My Notes
Attributes of a Black Swan
- It is an extreme outlier that we cannot predict
- It has a large impact
- We create explanations for it after it happened, even though we had (and have) no capability to predict the Black Swan
Risk assessments often exclude the presence of Black Swans and thus become faulty
A hot take: Classical economists interpret progress all wrong, incentives don’t create change. Free markets function well because they give people more opportunities to be lucky have experience a positive Black Swan event e.g. penicillin.
There are no social incentives for great problem avoiders, so we have a distinct lack of them.
How humans misinterpret history:
- You think you understand things when they are really more complicated
- Assessing events after the fact and attributing causes to those past events
- Experts who sort events into categories that fall within nicely constructed theories
Avoid the scalable, they are incredibly good ventures when successful, but far more random as to who and what will succeed.
We don’t accept art only for its own sake, but also to feel as though we are part of a community.
Black Swans occur relative to your expectations, so an open mind is one way to alleviate some hidden Black Swans
Narrative fallacy: the preference for stories and overinterpretations instead of truths. Stories can only be so complex, they do not approximate the actual randomness of the world.
The causes you want to evaluate and use as knowledge are the ones that can be tested by experiment.
Happiness depends more on the number of times you have positive feelings than the intensity of those feelings. The reverse is true of unhappiness, so lump pain into one sitting.
We associate chance with controlled games in casinos when in reality most risks are not computable with clear probabilities.
Fields that try to make predictions based on past events run into Black Swans because they do not consider the “unprecedented” in models.
Economists expect people to act “rationally” but their idea of rationality has only one answer and is often at odds with reality.
Stuff like life expectancy and height have averages and distributions we can predict. Our statistical models work for these types of data, but fail with events that have massive, hard to predict ranges like stock prices.
We fail to predict the future because:
- We are ignorant to unprecedented future events
- We trust simplified versions of more complex truths
- Inference tools are designed for predictable information like height or life expectancy, not in areas with vast outliers or massive scaling.
Gaussian bell curves make probabilities of outliers fall at an exponential rate, while Mandelbrotian mathematics do not have this restriction.
Guassian statistics do not have realistic assumptions so models built upon it fail.
Ricardo’s vision of trade, where each country produces what is it most efficient at measured by the value of the product in dollars, collapses when we consider that prices can vary quite wildly e.g. oil. So why should a country specialize at the mercy of price fluctuations?
Iatrogenics: they study of harm done by the healer.
Tags: Black Swan Taleb