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1-Page Summary of The Black Swan
The Black Swan by Nassim Nicholas Taleb is a philosophical treatment of his research into highly improbable, high-impact events. Such events are so improbable that they’re unpredictable, but people often try to fit them into a causal narrative after the fact in order to make history appear more organized than it really is.
In fact, predictive models rely on data from the past and that data can bias models against unforeseeable events. These Black Swans cannot be predicted by such models. For example, wealth or creative work distributions are not normally distributed because they include huge outliers like Black Swans. A fractal model is a better option for these types of data sets because it includes the inequality of specific subsets of that data without those extreme observations.
Professionals who study extreme events often have trouble predicting them. They prefer to invent a narrative that makes the unpredictable seem predictable in retrospect, but they can’t predict it because of complexities and externalities. The human mind also has difficulty comprehending randomness versus chaos, which is due entirely to chance or might obey an unknown organizing principle.
The best way to withstand a Black Swan event is to avoid taking unnecessary risks, as well as balancing risk by diversifying one’s portfolio. It’s also important to be humble about what you know and understand that predictions aren’t always accurate.
A Black Swan is an extremely rare event that happens beyond what we would normally expect. It’s so uncommon that it can’t be predicted with the normal bell curve distribution and as a result, people tend to think of reasons for why these events happen after they occur. The normal bell curve distribution doesn’t account for extreme outliers and therefore will shift if one occurs. These outliers are usually due to pure chance but because their circumstances are unique, they often become even larger than when they started out.
The mind creates an illusion of understanding complex things by making them seem orderly and predictable. This is called Platonifying, after the Greek philosopher Plato.
Predictability is relative to knowledge. Complex systems are perceived as random due to a lack of available information. People tend to simplify the cause of Black Swans (unexpected, rare events that have a big impact) after they happen and forget about all the factors that actually caused them. This can distort predictive models because it doesn’t take into account current threats faced by companies or communities, or serendipitous discoveries that can be just as important as those predicted in advance.
Most people are overconfident about the things they know. Experts in a changing field tend to be even more so, as they have access to more information and less likely to change their minds based on that information.
If you want to be prepared for a Black Swan event, it’s important to realize that you can’t predict the future. You should make decisions based on what information is available and not rely on long-term predictions. It’s also helpful to buy insurance or have redundant systems in place so that if something bad happens, your company won’t go under as quickly.
The normal bell curve is used for data sets that have no relation to each other and a fixed interval between observations. However, very few real-life situations are like this, so the normal bell curve isn’t applicable in most cases.
Black Swans are unpredictable events that can change the course of history. They’re very rare, but when they do happen, their impact is usually huge. The best way to explain Black Swan theory is by using a fractal model. It looks like it follows a simple pattern on first glance, but there’s actually much more variation in the data than you’d think at first glance.