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The Black Swan: The Impact of the Highly Improbable

Summary of the Blinkist summary :
(my thoughts are in italic)

The Black Swan explores the nature of what we perceive as random events, as well as the logical pitfalls that cause us to miss out on the bigger picture.

Taleb gives us a better understanding of our own shortcomings when it comes to making predictions. This can help us to recognise when our judgement is clouded by the desire to fit information into neat, easy-to-understand narratives.

Black Swans are events thought to lie outside the realm of possibility and yet happen anyway

For one thing, we’re inclined to be narrow-minded in our beliefs about the world. Once we have an idea about how the world functions, we tend to cling to it. But because human knowledge is constantly growing and evolving, this dogmatic approach makes no sense.

From “Think Again” I learnt that we should define ourselves through values, not beliefs.

Being dogmatic about our beliefs makes us blind to those concepts that fall outside the paradigms we’ve already accepted as true. For example, it’s not possible to understand medicine if you’re not aware that germs exist. So you might come up with a sensible explanation for illness but it’ll be flawed by a lack of crucial information.

This kind of dogmatic thinking can result in huge surprises. We’re sometimes surprised by events not because they’re random, but because our outlook is too narrow. Such surprises are known as “Black Swans” and they can prompt us to fundamentally reconsider our worldview.

Logical fallacies

One bad habit of us humans is creating narratives based on what we know of the past. While we tend to believe that the past is a good indication of the future, this is often a fallacy with potentially dire consequences. It leaves us prone to mistakes because there are simply too many unknown factors that could go against our narratives.

Narrative fallacy

We create linear narratives to describe our current situation. To make sense of the massive amount of information we’re faced with every day, our brains select only that information it considers important. In order to give meaning to those unconnected bits of information, we turn them into a coherent narrative.

However, creating such narratives is a poor way to gain any meaningful understanding of the world. Because the process works only by looking back on the past and doesn’t take into account the near-infinite explanations that are possible for anyone event.

The way that our brains categorise information makes accurate predictions extremely difficult.

Distinguish between non-scalable & scalable information

Non-scalable information such as body weight and height – has a definite statistical upper and lower limit. Therefore, it’s possible for us to make meaningful predictions about averages.

Non-physical or fundamentally abstract things like the distribution of wealth or album sales are scalable. It’s possible for a tiny % of the population to own an incredibly large % of the wealth.

The difference between scalable and non-scalable information is crucial if you want to have an accurate picture of the world.

The same ideas are being applied to tangible and intangible assets in Capitalism Without Capital.

Ludic fallacy

The ludic fallacy is the misuse of games to model real-life situations. We’re far too confident in what we believe we know. We tend to handle risk as we would in a game, with a set of rules and probabilities that we can determine before we play. Yet, treating risk like a game is itself a risky business. No matter how hard we try, we’ll never be able to accurately calculate every risk.

Taking an inventory of what you don’t know will help you assess risk better

By focusing only on what we know, we open ourselves to great and unmeasured risks. On the other than, if you can at least establish what it’s that you don’t know, then you’d be able to greatly reduce your risk.

Do not underestimate our ignorance

Having a good understanding of our limitations as human beings can help us to make better choices

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