This document discusses low probability, high impact events that are difficult to predict due to complexity but can have severe consequences. It advocates focusing on reducing vulnerability to rare events through measures like insurance, and acknowledging that historical data may fail to predict novel phenomena. Standard statistical models based on past distributions are susceptible to surprises from destructive outliers. Large losses can potentially occur from socioeconomic or statistical drivers like hostile policies or departures from textbooks examples.

1.  Low Probability –
High Impact Events
are impossible to
forecast
 Complexity
increases the
likelihood of a rare
event. The aim
reduce our
vulnerability to them

2.  Historical such
predictions constantly
fail. “That will never
happen to us”
 Focus instead on the
consequences of a rare
event
 One remedy buy
insurance

3.  Economics changes constantly – they provide a
frail basis for generalizations at times
 A system based on a standard frequency
distribution of the past can be subject to surprise
and encounter volatility
 For example great empires can decline suddenly.
Destructive outliers eventually happen – a war, a
famine, a depression, a destructive revolution

4.  Facts are at times rude. Facts change slowly
and other times rapidly.
 When the facts change, I change.
 It does not mean my core being of being
trustworthy, loving, fair, and responsible
changes. That remains unchanged by
circumstances.

5. Past events do not bear any
resemblance to future shocks
History tries to give certainty but
hides the never seen before
phenomena. It is hard to predict the
magnitude or length of duration of an
event. History can fool us.
Randomness
1) Socioeconomic: Example:
Hostile unseen governmental or
legal contingencies - competition
2) Statistical driven: Example
textbook examples

6. A dollar not lost is a dollar
earned.
The allurement to earn a profit
outranks preventive action to
avoid a loss.
The better aim is to focus on
avoiding errors rather than
winning. It permits survival.
Not losing is a victory too and
should be viewed equally as
profiting is a victory.
No one should have a piece of
the upside of winning without
a strategy to minimize losing
in a downside.
Large exposure to risky mortgages in 2008
almost caused the top ten banks to collapse.

7. In 2008 the banks nearly imploded due to the mortgage
debt crisis. The Secretary of the Treasury literally
begged and got a $700 billion dollar bail out of the
banks to avoid a Second Great Depression. The stock
market tumbled to record lows for the modern era.

8.  Two or more parties are able to make choices.
 Uncertainty exists in what will the other person do?
 Taking the greatest risk/reward strategy provokes the
greatest resistance of another player if they lose in the
end
 We settle for a compromise. Maximin or Minimax.
 Per Neumann try not to guess so much what the
opponent will do but in not revealing one’s own
intentions. The object is not to win but avoid
losing.

9. Standard Deviation used in finance
should not be used in risk
management.
99% of financial phenomena occurs
within 3 standard deviations. Yet
should an outlier appear it causes
the assume model to crash.
 The human mind observes and
assume a reasonable normal with
modest variation in life.
 It forgets about unguarded
uninsured unexpected moments.
 Decisions get based on their
implications. Caution– it assume
linearity
 Outliers exist.
▪ Positive: an original Google employee
▪ Negative: Being on the Titanic
 People who anticipate outliers and
understand their implicated
consequences can have dramatic
rewards if they act
100-year flood!
Bike hits road debris!

10. Long Term Capital Management 1998 MF Global 2011

11.  How is a question framed? It
influences a person’s
perception. Same statistic can
be framed:
▪ Chance a plane would crash
once in a 1000 years.
▪ Chance one in a 1000
flights known to crash
sounds riskier.
 Be careful how a question
is framed
 People tend to be risk adverse in
dealing with gains. They are shy
about seizing opportunities.
 People tend to be risk seeking in
deciding about losses or threats. Take
a risk to avoid a great or greater loss.
 People maintain a reference point far
longer than they should. People keep
a sunk in cost mentality. Keep
sinking money into an idea or effort
long after it being effective.
 Achieving zero risk is impractical to
impossible.

12.  Chaos is the product of an underlying order.
Insignificant perturbations are often the cause
of predestined crashes and long lived rise.
 Butterfly Effect - the sensitive dependence on
initial conditions, where a small change at one
place in a deterministic nonlinear system can
result in large differences to a later state –
Example Gompertz curve that predicts
epidemics

13. Gompertz Model Logistic Curve Model
Such forecast models are helpful when one
is on the verge of a tipping point

14.  An inherent danger to
optimizing makes one more
vulnerable
 Mother nature has backup
systems such as two eyes,
ears, lungs, etc. Redundancy
is essential to survival.
 A business views a backup
system as inefficient use of
funds. Redundancy is
avoided.
 Leverage gives greater
prospect of gain but risks
collapsing under a debt load.
 High salaries make managers
hide risks. There is no drawback
if they are taking short term
profits to justify their income that
will one day create long term
losses.
 CEO’s become rich and the
shareholders earn nothing or lose
in the end.
 Our biggest risk lies within us.
We over estimate our abilities
and under estimate what can go
wrong.

15. Nature’s Backup System if one Fails the Other Takes Over
Business Models Say Redundancy is Costly so Avoid It to be Efficient