People register emotionally when they have to push someone; when they only have to tip a lever, their brain behaves like Star Trek’s Mr. Spock.

The trolley problem is theoretical, but it does have real world implications. For example, the majority of people who eat meat would not be content to kill the animal themselves- they are happy to press the switch but not to push the man. Even those who do not consume meat tend to ignore the fact they are indirectly contributing to the deaths of animals due to production quotas, which mean the meat they would have eaten ends up getting wasted. They feel morally superior as they are not actively pushing anyone onto the tracks, yet are still like an observer who does not intervene in anyway. As we move towards autonomous vehicles, there may be real life instances of similar situations. Vehicles may be required to make utilitarian choices – such as swerving into a ditch and killing the driver to avoid a group of children.

Although psychology and ethics are separate fields, they often make use of the same thought experiments.

““Ford!” he said, “there’s an infinite number of monkeys outside who want to talk to us about this script for Hamlet they’ve worked out.”

— Douglas Adams, The Hitchhiker's Guide to the Galaxy

The Infinite Monkey Theorem and Mathematics

The infinite monkey theorem is a mathematical thought experiment. The premise is that infinite monkeys with typewriters will, eventually, type the complete works of Shakespeare. Some versions involve infinite monkeys or a single work. Mathematicians use the monkey(s) as a representation of a device which produces letters at random.

In Fooled By Randomness, Nassim Taleb writes:

If one puts an infinite number of monkeys in front of (strongly built) typewriters, and lets them clap away, there is a certainty that one of them will come out with an exact version of the ‘Iliad.’ Upon examination, this may be less interesting a concept than it appears at first: Such probability is ridiculously low. But let us carry the reasoning one step beyond. Now that we have found that hero among monkeys, would any reader invest his life’s savings on a bet that the monkey would write the ‘Odyssey’ next?

The infinite monkey theorem is intended to illustrate the idea that any issue can be solved through enough random input, in the manner a drunk person arriving home will eventually manage to fit their key in the lock even if they do it without much finesse. It also represents the nature of probability and the idea that any scenario is workable, given enough time and resources.

To learn more about thought experiments, and other mental models, check out our book series, The Great Mental Models.

Tagged: Andrew D. Irvine, Edwin Schrodinger, Ernst Mach, Galileo, John Gribbin, Ludwig Wittgenstein, Nassim Taleb, Nicholas Rescher, Pierre Laplace, Plato, Zeno

The Probability Distribution of the Future

Reading Time: 4 minutes

The best colloquial definition of risk may be the following:

“Risk means more things can happen than will happen.”

We found it through the inimitable Howard Marks, but it’s a quote from Elroy Dimson of the London Business School. Doesn’t that capture it pretty well?

Another way to state it is: If there were only one thing that could happen, how much risk would there be, except in an extremely banal sense? You’d know the exact probability distribution of the future. If I told you there was a 100% probability that you’d get hit by a car today if you walked down the street, you simply wouldn’t do it. You wouldn’t call walking down the street a “risky gamble” right? There’s no gamble at all.

But the truth is that in practical reality, there aren’t many 100% situations to bank on. Way more things can happen than will happen. That introduces great uncertainty into the future, no matter what type of future you’re looking at: An investment, your career, your relationships, anything.

How do we deal with this in a pragmatic way? The investor Howard Marks starts it this way:

Key point number one in this memo is that the future should be viewed not as a fixed outcome that’s destined to happen and capable of being predicted, but as a range of possibilities and, hopefully on the basis of insight into their respective likelihoods, as a probability distribution.

This is the most sensible way to think about the future: A probability distribution where more things can happen than will happen. Knowing that we live in a world of great non-linearity and with the potential for unknowable and barely understandable Black Swan events, we should never become too confident that we know what’s in store, but we can also appreciate that some things are a lot more likely than others. Learning to adjust probabilities on the fly as we get new information is called Bayesian updating.

But.

Although the future is certainly a probability distribution, Marks makes another excellent point in the wonderful memo above: In reality, only one thing will happen. So you must make the decision: Are you comfortable if that one thing happens, whatever it might be? Even if it only has a 1% probability of occurring? Echoing the first lesson of biology, Warren Buffett stated that “In order to win, you must first survive.” You have to live long enough to play out your hand.

Which leads to an important second point: Uncertainty about the future does not necessarily equate with risk, because risk has another component: Consequences. The world is a place where “bad outcomes” are only “bad” if you know their (rough) magnitude. So in order to think about the future and about risk, we must learn to quantify.

It’s like the old saying (usually before something terrible happens): What’s the worst that could happen? Let’s say you propose to undertake a six month project that will cost your company $10 million, and you know there’s a reasonable probability that it won’t work. Is that risky?

It depends on the consequences of losing $10 million, and the probability of that outcome. It’s that simple! (Simple, of course, does not mean easy.) A company with $10 billion in the bank might consider that a very low-risk bet even if it only had a 10% chance of succeeding.

In contrast, a company with only $10 million in the bank might consider it a high-risk bet even if it only had a 10% of failing. Maybe five $2 million projects with uncorrelated outcomes would make more sense to the latter company.

In the real world, risk = probability of failure x consequences. That concept, however, can be looked at through many lenses. Risk of what? Losing money? Losing my job? Losing face? Those things need to be thought through. When we observe others being “too risk averse,” we might want to think about which risks they’re truly avoiding. Sometimes the risk is not only financial.

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Let’s cover one more under-appreciated but seemingly obvious aspect of risk, also pointed out by Marks: Knowing the outcome does not teach you about the risk of the decision.

This is an incredibly important concept:

If you make an investment in 2012, you’ll know in 2014 whether you lost money (and how much), but you won’t know whether it was a risky investment – that is, what the probability of loss was at the time you made it.

To continue the analogy, it may rain tomorrow, or it may not, but nothing that happens tomorrow will tell you what the probability of rain was as of today. And the risk of rain is a very good analogue (although I’m sure not perfect) for the risk of loss.

How many times do we see this simple dictum violated? Knowing that something worked out, we argue that it wasn’t that risky after all. But what if, in reality, we were simply fortunate? This is the Fooled by Randomness effect.

The way to think about it is the following: The worst thing that can happen to a young gambler is that he wins the first time he goes to the casino. He might convince himself he can beat the system.

The truth is that most times we don’t know the probability distribution at all. Because the world is not a predictable casino game — an error Nassim Taleb calls the Ludic Fallacy — the best we can do is guess.

With intelligent estimations, we can work to get the rough order of magnitude right, understand the consequences if we’re wrong, and always be sure to never fool ourselves after the fact.

If you’re into this stuff, check out Howard Marks’ memos to his clients, or check out his excellent book, The Most Important Thing. Nate Silver also has an interesting similar idea about the difference between risk and uncertainty. And lastly, another guy that understands risk pretty well is Jason Zweig, who we’ve interviewed on our podcast before.

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If you liked this article you’ll love:

Nassim Taleb on the Notion of Alternative Histories — “The quality of a decision cannot be solely judged based on its outcome.”

The Four Types of Relationships — As Seneca said, “Time discovers truth.”

Tagged: Black Swan, Decision Making, Howard Marks, Nassim Taleb, Risk, Uncertainty

The Green Lumber Fallacy: The Difference between Talking and Doing

Reading Time: 6 minutes

“Clearly, it is unrigorous to equate skills at doing with skills at talking..”

— Nassim Taleb

“All that glitters is not gold,” the saying goes. We’re often fooled by aesthetics of things into thinking they are the thing. The gist of the Green Lumber Fallacy is this: What works in the real world is not necessarily match our stories of why it works. Unimportant details can often seduce us into thinking we know the reasons for something when we really don’t. Only time filters reality from narrative.

***

Before we get to the meat, let’s review an elementary idea in biology that will be relevant to our discussion.

If you’re familiar with evolutionary theory, you know that populations of organisms are constantly subjected to “selection pressures” — the rigors of their environment which lead to certain traits being favored and passed down to their offspring and others being thrown into the evolutionary dustbin.

Biologists dub these advantages in reproduction “fitness” — as in, the famously lengthening of giraffe necks gave them greater “fitness” in their environment because it helped them reach high up, untouched leaves.

Fitness is generally a relative concept: Since organisms must compete for scarce resources, their fitnesses is measured in the sense of giving a reproductive advantage over one another.

Just as well, a trait that might provide great fitness in one environment may be useless or even disadvantageous in another. (Imagine draining a pond: Any fitness advantages held by a really incredible fish becomes instantly worthless without water.) Traits also relate to circumstance. An advantage at one time could be a disadvantage at another and vice versa.

This makes fitness an all-important concept in biology: Traits are selected for if they provide fitness to the organism within a given environment.

Got it? OK, let’s get back to the practical world.

***

The Black Swan thinker Nassim Taleb has an interesting take on fitness and selection in the real world: People who are good “doers” and people who are good “talkers” are often selected for different traits. Be careful not to mix them up.

In his book Antifragile, Taleb uses this idea to invoke a heuristic he’d once used when hiring traders on Wall Street:

The more interesting their conversation, the more cultured they are, the more they will be trapped into thinking that they are effective at what they are doing in real business (something psychologists call the halo effect, the mistake of thinking that skills in, say, skiing translate unfailingly into skills in managing a pottery workshop or a bank department, or that a good chess player would be a good strategist in real life).

Clearly, it is unrigorous to equate skills at doing with skills at talking. My experience of good practitioners is that they can be totally incomprehensible–they do not have to put much energy into turning their insights and internal coherence into elegant style and narratives. Entrepreneurs are selected to be doers, not thinkers, and doers do, they don’t talk, and it would be unfair, wrong, and downright insulting to measure them in the talk department.

In other words, the selection pressures for an entrepreneur are very different from those on a corporate manager or bureaucrat: Entrepreneurs and risk-takers succeed or fail not so much on their ability to talk, explain, and rationalize as their ability to get things done.

While the two can often go together, Nassim figured out that they frequently don’t. We judge people as ignorant when it’s really us who are ignorant.

When you think about it, there’s no a priori reason great intellectualizing and great doing must go together: Being able to hack together an incredible piece of code gives you great fitness in the world of software development while doing great theoretical computer science probably gives you better fitness in academia. The two skills don’t have to be connected. Great economists don’t usually make great investors.

But we often confuse the two realms. We’re tempted to think that a great investor must be fluent in behavioral economics or a great CEO fluent in Mckinsey-esque management narratives, but in the real world, we see this intuition constantly in violation.

The investor Walter Schloss worked from 9-5, barely left his office, and wasn’t considered an entirely high IQ man, but he compiled one of the great investment records of all time. A young Mark Zuckerberg could hardly be described as a prototypical manager or businessperson, yet somehow built one of the most profitable companies in the world by finding others that complemented his weaknesses.

There are a thousand examples: Our narratives about the type of knowledge or experience we must have or the type of people we must be in order to become successful are often quite wrong; in fact, they border on naive. We think people who talk well can do well, and vice versa. This is simply not always so.

We won’t claim that great doers cannot be great talkers, rationalizers, or intellectuals. Sometimes they are. But if you’re seeking to understand the world properly, it’s good to understand that the two traits are not always co-located. Success, especially in some “narrow” area like plumbing, programming, trading, or marketing, is often achieved by rather non-intellectual folks. Their evolutionary fitness doesn’t come from the ability to talk but do. This is part of reality.

The Green Lumber Fallacy

Taleb calls this idea the Green Lumber Fallacy, after a story in the book What I Learned Losing a Million Dollars.

Taleb describes it in Antifragile:

In one of the rare noncharlatanic books in finance, descriptively called What I Learned Losing a Million Dollars, the protagonist makes a big discovery. He remarks that a fellow named Joe Siegel, one of the most successful traders in a commodity called “green lumber,” actually thought it was lumber painted green (rather than freshly cut lumber, called green because it had not been dried). And he made it his profession to trade the stuff! Meanwhile the narrator was into grand intellectual theories and narratives of what caused the price of commodities to move and went bust.

It is not just that the successful expert on lumber was ignorant of central matters like the designation “green.” He also knew things about lumber that nonexperts think are unimportant. People we call ignorant might not be ignorant.

The fact that predicting the order flow in lumber and the usual narrative had little to do with the details one would assume from the outside are important. People who do things in the field are not subjected to a set exam; they are selected in the most non-narrative manager — nice arguments don’t make much difference. Evolution does not rely on narratives, humans do. Evolution does not need a word for the color blue.

So let us call the green lumber fallacy the situation in which one mistakes a source of visible knowledge — the greenness of lumber — for another, less visible from the outside, less tractable, less narratable.

The main takeaway is that the real causative factors of success are often hidden from us. We think that knowing the intricacies of green lumber are more important than keeping a close eye on the order flow. We seduce ourselves into overestimating the impact of our intellectualism and then wonder why “idiots” are getting ahead.

But for “skin in the game” operations, selection and evolution don’t care about great talk and ideas unless they translate into results. They care what you do with the thing more than that you know the thing. They care about actually avoiding risk rather than your extensive knowledge of risk management theories. (Of course, in many areas of modernity there is no skin in the game, so talking and rationalizing can be and frequently are selected for.)

As Taleb did with his hiring heuristic, this should teach us to be a little skeptical of taking good talkers at face value, and to be a little skeptical when we see “unexplainable” success in someone we consider “not as smart.” There might be a disconnect we’re not seeing because we’re seduced by a narrative. (A problem someone like Lee Kuan Yew avoided by focusing exclusively on what worked.)

And we don’t have to give up our intellectual pursuits in order to appreciate this nugget of wisdom; Taleb is right, but it’s also true that combining the rigorous, skeptical knowledge of “what actually works” with an ever-improving theory structure of the world might be the best combination of all — selected for in many more environments than simple git-er-done ability, which can be extremely domain and environment dependent. (The green lumber guy might not have been much good outside the trading room.)

After all, Taleb himself was both a successful trader and the highest level of intellectual. Even he can’t resist a little theorizing.

Tagged: Antifragile, Biology, Evolution, Green Lumber Fallacy, Investing, Map and Territory, Mental Models, Nassim Taleb

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