Nassim discusses “how ‘evidence’ about the risk of terrorism as shown in the NYT and by BS vending journos makes no sense statistically” and “explains the difference between classes using principles of Extreme Value Theory EVT without any math.”
This tutorial presents the intuitions of the randomness of sample correlation (spurious correlation) and the methodologies in derivations.
Some later sections are somewhat technical as rederived an old equation with more precise functions (in order to apply to fat tails) and showed the distribution of the maximum of d variables with n points per variable.
This paves the way to the real scientific work on random matric theory under fat tails and failure of Marchenko-Pastur.
In this video, Nassim provides a general presentation of his research, in which he debunks Pinker’s faulty statistics in his book on violence (The Better Angels of Our Nature: Why Violence Has Declined).
Found on Nassim’s facebook page.
Thanks to QuantLabs.net for the link.
Quickly recorded. You do not decrease tail risk by increasing benefits, you decrease tail risk by decreasing tail risk.
What can we learn from Mr Dawkins’ errors and misuse of probability?
“Micro-Mooc on a paper by Taleb and Tetlock (one manifestation of the LUDIC FALLACY). There are serious statistical differences between predictions, bets, and exposures that have a yes/no type of payoff, the “binaries”, and those that have varying payoffs, which we call the “vanilla”. Real world exposures tend to belong to the vanilla category, and are poorly captured by binaries. Yet much of the economics and decision making literature confuses the two. Vanilla exposures are sensitive to Black Swan effects, model errors, and prediction problems, while the binaries are largely immune to them. The binaries are mathematically tractable, while the vanilla are much less so. Hedging vanilla exposures with binary bets can be disastrous–and because of the human tendency to engage in attribute substitution when confronted by difficult questions,decision-makers and researchers often confuse the vanilla for the binary.”
The paper is here: http://papers.ssrn.com/sol3/papers.cfm?abstract_id=2284964
More general Fat Problems with Tails: http://www.fooledbyrandomness.com/FatTails.html
Micro Mooc #3. The law of large numbers is the most important thing in life and science. It is the basis of epistemology and problem of induction. How many observations do you need to know if something is true? We get into the plumbing and show how it is too slow under fat tails. This is a simplified (but technical) presentation of a segment of “Probability and Risk in the Real World”, the Technical Companion for The Black Swan http://www.fooledbyrandomness.com/FatTails.html
A quick tutorial introducing the idea of Fat Tails in about 8 minutes, as simply as possible. Fat Tails make us understand the world much less than we think; they make much of the quantitative results in social “science” anecdotal (or worse, negative information).
The main (more complicated) text is at http://www.fooledbyrandomness.com/FatTails.html