A presentation on Fooled By Randomness- By Nassim Nicholas Taleb

4. 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

5. •
•
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

6. 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

7. 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

8. 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

10. MANY TIMES, WE SELECT THE WRONG FRAME OF
REFERENCE WHILE RELATING OUR SUCCESS TO
OTHERS

12. LOSERS TAKE ALL

14. 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?

15. 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

16. 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