FooledbyrandomnessI came across this book while reading Malcolm Gladwell’s seminal piece on behavioral economics. Since then I had always been wanting to read it. My travel , a fortnight ago gave me sometime for myself and hence was able to read this book by Nassim Taleb.

This books is all about, as the title suggests, randomness, It’s manifestations in our lives, the way we approach/escape it, and the consequences of the same. Here is what I have learnt from the book :

Who are fooled by randomness ?
There are a lot of us who make the mistake of thinking in probabilities of events alone , and not looking at the expected outcome of the event. The author makes a strong point that we must start thinking from a payoff point of view rather that toying with mere probabilities. There are umpteen number of cases presented in the book, which gives a reader a chance to profile the nature of the person who is fooled by randomness. The following are some of the behaviors of such a person

  • Overestimation of beliefs in some measure,   either economic or statistical
  • Using a rigorous economic analyses to   trade
  • Tendency to get married to positions
  • Tendency to change their story
  • No precise game plan ahead of time as to what to   do in the event of losses
  • Absence of critical thinking expressed in   absence of revision of their stance with stop losses
  • Denial

One needs to be appreciative of the fact that success / failure is dependent on path outcome. It is more often that luck decides the success rather than anything else!

Skewness and Asymmetry:
Most of our understanding of the world is that payoffs of various events are same. Hence we are attuned to think in terms of odds of a certain event  , rather than expected payoff of an event. This is particularly problematic when one looks at asymmetric distributions. Time series events relies solely on historical data and hence does not tell us anything about the rare event that would hit us in the future. In that sense, historical data should be used to detect those events that have not happened in the past and then factor those events in the future calculations.  Rare events analysis makes sense when there is a cumulative effect to the things one is talking of. For example , Money management ,Research , yield of which is definitely a rare event,Startup until it crosses the chasm, etc

Statisticians for this precise reason cannot detect rare events as their historical data by definition tries to remove outliers, and does not have any outliers which makes an event rare event.

Robert Lucas dealt a blow to econometrics by saying:
If people were rational, then their rationality would cause them to figure out predictable patterns from the past and adapt so that past information would be completely useless for predicting the future

The problem with Induction:
The following statement summarizes the problem with induction:
" I have just completed a thorough statistical examination of the life of Vajpayee. For 80 years close to 21000 observations, he did not die once. I can hence pronounce him as immortal with a high degree of statistical significance"
In this aspect, one comes across a lot of theories in our lives and unless we think critically about them, we will be carried away by them . A theory that does not present a set of conditions under which it world be considered wrong would be termed charlatanism – it would be impossible to reject otherwise. There are essentially two types of theories, firstly Theories that are known to be wrong , as they were tested and adequately rejected AND secondly , Theories that have not yet been known to be wrong, not falsified yet , but are exposed to be proved wrong.

Are we probability blind ?
There are a lot of  situations when we become blind to the basic principles of probability. When we peruse the list of millionaires list, top 10 lists etc, it appears that the attributes which the journalist seems to bring out are THE ATTRIBUTES of success. Never does it strike us, that it might be because of pure luck that the person has become a millionaire. In all articles where we see generalizations being made my journalists, we must be extra careful about few aspects. They are:

     

  • Article inevitably suffers from survivorship   bias- Winners appear and Losers disappear. Is there some sampling correction   carried out ?
  • What is the initial sample size of the   population , from which someone is deriving a conclusion. If the sample size   is small, then there could be some cause of belief, YES Belief…This goes   against the principle of statistics which says that by doubling the size of   sample, one increases the probability confidence by 1.414 times. An example   would prove a point. If the initial sample size is very large , lets say   100000 monkeys sit on a type writer and start typing something and at the end   100 monkeys come up with a novel, out of which 10 become national best   sellers, and one becomes an international best seller. If one were to look at   100 monkeys who at the first place are visible and draw conclusions about the   monkeys writing abilities, then there is great bias in the way we look at   things. In the above case the sample size in the beginning was 100000 was too   high that by a mere chance 100 monkeys could have produced some comprehensible   stuff..However on the other hand, if there are 100 monkeys to begin with and   10 produces some comprehensible stuff, then there is case for monkey and   typewriter business.
  • Most of us confuse between absence of evidence   and evidence of absence.
  • Reference anchoring :We tend to feel good / bad   based on the last reference point in our mind. It is not necessary that 2X in   our bank would make us more happy than X in our bank.Also people might have 2   different anchor points for win and lose situation. In our lives, we have to   take in to consideration about reference anchoring to understand various   phenomenon
  • It is very important to understand the   difference between joint and conditional probability, If not its easy for   anybody to take you for a ride with specious arguments
  • Too much of importance is given to reason part   of a human that one over looks the abundant research which seems to say   emotions which become the lubricants of reason drive the decision   process

Monte-Carlo Simulation:
One of the biggest takeaways from the book is the importance of playing with Monte-Carlo generator so as to come to terms with the alternate paths which world would have taken , which we cannot see them. We can only simulate them and then be prepared for the rare event. One has to read the book to understand Monte-Carlo simulation and how simple, though powerful playing with it can help an investor / trader than pouring in to Newspaper, Media CNBC, Mobile alerts which only add to the noise.

Over all, a thoroughly engaging book for anybody and it should not be missed by any student of finance.

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