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The book Black Swan counters fallacies like this reasoning. 1. Since his example (the fall of the Berlin Wall) is a one time event without any historical data
by caseysoftware 9y ago
The book Black Swan counters fallacies like this reasoning.
1. Since his example (the fall of the Berlin Wall) is a one time event without any historical data and can only happen once, it's glorified guessing.
2. He ignores that a bell curve is specifically for results that have very predictable ranges. In Black Swan, Taleb calls this "Mediocristan" because the range of height of adult human males holds roughly to a predictable range aka we don't run into men who are 6 inches or 10 feet tall.
3. By taking this once in a lifetime event and applying a bell curve to it - as if the existence Berlin Wall was a gaussian event - he's trying to make guessing look like science.
4. After declaring it "proven", he goes on to compare it to another one time event.. then end of human life.
If he was talking about a regular event with historical data, this may be useful analysis but as is.. oh, he's selling a book. Got it.
- tgb 9y agoOn the contrary, the article never mentioned bell curve, gaussian, or normal distribution. You're projecting your own assumptions onto it. The reasoning may be suspect, but it makes no assumptions about distributions and you've misunderstood it entirely.
- aub3bhat 9y agoErr no you didn't get it. Actually it does use non long tail distribution see the whole 95% figure etc. Those are assumptions about the distributions.
- tgb 9y agoNo, it makes no assumptions about any distribution whatsoever. This is like Chebyshev's inequality: https://en.m.wikipedia.org/wiki/Chebyshev%27s_inequality https://en.m.wikipedia.org/wiki/Chebyshev%27s_inequality Edit: it does make one assumption, namely that there will be a finite number of humans ever.
- caseysoftware 9y agoFrom that wikipedia page: > "Because it can be applied to completely arbitrary distributions provided they have a known finite mean and variance, the inequality generally gives a poor bound compared to what might be deduced if more aspects are known about the distribution involved." In the situations cited in the article, we are dealing with both an unknown mean and an unknown variance and therefore an unknowable distribution. So while Chebyshev is "weaker" than a Gaussian distribution, it's still a distribution.
- tgb 9y agoSorry, I was not saying that the article applied Chebyshev's inequality. It's not related at all. But it's an example of a result where you do not have to assume that your distribution is a member of a particular family of distributions. The result being applied here is really really general: it says that 95% of the probability mass occurs before the point at which 95% of the probability mass has occurred by. This is tautological. Like saying that 50% of people are below the median - it's true without making any assumptions about people (except that there are finite numbers of them).