1. THE BLACK SWAN

2. A Black Swan is… … Something you didn’t expect which has a strong impact. (So the more you think you know the more vulnerable you are because your confidence in predicting the likelihood of a given event will make you more vulnerable in the case where you don’t)

3. Mediocristan In Mediocristan everything is constrained by boundary conditions: time, the limits of biological variation, the limits of hourly compensation, etc. Random variation of attributes exists in Mediocristan, and can be usefully described by Gaussian probability models (e.g. the bell curve) Because overall constraints are in effect for all occurrences no single data point will have any great effect on the mean or average of the whole. Examples: height of people, calories eaten per day, wages earned by cab drivers.

4. Extremistan Extremistan is the land of scalability: variation within distributions is unconstrained and unpredictable. Generators of events produce distributions with very large or very small extreme values, relatively frequently. And those extreme values often affect the sum of attribute values in a sample distribution + the mean value of such distributions. The probability of occurrence of extreme values varies greatly from Gaussian models. In fact, many attribute value distributions in Extremistan do not fit any known models well. Examples: booksales, wealth, website hits. Since extreme occurrences can greatly affect statistical properties of distributions from Extremistan, it is hard to make reliable inferences from sample data.

5. Two examples Sample 100.000 Mexicans: Height (Mediocristan): most extreme occurrence will move the average only 0.001% Wealth (Extremistan): most extreme occurrence will move the average 467% Getting Carlos Slim in your sample is a 3-4 sigma event (less than 0.1% chance), but it will blow your model of ‘mexican wealth’ wide open.

6. The Barbell Approach Extreme conservatism + extreme risk taking. Taleb makes money

7. … if you can’t barbell: 1.Have respect for time and nondemonstrative knowledge 2.Avoid optimization: learn to love redundancy 3.Avoid prediction of small-probability payoffs 4.Beware the ‘atypicality’ of remote events 5.Beware moral hazard with bonus payments 6.Avoid some risk metrics 7.Positive or negative black swans? 8.Do not confuse absence of volatility with absence of risk 9.Beware presentations of risk numbers