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Executive Times |
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2007 Book Reviews |
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The Black
Swan: The Impact of the Highly Improbable by Nassim
Nicholas Taleb |
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Rating: |
**** |
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(Highly Recommended) |
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Click on
title or picture to buy from amazon.com |
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Unknowns Nassim Nicholas Taleb
tells readers of his new book, The Black
Swan, that “a black swan is a highly improbable event with three
principal characteristics: it is unpredictable; it carries a massive impact;
and after the fact, we concoct an explanation that makes it appear less
random and more predictable than it was.” This quirky and thought provoking
book promotes the notion that randomness lives, and blasts those who rely on
data that doesn’t include the impact of the highly improbable, which does
occur. We place too much emphasis on the odds that the past will repeat
itself. He calls the bell curve predictability “Mediocristan”
and the wild gyrations of the world we really live in as “Extremistan.”
Here’s an excerpt, pp. 158-163: The Beauty of Technology: Excel Spreadsheets In the not too distant
past, say the precomputer days, projections remained
vague and qualitative, one had to make a mental effort to keep track of them,
and it was a strain to push scenarios into the future. It took pencils,
erasers, reams of paper, and huge wastebaskets to engage in the activity. Add
to that an accountant’s love for tedious, slow work. The activity of
projecting, in short, was effortful, undesirable, and marred with self-doubt. But things changed with the
intrusion of the spreadsheet. When you put an Excel spreadsheet into
computer-literate hands you get a “sales projection” effortlessly extending
ad infinitum! Once on a page or on a computer screen, or, worse, in a
PowerPoint presentation, the projection takes on a life of its own, losing
its vagueness and abstraction and becoming what philosophers call reified,
invested with concreteness; it takes on a new life as a tangible object. My friend Brian Hinchcliffe suggested the following idea when we were
both sweating at the local gym. Perhaps the ease with which one can project
into the future by dragging cells in these spreadsheet programs is
responsible for the armies of forecasters confidently producing longer-term
forecasts (all the while tunneling on their assumptions). We have become
worse planners than the Soviet Russians thanks to these potent computer
programs given to those who are incapable of handling their knowledge. Like
most commodity traders, Brian is a man of incisive and sometimes brutally
painful realism. A classical mental
mechanism, called anchoring, seems to be at work here. You lower your anxiety
about uncertainty by producing a number, then you
“anchor” on it, like an object to hold on to in the middle of a vacuum. This
anchoring mechanism was discovered by the fathers of the psychology of
uncertainty, Danny Kahneman and Amos Tversky, early in their heuristics and biases project. It
operates as follows. Kahneman and Tversky had their subjects spin a wheel of fortune. The
subjects first looked at the number on the wheel, which they knew was random, then they were
asked to estimate the number of African countries in the United Nations.
Those who had a low number on the wheel estimated a low number of African
nations; those with a high number produced a higher estimate. Similarly, ask someone to
provide you with the last four digits of his social security number. Then ask
him to estimate the number of dentists in We use reference points in
our heads, say sales projections, and start building beliefs around them
because less mental effort is needed to compare an idea to a reference point
than to evaluate it in the absolute (System
1 at work!). We cannot work without a point of reference. So the introduction of a
reference point in the forecaster’s mind will work wonders. This is no
different from a starting point in a bargaining episode: you open with high
number (“I want a million for this house”); the bidder will answer “only
eight-fifty”—the discussion will be determined by that initial level. The Character of Prediction Errors Like many biological
variables, life expectancy is from Mediocristan,
that is, it is subjected to mild randomness. It is not scalable, since the
older we get, the less likely we are to live. In a developed country a
newborn female is expected to die at around 79, according to insurance
tables. When she reaches her 79th birthday, her life expectancy, assuming
that she is in typical health, is another 10 years. At the age of 90, she
should have another 4.7 years to go. At the age of 100, 2.5 years. At the age of 119, if she miraculously lives that
long, she should have about nine months left. As she lives beyond the
expected date of death, the number of additional years to go decreases. This
illustrates the major property of random variables related to the bell
curve. The conditional expectation of additional life drops as a person gets
older. With human projects and
ventures we have another story. These are often scalable, as I said in
Chapter 3. With scalable variables, the ones from Extremistan,
you will witness the exact opposite effect. Let’s say a project is expected
to terminate in 79 days, the same expectation in days as the newborn female
has in years. On the 79th day, if the project is not finished, it will be
expected to take another 25 days to complete. But on the 90th day, if the project
is still not completed, it should have about 58 days to go. On the 100th, it should have 89 days to go. On the
119th, it should have an extra 149 days. On day 600, if the project is not
done, you will be expected to need an extra 1,590 days. As you see, the
longer you wait, the longer you will be expected to wait. Let’s say you are a refugee
waiting for the return to your homeland. Each day that passes you are getting
farther from, not closer to, the day of triumphal return. The same applies to
the completion date of your next opera house. If it was expected to take two
years, and three years later you are asking questions, do not expect the
project to be completed any time soon. If wars last on average six months,
and your conflict has been going on for two years, expect another few years
of problems. The Arab-Israeli conflict is sixty years old,
and counting—yet it was considered “a simple problem” sixty years ago.
(Always remember that, in a modern environment, wars last longer and kill
more people than is typically planned.) Another example: Say that you send
your favorite author a letter, knowing that he is busy and has
a two-week turnaround. If three weeks later your mailbox is still empty, do
not expect the letter to come tomorrow—it will take on average another three
weeks. If three months later you still have nothing, you will have to expect
to wait another year. Each day will bring you closer to your death but
further from the receipt of the letter. This subtle but extremely
consequential property of scalable randomness is unusually counterintuitive.
We misunderstand the logic of large deviations from the norm. I will get deeper into
these properties of scalable randomness in Part Three. But let us say for now
that they are central to our misunderstanding of the business of prediction. DON’T CROSS A RIVER
IF IT IS (ON AVERAGE) FOUR FEET DEEP Corporate and government
projections have an additional easy-to-spot flaw: they do not attach a possible error rate to their
scenarios. Even in the absence of Black Swans this omission would be a
mistake. I once gave a talk to
policy wonks at the The attendees were tame and
silent. What I was telling them was against everything they believed and
stood for; I had gotten carried away with my aggressive message, but they
looked thoughtful, compared to the testosterone-charged characters one
encounters in business. I felt guilty for my aggressive stance. Few asked
questions. The person who organized the talk and invited me must have been
pulling a joke on his colleagues. I was like an aggressive atheist making his
case in front of a synod of cardinals, while dispensing with the usual
formulaic euphemisms. Yet some members of the
audience were sympathetic to the message. One anonymous person (he is
employed by a governmental agency) explained to me privately after the talk
that in January 2004 his department was forecasting the price of oil for
twenty-five years later at $27 a
barrel, slightly higher than what it was at the time. Six months later,
around June 2004, after oil doubled in price, they had to revise their
estimate to $54 (the price of oil
is currently, as I am writing these lines, close to $79 a barrel). It did not
dawn on them that it was ludicrous to forecast a second time given that
their forecast was off so early and so markedly, that this business of
forecasting had to be somehow questioned. And they were looking twenty-five years ahead! Nor did it
hit them that there was something called an error rate to take into account. * Forecasting without
incorporating an error rate uncovers three fallacies, all arising from the
same misconception about the nature of uncertainty. The first fallacy: variability matters. The first error
lies in taking a projection too seriously, without heeding its accuracy. Yet,
for planning purposes, the accuracy in your forecast matters far more the
forecast itself. I will explain it as follows. Don’t cross a river if it is four feet deep on
average. You
would take a different set of clothes on your trip to some remote destination
if I told you that the temperature was expected to be seventy degrees
Fahrenheit, with an expected error rate of forty degrees than if I told you
that my margin of error was only five degrees. The policies we need to make
decisions on should depend far more on the range of possible outcomes than on
the expected final number. I have seen, while working for a bank, how people
project cash flows for companies without wrapping them in the thinnest layer
of uncertainty. Go to the stockbroker and check on what method they use to
forecast sales ten years ahead to “calibrate” their valuation models. Go find
out how analysts forecast government deficits. Go to a bank or
security-analysis training program and see how they teach trainees to make
assumptions; they do not teach you to build an error rate around those
assumptions—but their error rate is so large that it is far more significant
than the projection itself! The second fallacy lies in
failing to take into account forecast degradation as the projected period
lengthens. We do not realize the full extent of the difference between near
and far futures. Yet the degradation in such forecasting through time becomes
evident through simple introspective examination—without even recourse to
scientific papers, which on this topic are suspiciously rare. Consider
forecasts, whether economic or technological, made in 1905 for the following
quarter of a century. How close to the projections did 1925 turn out to be? For a convincing experience, go read George
Orwell’s 1984. Or look at more
recent forecasts made in 1975 about the prospects for the new millennium.
Many events have taken place and new technologies have appeared that lay
outside the forecasters’ imaginations; many more that were expected to take
place or appear did not do so. Our forecast errors have traditionally been
enormous, and there may be no reasons for us to believe that we are suddenly
in a more privileged position to see into the future compared to our blind
predecessors. Forecasting by bureaucrats tends to be used for anxiety relief
rather than for adequate policy making. The third fallacy, and
perhaps the gravest, concerns a misunderstanding of the random character of
the variables being forecast. Owing to the Black Swan, these variables can
accommodate far more optimistic—or far more pessimistic—scenarios than are
currently expected. Recall from my experiment with Dan Goldstein testing the
domain-specificity of our intuitions, how we tend to make no mistakes in Mediocristan, but make large ones in Extremistan
as we do not realize the consequences of the rare event. What is the implication
here? Even if you agree with a given forecast, you have to worry about the
real possibility of significant divergence from it. These divergences may be
welcomed by a speculator who does not depend on steady income; a retiree,
however, with set risk attributes cannot afford such gyrations. I would go even
further and, using the argument about the depth of the river, state that it
is the lower bound of estimates (i.e., the worst case) that matters when
engaging in a policy—the worst case is far more consequential than the
forecast itself. This is particularly true if the bad scenario is not
acceptable. Yet the current phraseology makes no allowance for that. None. It is often said that
“is wise he who can see things coming.” Perhaps the wise one is the one who
knows that he cannot see things far away. Get Another Job The two typical replies I
face when I question forecasters’ business are: “What should he do? Do you
have a better way for us to predict?” and “If you’re so smart, show me your
own prediction.” In fact, the latter question, usually boastfully presented,
aims to show the superiority of the practitioner and “doer” over the
philosopher, and mostly comes from people who do not know that I was a
trader. If there is one advantage of having been in the daily practice of
uncertainty, it is that one does not have to take any crap from bureaucrats. One of my clients asked for
my predictions. When I told him I had none, he was offended and decided to
dispense with my services. There is in fact a routine, unintrospective
habit of making businesses answer questionnaires and fill out paragraphs
showing their “outlooks.” I have never had an outlook and have never made
professional predictions—but at least I
know that I cannot forecast and a small number of people (those I care
about) take that as an asset. There are those people who
produce forecasts uncritically. When asked why they forecast, they answer,
“Well, that’s what we’re paid to do here.” My suggestion: get another
job. This suggestion is not too
demanding: unless you are a slave, I assume you have some amount of control
over your job selection. Otherwise this becomes a problem of ethics, and a
grave one at that. People who are trapped in their jobs who forecast simply
because “that’s my job,” knowing pretty well that their forecast is
ineffectual, are not what I would call ethical. What they do is no different
from repeating lies simply because “it’s my job.” Anyone who causes harm by
forecasting should be treated as either a fool or a liar. Some forecasters
cause more damage to society than criminals. Please, don’t drive a school
bus blindfolded. * While
forecast errors have always been entertaining, commodity prices have been a
great trap for suckers. Consider this 1970 forecast by Also note
this additional aberration: since high oil prices are marking up their
inventories, oil companies are making record bucks and oil executives are
getting huge bonuses because “they did a good job”—as if they brought profits
by causing the rise of oil prices. The Black
Swan is an unusual book by an unusual author. Odds are that reading it
will increase your appreciation of what you don’t and can’t know, and what a
big impact that has on your life. Steve Hopkins,
July 25, 2007 |
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The recommendation rating for
this book appeared in the August 2007
issue of Executive Times URL for this review: http://www.hopkinsandcompany.com/Books/The
Black Swan.htm For Reprint Permission,
Contact: Hopkins & Company, LLC • E-mail: books@hopkinsandcompany.com |
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