We have
spent a couple of posts looking at the problem of the Black Swan – the impact
of the highly improbable. We have discussed the turkey and the Thanksgiving feast
in part 1 of this set. One doesn’t want to be a surprised turkey. We have
looked at the problem of induction or inductive knowledge which includes how
can we logically go from specific instances to reach general conclusions.
According to Nassim Taleb there are traps built into any kind of knowledge
gained from observation. He also suggests that those who believe in the
unconditional benefits of past experience cannot project into the future. Remember
back to our turkey example, was the farmer surprised by the outcome?
Having
made the above observation, I will say there are some situations where we can
use past experiences to draw conclusions about future observations. Suppose you
are asked to find the average height of the all men in the United States. You
could take a sample of say 10,000 men and draw some fairly detailed
conclusions. You would be able to take that data and make some conclusions
regarding height of men in general. Now if you were to measure one more man
from the population in general your estimate of his height would likely be
within the parameters you had established. The same would be true for weight.
Another way to state this is, one more observation will not significantly
impact the predicted results. Now suppose you sample people who are worth more
than $10 million. Let’s suppose that your sample does not include Bill Gates.
Your sample size may be quite large, relatively speaking, but if it doesn’t
include Bill Gates then as soon as Bill Gates is included in the analysis we
have a Black Swan situation. A deviation so large that it blows up the study.
So, you decide that Bill Gates must be an aberration… (sorry, let me be
specific, in the data only), and toss that data point out. Then the next data
point you sample includes Steve Job’s estate. The difference between sampling
height and let’s say millionaires is one set of data follows a bell curve
(predictable or regular) or a variation of such things and one doesn’t. Many
who have studied this feel that economics and social sciences cannot be defined
by bell curves and its like. Many in econometrics feel that the world can be
defined by regular occurring events or activities. Taleb suggests that “a nerd
is someone who thinks exceedingly inside the box” or is blind to Black Swans.
Some
other themes arise from our blindness to the Black Swan. Again I turn to Taleb
and his book, The Black Swan the Impact
of the Highly Improbable. He suggests the following ideas. We tend to focus
on preselected segments of the seen and generalize from it to the unseen. We
fool ourselves with stories that cater to our thirst for distinct patterns. We
behave as if the Black Swan does not exist, human nature is not programmed for
Black Swans. What we see is not necessarily all that is there. History hides
Black Swans from us and gives us a mistaken idea about the odds of these
events. We “tunnel”, that is we focus on a few well-defined sources of
uncertainty at the expense of the others that do not easily come to mind.
As you listen to the news and look at
economic projections remember that economists and their associates are working
in a world full of Black Swans or full of Bill Gates type situations. It is
important to realize that studies done on how successful economists have been
in their predictions have shown economists have an exceptionally poor track
record of being able to predict things. Just because an economist is able to
show past data to support his position may have little or no relationship to
future events. It all depends on where the Black Swans are.
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