Fooled By Randomness

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A presentation on Fooled By Randomness- By Nassim Nicholas Taleb

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Fooled By Randomness

  1. 1. THEORY OF DETERMINISM V/S THEORY OF RANDOMNESS Black Swan Event • Something did not happen till now that does not mean that it will not happen in future. • No amount of observations of white swans can allow the inference that all swans are white, but the observations of a single black swan is sufficient to refute that conclusion. • He says that statistics is very good measure to take decisions but highly destructive if used to manage risks and exposures
  2. 2. • • SILENT EVIDENCE Taleb introduces the concept of Alternative Histories. When considering success, you must also consider the likelihood of success given the probability of a negative result having occurred. Failure to consider the potential for negative results and judging based only on the success witnessed is the survivorship bias. We tend to denigrate history by thinking the things that happen to others won’t happen to us. Additionally, most of us carry on without knowing the real odds of our demise, unlike in Russian Roulette. TIMESCALE AND NOISE
  3. 3. SURVIVAL OF THE LEAST FIT – CAN EVOLUTION BE FOOLED BY RANDOMNESS • Here we will see the variety of characteristics seen in the fools of randomness (Acute successful randomness fool) • Don’t overestimate the accuracy of your beliefs. You may not be right every time just because you have been mostly right in the past • Always assess your ideas and make sure it still holds true. Always have a backup plan if things do not go as per planned, else one such event could be catastrophic. Be critical about each and every thing and accept your mistakes as early as possible before they grow bigger
  4. 4. SKEWNESS AND ASYMMETRY • Here we throw light on how median induces asymmetry in thinking and how it can be encountered • The median is not the message • Asymmetric odds means that probability are not 50% for each event, but that the probability on one side is higher than the probability of other. Asymmetric outcomes means that payoffs are not equal • Assume that I engage in a gambling strategy that has 999 chances in 1000 of making $1 and one chance in 1000 of loosing 10000 Event Outcome Expectation A 999/1000 $1 $.999 B 1/1000 $-10000 -$10 Total • Probablity -$9.001 This point is simple and understood by anyone making a simple bet. Yet people in financial markets do not seem to internalize it. People confuse probability and expectation
  5. 5. THE PROBLEM OF INDUCTION • What is the problem of induction? • Generalizing about the properties of a class of objects based on some number of observations of particular instances of that class • Presupposing that a sequence of events in the future will occur as it always has in the past • Here the author again back to his black swan philosophy to corroborate his point about black swans • No amount of observations of white swans can allow the inference that all swans are white, but the observations of a single black swan is sufficient to refute that conclusion. • He says that statistics is very good measure to take decisions but highly destructive if used to manage risks and exposures How do we deal with the problem of induction? Example : the optimal strategy would be to believe in existence of god. If God exists the believer would be rewarded but if he does not exist, the believer would have nothing to lose. If the science of statistics can benefit me in anything, I will use it, if it poses a threat, then I will not. I want to take the best of what past can give me without its dangers. In terms of trade, I will trade on ideas based on some observations, but I will make sure that the cost of being wrong are limited
  6. 6. MANY TIMES, WE SELECT THE WRONG FRAME OF REFERENCE WHILE RELATING OUR SUCCESS TO OTHERS
  7. 7. LOSERS TAKE ALL
  8. 8. RANDOMNESS & OUR MINDS: WE ARE PROBABILITY BLIND Satisfaction Flawed not just imperfect Degree in a fortune cookie Two systems of reasoning We are option Blind Imagination of probabilities: 75% fat free v/s 25% fat?
  9. 9. Trader Name Learned Name Description “I am as good as my last trade.” Prospect theory Looking at differences and not absolutes, and resetting to a specific reference point. “Sound bite effect” or “Fade the fears” Affect heuristic, risk-as-feeling theory People react to concrete and visible risks, not abstract ones “It was so obvious” or “Monday morning quaterback” Hindsight Bias Things appear to be more predictable after fact “You were wrong” Belief in the law of small numbers Inductive fallacies; jumping to general conclusions too quickly Brooklyn smarts/MIT intelligence Two systems of reasoning The working brain is not quite the reasoning one “It will never go there” Overconfidence Risk-taking out of an underestimation of odds
  10. 10. Wax in my Ears: Living with Randomitis “People are emotional even though intelligent enough to understand that they have a predisposition to be fooled by randomness.” Wittgenstein’s Rule

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