Summary of Fooled by Randomness, by Nassim Taleb

Short summary reading time: 3 min

Long summary reading time: 23 min

Book reading time: 6h54

Score: 9/10

Book published in: 2001

Main Idea

Randomness, because it is unseen, plays a much bigger role in life than we are ready to accept.

About Fooled by Randomness

Fooled by Randomness is a book written by Nassim Taleb. Taleb used to work as a trader in New York. Then he retired and wrote books.

This one is the first installment of The Incerto, Taleb’s series discussing randomness and asymmetry in life.

The Incerto is made out of:

  1. Fooled by Randomness
  2. The Black Swan
  3. The Bed of Procrustes
  4. Antifragile
  5. Skin in the Game

The main thesis of the book is that we often underestimate the role of chance and randomness in life.

Taleb analyzes several situations where randomness intervenes without our noticing.

This book had an unexpected impact on me.

It killed my impostor syndrome. I learned that we are not as responsible for where we are in life as we think we are. Sometimes, randomness enables you to enjoy perks you think you don’t deserve to enjoy. That doesn’t mean it is wrong. Such is life.

Things in life aren’t as optimized as we think. Randomness can positively impact your life as much as it can negatively impact it.

Just like that, I stopped feeling bad for reaching positions I originally didn’t believe I deserve to reach. If it was random, why should I feel bad about it?

Fooled by Randomness is a great book, but the author unnecessarily lengthens the book at times (for his own enjoyment, sure, but it annoys me).

As a result, I’ll give this book a 9/10.


Get the book here.

Short Summary of Fooled by Randomness

Life is mostly random.

We can’t perceive this randomness because our brains are not wired to.

We approach and interpret life using heuristics, a series of mental shortcuts that help us make sense of the world in a logical manner. However, the way we perceive the world isn’t at all how the world is. We are biased.

Let’s take narratives as an example.

Narratives are compelling stories we tell ourselves. A narrative links a series of random actions to each other to make them appear as a logical sequence of events.

Eg: when I tell myself my own life story, I say that I studied for one year but didn’t like it, so I took a gap year. I make it sound like the gap year was a logical thing to do, while it was in fact completely random.

Part of the job of building narratives is creating “past predictions.”

Making past predictions is the action of looking at historical events and making these events appear as a logical sequence, leading up to the situation we have today. Past predictions make history sound like all that happened in the past was “bound to happen”.

Eg: When we look at it today, many say that 9/11 was perfectly predictable. No, it wasn’t. It only appears predictable after it happened, due to past predictions.

So, what do we use past predictions and narratives for?

We use past predictions and narratives as sources of information to predict the future. The belief we have is that the future will resemble the past, so if we look at the past, we will be able to catch a glimpse of the future.

Unfortunately, this is another fallacy. The future is random and unpredictable.

It’s so unpredictable that the overwhelming majority of predictions we make end up being wrong. This is the first problem. The second problem is that we have a very narrow understanding of history.

History as we know it is not perfect. It’s an account of some of the events that happened in the past. It is missing all of the events that didn’t happen (in an alternative universe, the Titanic didn’t sink) as well as the events we don’t know about.

The past, as a result, is only a little more well-known than the future and the present.

The present isn’t well-known at all.

Few are aware that they’re living history when they’re actually living history. These moments are always realized ulteriorly (eg: no one knew at the beginning of WWI, that it was the beginning of WWI).

Nobody can predict the future, as we said.

People that do and that, somehow, end up being right every time (eg: “legendary investors”) are called lucky fools.

Lucky fools exist because enough people tried to do what they did so that at least one would succeed.

Eg: if 10 000 people try to become day traders, at least one will succeed out of mere luck.

Out of these 10 000 people, the successful ones, the “survivors” are hailed as “legendary investors” by the press, while their success was likely due to mere luck instead of skill. One person, eventually, wins the lottery because there are enough players for that to happen.

The rest of day traders (or lottery losers), those who failed, are called silent evidence.

They’re evidence that predicting the future isn’t possible. But they’re not taken into account in the press, of course. Failure is invisible.

Business books are always about people that succeed, never about those that lose (except for What I learned Losing a Million Dollars). Business books don’t take silent evidence into account.

Silent evidence is another reason why we can’t trust history. Since we can’t trust history, it would be foolish to believe we can predict the future based on the past.

And even if we had a perfect picture of the past, we still wouldn’t be able to use it to predict the future since the past does not resemble the future.

There will always be events happening in the future that have never happened before – hence impossible to predict.

These events, when unpredictable and with high impact, are called Black Swans.

Table of Content

Part I: Solon’s Warning

Part II: Monkeys on Typewriters

Part III: Wax in my Ears


Summary of Fooled by Randomness Written by Nassim Taleb

Probability is not a mere computation of odds on the dice or more complicated variants; it is the acceptance of the lack of certainty in our knowledge and the development of methods for dealing with our ignorance.


The lucky fool is someone that attributes his success to skills instead of luck, when luck played the most part.

In general, we widely underestimate the role of luck in life. We see patterns where there are none and try to explain how events “logically lined up” while in fact, they happened randomly.

The book will further talk about situations in which people mistake luck for skills. The reason for this mistake is that humans are not wired to deal with probabilities.

Part I: Solon’s Warning

When Solon, a wise Greek legislator, visited Croesus, a very rich man, he warned him not to take his wealth for granted as life can change quickly. You can be rich today, and lose everything tomorrow.

-> what comes with luck, can be taken away by luck.
-> the black swan problem: it does not matter how often you succeed if one failure makes you lose it all.

Chapter 1: If you’re so Rich, Why Aren’t You So Smart?

Lucky fools don’t realize they are lucky. In fact, they’re persuaded their success is due to their action and knowledge.

When someone experiences success, their brain rewards them with serotonin. They become confident and seek to reiterate their success. The more they win, the more they want it. It’s a virtuous circle.

When the first failure hits, they start doubting. Then they fail a second time. And the virtuous circle becomes a vicious one.

People cannot conceal their emotions, and emotions play a huge part in how we act.

People that become leaders of their organization are not more skilled than others. They just feel and show emotions differently – they have charisma – which helps them become leaders.

Your Dentist Is Rich

People don’t understand probability.

Imagine a dentist doing his job for 30 years, living in an upper-middle-class house.

Now imagine a janitor winning the lottery and moving in the dentist’s neighborhood, in an upper-middle-class house.

If we look at these two people’s lives in parallel universes, which one is more likely to always obtain the same result?

The dentist. The janitor wins the lottery in one universe out of one million.

In essence, this is what probability is. How likely will one event repeat in a parallel universe?

Chapter 2: A Bizarre Accounting Method

You can’t judge a performance according to the results it gets. You need to judge it according to the cost of the alternatives.

Looking at what might have been is called looking at alternative histories.

Imagine you play Russian roulette for $10 million. 5 times out of 6, you win. 1/6, you die.

Imagine you win and become rich. You write a blog post about how you made $10 million playing Russian roulette, and suddenly, everyone starts to play Russian roulette to become rich.

Would it be smart? No.

Probability is about assessing what may happen in other universes. In the Russian roulette case, there are 6 outcomes (because there is one bullet in a barrel can have 6 bullets in total.)

  1. No bullet – you survive
  2. No bullet – you survive
  3. No bullet – you survive
  4. No bullet – you survive
  5. No bullet – you survive
  6. Bullet – you die

Six outcomes, but one will happen in reality.

Now, imagine thousands of people play Russian roulette for $10 million once a year, for 20 years. Statistically, a few of them will make it to the end.

The media will speak of their incredible wealth. They will be famous, and heroes.

But no one will speak of all the other people that died playing.

Survivorship bias.

This metaphor should help us understand how we should consider wealthy people.

We look at someone’s wealth without looking at how the wealth was obtained, which gives a false impression.

$10 million earned from Russian roulette ≠ $10 million earned being a dentist.

-> certainty is events that are likely to reproduce across different alternative histories.
-> uncertainty is the opposite.

Life is more vicious than Russian roulette, for three reasons.

  1. It delivers the killing bullet so infrequently that you forget it may happen. This is the black swan problem. The one event that provokes complete ruin – or death.
  2. “Life’s barrel” is unobservable. We can’t possibly account for every outcome, every event that “might have happened”. We can’t possibly think about all the alternative histories.
  3. You can’t warn someone of a loss that didn’t happen. Eg: you can’t tell someone that won at Russian roulette that what they did was dangerous if they don’t know they were playing Russian roulette in the first place. Applied to business, you can’t warn people (that are unaware of the situation they are in) that they could have lost it all.

Alternative histories don’t exist to make prophecies or help you “win”. They exist to find out the black swan, and see how likely the result you obtained was in the first place.

In general, we are not wired to estimate risk and probabilities.

People will more readily sign up for insurance against a specific event less likely to happen, than against an abstract event more likely to happen.

Risk detection and risk avoidance do not come from the rational part of the brain, but the emotional one.

Chapter 3: A Mathematical Meditation on History

The author developed a range of tools that help him think about risk.

Let’s have a look at them:

Alternative sample paths: these are the alternative histories we talked about. They are not alternatives in terms of outcome (the result), but in terms of path (what you do to reach the outcome).

If you buy a stock today, and decide to sell it in one year, this tool is interested in all of the prices this stock will have during the year, and that may influence your behavior.

Random sample path: succession of virtual historical events, starting at a given date and ending at another, subjected to some varying level of uncertainty.

Stochastic processes refer to the dynamics of events unfolding over the course of time.

Monte Carlo generator: a simulation tool that simulates anything. We can use it to determine how many times such or such outcome would happen. It’s a tool that enables you to “learn from the future” by looking at the outcome of different paths.

It’s not natural for people to learn from history. They always think “this time, it’s different”, or “this won’t happen to me”.

This is why the economy booms then busts. People don’t learn. The cycle repeats itself.

The denigration of history and its lessons may be called some form of historical determinism. Historical determinism asserts that whatever happened was “bound to happen”. Since people indeed, don’t learn from history, we may assume that history is forced to repeat itself.

When we look at the past, it seems easy in hindsight to predict the events that happened. A lot of people today said “we should have expected and prevented 9/11”.

This is the hindsight bias.

They look at the event with the information they learned after the event happened. As a result, predicting it seems rather easy.

It’s much easier to “predict” an event after it happened, than before.

-> a mistake is not something to be determined after the fact, but in the light of the information until that point.

This means that you should spot a mistake before you make it – there is no point otherwise.

So, why do people keep on looking at history as if it could have been predicted, while it couldn’t?

Because our minds are not designed to understand how the world works, but to find solutions to a problem quickly.

Now, another bias. People that find themselves good at predicting the past, will also find themselves good at predicting the future (while they can’t).

Hence, we never learn.

Unlike hard sciences (physics, chemistry), history cannot lend itself to experimentation. Overall, in the medium to long term, history will deliver most of the possible scenarios.

The idea is ergodicity. It means that under similar conditions, long sample paths would resemble each other (the equivalent is “history is a continuous cycle”).

If we take periods of history that are long enough, we see that they all more or less resemble each other.

In the long term, everyone becomes the average of all of his alternative histories (luck does no longer plays, crushed by time).

Distilled Thinking on Your Palmpilot

What’s the difference between noise and information? Noise has more randomness.

Same thing when we look at the difference between journalism and history.

Journalists should look at the world like historians. Instead of saying “the market is up”, they should say “the market is up but it’s not super relevant as it is coming mainly from noise”. Meaning: the market is up, but it was unpredictable (random).

Mathematically, progress means that new information is more valuable than old information. When in doubt about the value of new info, it is always better to reject it.

Why? Let’s take tech as an example.

The mainstream narrative says that tech changed our lives. But it’s not obvious. When you look at the number of tech patents, only a few of them changed our lives.

But of course, we only see and count the ones that actually worked out (telephone, airplane, etc).

We don’t see all of the tech that we never used.

Because the tech that worked has had a positive impact, people say that that tech is de facto good.

However, the opposite may be true. The opportunity cost of missing the positive tech is small compared to all of the “bad” tech one has to go through to get there.

The same can be said of information. The amount of bad information (noise) one has to go through to find the good one isn’t worth it. Bad information is not only useless, it’s toxic.

Is the information worth the amount of news one has to go through?

It was demonstrated by Shiller. He wrote a paper where he said that markets swung too much in relation to the value they were supposed to indicate. They were too high or too low to pretend to be efficient.

If markets weren’t efficient, it meant that where they stood daily was irrelevant.

Philostratus In Monte Carlo: On The Difference Between Noise And Information

What’s the difference between noise and information?

Imagine a dentist that invests in the stock market in his free time. He is a good investor and earns yearly 15% with a 10% error rate (volatility).

That 15% is over one year. But a lot of things can happen in one year.

When you translate the probability that his portfolio is up broken down in periods of time, this is what happens.

Every second, the dentist has 1/2 chance to see his portfolio going negative.

But if he looks at his portfolio once a year, for twenty years, he will see green 19/20! Not bad.

What does it mean?

  1. Over the short term, the dentist is not looking at the returns of his portfolio, but at the variance.
  2. Our emotions don’t understand that.

-> this explains why people that frequently look at noise aka randomness (aka the news) burn out.

Chapter 4: Randomness, Nonsense, And The Scientific Intellectual

In Vienna in the 1930s, physicists decided to update their scientific methods.

They declared that a statement could fall into two categories.

  1. Deductive: 2 + 2 = 4.
  2. Inductive: verifiable somehow: Eg: It rains in Spain.

Inductive statements are often impossible to be derived any principles from.

These two methods split intellectuals into two groups: scientific intellectuals, and literary intellectuals.

How to recognize them?

If we ask the Monte Carlo generator to produce something, it cannot repeatedly produce scientific intellectual work, as these only happen in fixed, certain conditions. But it can at will reproduce literary intellectual work (work whose results is similar in different conditions).

Chapter 5: Survival Of The Least Fit—Can Evolution Be Fooled By Randomness?

The author tells the story of two traders who, over a few years made an enormous amount of money, only to be wiped out in a crash in a few days.

These people weren’t good investors. They were simply at the right place at the right time, trading the right instruments.

They were fools of randomness.

Here are their traits:

  • An overestimation of the accuracy of their beliefs in some measure, either economic or statistical.
  • A tendency to get married to positions
  • The tendency to change their story: going from short-term trader to “long-term” investor
  • No precise game plan ahead of time as to what to do in the event of losses: they never even imagined they would lose.
  • Absence of critical thinking expressed in absence of revision of their stance with “stop losses”: they refused to use stop losses.
  • Denial

The best traders are often seen as the worst ones in the short and medium-term, but are revealed to be the best ones in the long term.

The worst traders are the best in the short and medium terms, but the worst in the long term. They are the ones making huge gains due to taking huge risks. Until time catches them, and they end up going bust.

This is counter-intuitive because of Darwin. According to Darwinian theory, companies improve because they compete. As time passes by, the bad companies go bankrupt. Only the good ones survive.

But this is false, for two reasons. First, Darwinian selection is based on reproduction, not survival (species improve when they give birth to a better version than their current ones, not when they grow old).

Second, randomness can negatively influence genes.

Steven Jay Gould demonstrated the existence of “genetic noise”. That is, that some species “genetically regressed” due to randomness. Inferior genetic traits, despite being inferior, still managed to survive.

However, this was only in the medium-term. Over a long period of time, species improve (or disappear). Darwin’s theory was about the long-term.

-> we don’t live in a world where things are continually improving.

In fact, things don’t even move continuously.

If we stretch time to infinity, every species will end up disappearing. Darwinian evolution does not mean that all species are at the pinnacle of their form, all of the time.

Similarly, a bad trader that is winning seems like a great reproductive partner for the medium and short term. In reality, he isn’t for either of them.

In red, a bad trader. In blue, a good trader.

Chapter 6: Skewness And Asymmetry

Let’s talk about asymmetry with gambling as an example. Asymmetric odds mean that probability for each event is not 50-50, but say, 40-60.

Asymmetric outcomes mean that the payoffs are not equal.

Imagine that you play a game where you have 999/1000 chances to win $1 (the probability), and 1/1000 chance to lose $10 000.

999/1000 = 0.999
0.999*$1 = $0.999 -> this is your expectation
0.001*-$10 000= -$10 -> this is your expectation

-$10 + $0.999 = -$9.001

Your expectation at this game is that you lose -$9.001.

Probability ≠ expectation.

Bull and Bear Zoology

Bullish and bearish mean little when we prefer the expectation to the probability.

If there’s a 70% chance that the market goes up by 1%, but 30% that it goes down by 10%, the expectation is:

  • (0.7*1%) + (0.30*-10%) = 0.7 – 3 = -2.3%.

The expectation is that the market goes down by 2.3%.

To quote the author:

It is not how likely an event is to happen that matters, it is how much is made when it happens (…). How frequent the profit is irrelevant; it is the magnitude of the outcome that counts.

As a result, you can be bullish on the market but still have negative total expectations.

The author explains then he never sought to make money regularly on the stock market. Quite the opposite.

He made money on “rare events”. These events were unlikely to be predicted and unlikely to happen twice. He did so because these events were widely undervalued – which means he could make a lot of money at one time.

This is also why the author does not predict the future based on the past. Because you have in history, events “that never happened before” but that will happen in the future.

We don’t know about these events. Hence, using the past (where they did not exist) to try to predict the future is useless.

All things that never happened eventually do happen.

The broader the period of history you look at, the better it is. The problem is that people look too much at recent history.

You won’t be able to predict a Black Swan by looking at the past.

The Rare-Event Fallacy

The author defines a rare event where the quote “beware of calm waters” can be applied.

It is, for example, the polite and discreet neighbor that ends up being a serial killer.

The author associates rare events with any misunderstanding of the risks derived from a narrow interpretation of past time series.

A rare event exists precisely because it is unexpected. If it was expected, it would not have happened.

Statistics is based on the idea that the more info we have, the more we can confidently predict a result. It’s true, but only when distribution is symmetric.

If you have an urn with 10 black balls, but ignore whether there are red balls or not, you will find out whether there are red balls much faster than if there isn’t any (you need to draw the 10 balls to be sure there aren’t any red balls, while drawing one red ball would confirm that there is at least one).

Asymmetry in knowledge is extremely important.

Imagine the stock market is an urn with a set number of black and red balls. Now, imagine that someone replaces the red balls with black balls without you knowing. Suddenly, the fundamentals of the urn change, and so will the expectations.

In that case, looking at past results of drawing balls from the urn will not help you much.

When the market’s fundamentals change, you can no longer inspire yourself from the past.

It’s the same thing with the stock market.

Econometrics study that exactly. The stock market in different time series. But is it worth it? The market of 1990 is so different than what it is now. Can we really expect similar outcomes?

Chapter 7: The Problem of Induction

The problem of induction is the following:

No amount of observations of white swans can allow the inference that all swans are white, but the observation of a single black swan is sufficient to refute that conclusion.

Empiricism (observing events and making assumptions about them) is dangerous.

If we look at data about car accidents, we will see that they mostly happen closer to people’s homes.

An empiricist could then tell you “you are more likely to have an accident in your neighborhood”. But is it the case? Or is it that people simply spend more time driving around their homes?

Data in fact, should not be used to prove a statement, but to disprove it. It’s not because something “has never happened” that it “never happens”.

Consider these two statements:

  • A: no swans are black because all swans I have seen were white.
  • B: not all swans are white

You cannot make statement A. But one black swan would be enough to make statement B.

Another logical flaw in predicting the future based on the past is if there were unexpected events in the past. If the past did not resemble its own past, why would our future resemble our past?

Since Past 2 and Past 1 are different, why would Future resemble Past 1? You can’t expect to predict Future based on Past 1.

Furthermore, markets, such as life, do not have symmetric returns. It doesn’t matter how many times you win if only one loss is bigger than all of your gains.

Maximizing your chances of winning only does not lead to maximizing the total expectation of total gains.

Karl Popper came up with a great answer to the problem of induction. According to him, there are two types of scientific theories.

  1. Theories that have been tested and proven wrong (called falsified).
  2. Theories that have not yet been proven wrong.

Why is the theory never right? Because we will never know if all swans are white.

In a way, science is something we can falsify. If we can’t, it’s not science.

-> science should not be taken as seriously as we think.

Newtonian physics was disproved by Einstein. This is why Newtonian physics is science.

Astrology is not science, because the astrologist, upon explaining his mistake, could always say “well, Mars may have been slightly off, which would explain that…”

Karl Popper did not think that information could increase your knowledge, at least for certain disciplines (we don’t know which ones). The reason is that knowledge and discovery are not so much about what we know, but about what we don’t know.

To quote Popper:

These are men with bold ideas, but highly critical of their own ideas; they try to find whether their ideas are right by trying first to find whether they are not perhaps wrong. They work with bold conjectures and severe attempts at refuting their own conjectures.

For Popper, a theory cannot be verified. Only dismissed.

Our memory is not made to remember a list of random facts. We need a story, some logic that links events to each other. Our brain takes random facts and finds a link to explain them.

The induction problem works the same way. It is merely taking a bunch of particulars and making them general.

Doing so, we reduce the existing randomness.

Pascal said that it’s much better to believe in God than not because if God exists, the believer is rewarded. If he does not, the believer loses nothing.

Likewise, you can use statistics when it gives you an edge but refrain from doing so when it does not help you.

Part II: Monkeys on Typewriters

If you take an infinity of monkeys and give them typewriters, one of them would eventually come up with the exact version of the Iliad.

But would that same monkey also type the Odyssey?

Likely no. Yet, people often assume that in life.

The mainstream believes that if someone did well in the past, he’s likely to do well in the future.

It may actually be true, but it depends on two variables: how random was the activity he did well in, and how many monkeys were involved in the operation.

-> the greater the number of business people, the more likely one will do particularly well.

The problem is that you rarely can count all of them at the beginning. Those that lose usually vanish, and society only sees the winners, praising “intelligence” while it may just have been luck.

In an activity where success is widely determined based on luck, the more people try something, the more likely one will eventually succeed.

Chapter 8: Too Many Millionaires Next Door

The author tells the story of a couple earning 500k per year, living in Park Avenue in New York. Despite the fact they did better than 99.5% of people in the country, they consider themselves as losers. Why? Because they compared themselves to their neighbors, all successful people.

No losers live on Park Avenue.

There is a survivorship bias. They only see those that succeed. They don’t see the people that really failed.

Such a problem is found in the book The Millionaire Next Door.

The book explained how people that became millionaires became so by simply…spending as little as possible.

There are two problems with these observations:

  1. Visibility Winners: the people selected were the winning monkeys.
  2. Bull Market: the period during which these people became millionaires was a period of extreme and rapid stock market positive performances.

Survivorship bias is chronic. Why?

Because we draw conclusions on what we see, not of what we don’t see. Because of that, we assume that what we see will be the most probable.

The survivorship bias implies that the highest performing realization will be the most visible. But it’s wrong. It’s wrong because the losers do not show up.

Some financial gurus, for example, “found out” that failing money managers in point A in time had better financial performances in point B than successful managers.

The only problem is that they made the study based on failing managers that had survived up to then. They didn’t take into account the failing managers that went bankrupt.

The same can be said of optimists. Optimists take more risks. Those who win show up and give the advice to be optimistic.

Those who lose disappear, and never give any advice.

Chapter 9: It Is Easier to Buy and Sell Than Fry an Egg

If you go to your dentist, the chances that he can take care of teeth only out of luck is…rather small. The same can be said with a musician. These people’s track record means something. There isn’t much randomness.

But not in business. A track record means little. Too much randomness.

Fooled by Numbers

Let’s take 10 000 fund managers and put them in a Monte Carlo generator.

They each have a 50% of chance to make $10k per year or lose $10k.

Those that lose $10k are done forever, we will never see them again.

Here’s how it will go, for five years.

Year 1Out of 10 000, 5000 managers survive. 5000 change job.
Year 2Out of 5000, 2500 managers survive. 2500 change job.
Year 3Out of 2 500, 1250 managers survive. 1250 change job.
Year 4Out of 1250, 625 managers survive. 625 change job.
Year 5 Out of 625, 313 managers survive. 313 change job.

In the real world, newspapers and biographers will highlight some imaginary qualities that these managers had that “made them succeed”, blinded by the fact that they survived purely out of luck.

Let’s now take 10 000 incompetent managers.

Instead of 50-50, they have a 45% chance to make $10k, and a 55% chance to lose $10k.

After 5 years, we’ll still have 184 managers surviving. Less than 2% than the total, sure, but give them business suits and they’ll be regarded as “legendary investors”.

-> a huge population of bad managers will produce a small number of great track records.
-> the number of managers that survive depends on the number of managers that started.

The same thing goes for individuals experiencing “lucky streaks”. With a sample population big enough, at least one subject will deviate from the mean.

-> the larger the deviation from the mean, the more likely it is coming from luck rather than skills.

Life Is Coincidental

The author makes a range of observations of coincidences that are in fact the result of randomness.

From meeting people you know in random places to having two people in the same room with shared birthdays, these are just the consequence of a sample big enough for such a “coincidence” to happen.

A scientist once looked at data from roulette in a casino, and found a pattern in the result.

Even randomness does not look random.

Chapter 10: Loser Takes All—on The Nonlinearities Of Life

Nonlinearity results from a linear force exerted on an object that causes a disproportionate result.

Eg: you add drops of water into a vase, until one drop makes the vase overflow.

All the drops were the same, but one created a disproportionate event.

These dynamics are often called “chaos theory”. Chaos theory concerns itself primarily with functions in which a small input can lead to a disproportionate response.

They have in essence, nothing to do with chaos, but with asymmetry.

“A butterfly flaps its wings in India and there is a storm in the US” is an example.

The final outcome is often undeserved. The keyboard QWERTY, for example, wasn’t invented to help people type faster but slower, to avoid typewriters breaking down. Now that people learned to use them, we can’t change them.

This effect is called path-dependent outcome.

Microsoft is one of these outcomes. It was not the best software, and Bill Gates was not the best or smartest businessman, but it’s the one that won due to network effects. And when he won, he won big.

These effects go against economics, where the best is supposed to win.

Mathematics Inside and Outside the Real World

Our brain does not like what is non-linear. Progress is one of these things. You practice for a year, and suddenly, you get some results.

Progression isn’t linear. This is why most people give up in the first place. Those who go the extra mile are rewarded.

People that go the extra mile get rewarded.

When It Rains, It Pours

Non-linearity is everywhere.

  • Everyone wants to publish your book, or no one does
  • You whether become really rich, or you stay poor
  • It’s better to have 10 people that love your work than 1000 that appreciate it

The Internet has worsened this. Winner takes all.

Chapter 11: Randomness And Our Mind: We Are Probability Blind

Paris, or the Bahamas?

This chapter is about heuristics described in Daniel Kahneman’s book “Thinking fast and slow” (the book was written after Fooled by Randomness, so it’s normal Taleb speaks about it).

Check out this Elon Musk’s tweet containing 50 of them if you’re interested.

Why We Don’t Marry the First Date

Our brains are made out of modules each tasked to solve a specific problem. You use different parts to look at art or solve an equation.

On Causality

Causality is interesting when the magnitude of the effect is important. If you cycle from Paris to Shanghai with a friend, and beat him by one second, you can’t possibly say you’re better than him as randomness likely played a role.

If you beat your friend by one week, then yes, you can.

You also need to select the right variable that could have caused you to win when looking for an explanation. Is it sleep, diet, or simply genetics? Causality is complex.

Little changes or differences are often noise. Don’t mind them.

Big changes though, aren’t.

Part III: Wax in my Ears

When Odysseus went back to Athens, he had to pass by the mermaids.

They sang to attract the sailors that would throw themselves into the sea, then be killed. Odysseus wanted to listen to their songs, so he put some wax in the ears of the sailor to protect them and tied himself to the mast, instructing his sailors to never release him until they had passed the mermaids.

Odysseus is a hero. We aren’t. We can’t fight our emotions. So we should also put some wax in our ears.

Wittgenstein’s Ruler

Unless you have confidence in the ruler’s reliability, if you use a ruler to measure a table you may also be using the table to measure the ruler.

Unless a reviewer is qualified to review a book, a book review says more about the reviewer than about the book.

Chapter 12: Gamblers’ Ticks And Pigeons In A Box

The author explains that he caught himself at several times repeating a routine that he practiced one day he made a lot of profit.

He was disappointed in himself. While he knew superstition to be pointless, his emotions thought otherwise.

Despite mainstream thinking, superstitions aren’t cultural, but biological.

The psychologist B. F. Skinner showed how both humans and animals constantly look for logical patterns in randomness to help them get what they want.

This is something that we need to deal with, as this is emotional and we cannot split ourselves from our emotions.

Chapter 13: Carneades Comes To Rome: On Probability And Skepticism

For the Greeks, nothing was written in stone. They often changed their mind and were probabilistically oriented, not dogmatic about their positions.

Explained simply, they didn’t mind to doubt and changed their mind often!

Christianity in the middle ages was much more dogmatic, until the Renaissance. Today, society considers that changing your mind is bad.

Yet one of the most famous and successful people to change their mind quickly is George Soros. He is not path-dependent. He doesn’t take action in light of his previous ones. Each day is a clean slate.

The rest of the people are not like George Soros. We have a hard time letting go of an idea when we have invested time into it.

Changing your mind all the time is a rational (not an emotional) thing. People that lack a disposition to attachment in their brain often change their minds – they are called psychopaths.

Computing Instead of Thinking

A hedge fund almost bankrupted the stock market in 1998 when they made decisions based on their own certainty that they could predict the market.

When the crash happened, they didn’t blame it on them. They blamed it on the crash, and on their peers that they claimed attacked them.

When we succeed, we think it is thanks to ourselves. When we fail, we think it is because of randomness.

Chapter 14: Bacchus Abandons Antony

No matter how good we are at choosing, we cannot avoid neither randomness nor emotions. But we can do the right thing, take the heroic or dignified path, to get even with randomness – we can make things right when randomness shackles them.

Remain worthy in times of uncertainty.

Your behavior is the only thing in your life that isn’t random.


First Thought: The Inverse Skills Problem

The higher you go on the corporate ladder, the more money you make. However, the higher you go, the lower the evidence you make any significant contribution.

This is the inverse rule.

There are skills that are visible (being a cook, a dentist, etc) and skills that depend more on randomness (like marketing or business).

The only way you can verify these skills is through repetitiveness. If the cook nails the steak a hundred times, he is skilled.

Consider the difference between judging on the process, and judging on results. People at the bottom of the hierarchical pyramid will be judged on both processes and results. The CEO though, will be judged on results alone.

However, the link between the skills of the CEO and the results of the company is tenuous. He may just need to be charismatic enough to get the company going. The environment plays a huge role in business, much less than in the case of the dentist, or the cook.

CEOs have nothing to lose. When they win, they win big, and when they lose, the shareholders lose.

Imagine two incompetent CEOs. They both make a random decision. One is a disaster and the CEO is fired. The other is a success and the CEO becomes wealthy.

The second CEO becomes a hero, despite being as incompetent as the first one.

When we evaluate CEOs, what we see isn’t necessarily what there is.

Second Thought: On Some Additional Benefits Of Randomness

When you lack knowledge, it enables you to be more relaxed. Those that seek to maximize pleasure are much happier than those who seek to be efficient at all costs.

Man is not made to have a schedule. We are not made to operate within constraints. Knowing everything isn’t always good.

When you optimize too much and notice a little bit of fluctuation (noise), this can cascade into panic.

When you allow for a little bit of randomness, it gives you a buffer, a tolerance threshold for the unexpected.

Unpredictability can be a strong deterrent.

Third Thought: Standing On One Leg

We favor the visible, the embedded, the personal, the narrated, and the tangible; we scorn the abstract. Everything good (aesthetics, ethics) and wrong (Fooled by Randomness) with us seems to flow from it.”

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  • Post category:Summaries
  • Post last modified:May 26, 2022