Executive Times






2007 Book Reviews


The Black Swan: The Impact of the Highly Improbable by Nassim Nicholas Taleb




(Highly Recommended)




Click on title or picture to buy from amazon.com






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 Manhattan. You will find that by making him aware of the four-digit number, you elicit an estimate that is correlated with it.

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.




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 Woodrow Wilson Center in Washington, D.C., challenging them to be aware of our weaknesses in see­ing ahead.

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 U.S. officials (signed by the U.S. Secretaries of the Treasury, State, Interior and Defense): “the standard price of foreign crude oil by 1980 may well decline and will in any event not experience a substantial increase.” Oil prices went up tenfold by 1980. I just wonder if cur­rent forecasters lack in intellectual curiosity or if they are intentionally ignoring forecast errors.

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



Buy The Black Swan

@ amazon.com

Go To Hopkins & Company Homepage



Go to 2007 Book Shelf

Go to Executive Times Archives


Go to The Big Book Shelf: All Reviews





*    2007 Hopkins and Company, LLC


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 • 723 North Kenilworth AvenueOak Park, IL 60302
Phone: 708-466-4650 • Fax: 708-386-8687

E-mail: books@hopkinsandcompany.com