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The Black Swan

The Black Swan, by Nassim Nicholas Taleb

I like how this book starts with a short autobiography. I learned that Nassim Nicholas Taleb (NNT) grew up in Amioun, Lebanon, he grew up with a true indifference about money, and he read lots and lots of books (100+ per year) because he had a strong intellectual curiosity. Fundamentally, I would say that NNT is a philosopher, and “The Black Swan” is more of a philosophical book than I expected. In my opinion, NNT’s ideas should receive serious consideration; he successfully predicted the 2008 financial crisis and profited heavily from it, and furthermore, “The Black Swan” warns about the consequences of an epidemic, which we are clearly experiencing right now. Written in 2007, “The Black Swan” correctly predicted the housing crisis of 2008 and the pandemic of 2020. NNT deserves some recognition.


One of the major themes of this book is Mediocristan vs Extremistan (Fat Tony vs Dr. John). Conventional statistics and prediction methods support Mediocristan, whereas NNT advocates Extremistan. In Mediocristan, events and quantities follow a well-defined Gaussian bell-curve, whereas in Extremistan environments, events and quantities are dominated by low-probability, high-consequence events. Throughout the book, NNT uses Dr. John to represent the Mediocristan thinkers. These people include engineers, scientists, financial analysts, economic forecasters, and the like who think exceedingly inside the box. Often, they are referred to as “the nerd.” Typically, these types of people are classified by their black suits, white shirts, boring voices, and high-fidelity mathematical models. Dr. John is the perfect embodiment of the type of person that I meet regularly in the computer science and engineering worlds. The Dr. Johns of this world often make me frustrated and think that I do not belong in this world of boxy thinkers. Personally, I don’t want to be a Dr. John, but I think that at times I can easily fall into this categorization. I like the story that NNT uses to epitomize the difference between Dr. John and Fat Tony so much that I’m going to repeat it here. The conclusion is, don’t be a sucker.


NNT (author): Assume that a coin is fair, i.e, has an equal probability of coming up heads or tails when flipped. I flip it ninety-nine times and get heads each time. What are the odds of my getting tails on the next throw?


Dr. John: Trivial question. One half, of course, since you are assuming 50 percent odds for each and independence between draws.


NNT: What do you say, Tony?


Fat Tony: I’d say no more than 1 percent, of course.


NNT: Why? I gave you the initial assumption of a fair coin, meaning that it was 50 percent either way.


Fat Tony: You are either full of crap or a pure sucker to buy that “50-pehcent” business. The coin gotta be loaded. It can’t be a fair game. (Translation: It is far more likely that your assumptions about the fairness are wrong than the coin delivering ninety-nine heads in ninety-nine throws.)


Fat Tony (whispering in NNT’s ear): I know these guys with the nerd examples from the bank days. They think way too slow. And they are too commoditized. You can take them for a ride.


When it comes to the future, we cannot predict it. But our mathematical models often give us the illusion that we can. Movies are an excellent example. Filmmakers cannot predict what movies are going to be popular. People tend to group around particular movies, and we cannot always predict if a certain movie will be the one that people group around. This type of grouping has almost nothing to do with whether the movie was well-made or not. Same with the arts and books. There are lots of good singers, artists, and writers, but very few of them get large recognition. People tend to group around a particular product. Similarly, when it comes to the past, we do an awful job of remembering it. We only remember specific parts of history, and we tend to forget the parts that do not fit into the narrative we’ve created, and consequently we restructure the past. This is known as the narrative fallacy. The narrative fallacy and confirmation bias work together to cause us to think of the past, and the extreme events of the past, as something predictable. In the moment, however, these events are outliers. That is why a journal is invaluable, because it is written “in the moment” and helps us understand events as they actually occurred.


We cannot predict Black Swans! There are 3 characteristics that NNT uses to identify Black Swans:

  1. Unpredictability

  2. Consequences

  3. Retrospective explainability

We are blind to black swans, because of the following errors:

  1. Confirmation bias: we only focus on the small set of things that we can see and generalize those known things to the unknown

  2. Narrative fallacy: we develop stories that fit the patterns we see

  3. Silent evidence: history tells us that we should have been able to predict the Black Swan

  4. Tunnel vision: we focus on well-defined sources of uncertainty

Black Swans are only Black Swans to the sucker. Just like if you are sitting at a Poker game, and you don’t know who the sucker is, then the sucker is you. With regards to the Thanksgiving turkey, the turkey is the sucker. It is fed well every day, and it lives a stress-free life. Until Thanksgiving. On Thanksgiving, the turkey’s life suddenly ends. The turkey was blind to the Thanksgiving event; from the turkey’s perspective, Thanksgiving was a Black Swan. To the butcher though, Thanksgiving was a predictable and anticipated event. From the butcher’s perspective, there was no Black Swan. The turkey is the sucker. Don’t be the sucker.


There is a difference between jobs that are scalable and those that are not. For example, jobs that pay by the hour are generally not scalable. On the other hand, jobs like scientists and entrepreneurs are jobs that are exposed to lots of unpredictable scenarios, any of which have the potential to be a major breakthrough. A scientist can accidentally discover a cure to the next pandemic, a businessman can start the next Apple, or a developer can create the next Facebook. If you want a big-break, expose yourself to Extremistan careers and Extremistan circumstances. For example, if you are a scientist or engineer, then you have the potential to be part of a ground-breaking, world-changing development. But you need to expose yourself to lots and lots of situations. Attend parties and socialize. The down-side of parties is very small – they take some of your time. However, the upside is massive. Any one of the interactions at a party can lead to finding your next business partner, or can spark your imagination for the next $1B idea. Go to parties, learn to be sociable, and stop thinking like a “nerd,” i.e., exceptionally inside the box. NNT encourages us to stop taking things so seriously. Have some fun. Give a taxi driver a $100 tip just to see his reaction. In an interview, stop answering the questions, and just talk about whatever you’ve been thinking about recently.


I appreciated the large amount of humor in this book. Most of it was targeted towards people in black suits that use Gaussian distributions, engineers, financialists, and others who use complex mathematical models in attempts to predict events that we simply cannot predict. In engineering, combustion modeling is an excellent example. We have thousands of papers dedicated to complex combustion dynamics, yet when it comes to actual combustion in an engine, the only way to fully understand what’s happening is to test it. All the complex mathematical models that attempt to minimize Gibbs free energy, predict inert species, and calculate emissions, do not work. They cannot predict future combustion performance, just like economic forecasts cannot reliably predict the financial future. Realistically, there are too many variables to model.


laudatory: expressing praise


lurid: causing horror or revulsion; shocking


epistemology: the study or theory of knowledge, especially related to its limits and validity


bibulous: fond of alcoholic beverages


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