Opportunities and threats in improbable events

I loved Nassim Nicholas Taleb’s earlier book Fooled by Randomness, and I am really loving The Black Swan as well. It has much of the charm of Malcolm Gladwell’s current NYT best seller, Outliers. — But while Gladwell leaves us with merely the understanding that the success of various prominent individuals such as Bill Gates or Steve Jobs is due largely to the “luck” of being born in the right place and at the right time, he gives us no greater insight into how a better understanding of luck (in the form of randomness and improbability forces at work upon us) might better guide us in choosing national and international policies or even actively choosing our life paths taking into consideration the improbable events such as birth date and birth place work that for our success as well as those that against it.

In contrast, Taleb’s The Black Swan sets its sights on providing us a theory and method to make such decisions in a way that better recognizes the role of randomness in our lives. Even more, it also shows us that any theory which simplifies real world complexity enough for us to see patterns also inherently blinds us to the risks hidden in the messy real world complexity which we have now simplified out.

Gladwell tells us that random chance defines our opportunities and the threats we will face; Taleb tells us how to seize the random opportunities that might occur in our lifetimes, and how to prepare for the improbable but catastrophic events that surely will happen someday and often change our lives in unimaginable ways.

To fully appreciate the Black Swan, it is useful to see how it builds upon insights set out in Taleb’s previous book, Fooled by Randomness.

Fooled by Randomness highlighted the failures of Wall Street market makers who mistook the correctness of their string of predictions as a sign of their own genius, rather than just random chance — at least until the financial markets changed and suddenly their predictions were consistently wrong and their were suddenly hoisted on their on petards. In Fooled by Randomness, Taleb points out an old investment scam where an investment advisor sends out 2^12 (4096) free investment newsletters predicting that the market will be up next month, and sends out another 2^12 free newsletters to other people predicting that the market will be down next month. Let’s say the market is down. Now the broker removes all the people he sent the (wrong) up prediction from his list. But he takes the four thousand plus people to whom he sent the (correct) down prediction newsletters into two parts and he send half another down prediction, and the other half an up prediction. A month later the market is up — and over two thousand people have seen this investment advisor make the right pick twice. So now he divides that group that got the two right picks into two halves and sends 1024 people an up prediction and the other 1024 the down prediction. Every month he drops off the half of his mailing list that got the bad pick that month , but the remaining half has seen him make one right pick after another! At the end of a year he has one newsletter recipient who has seen him make 12 correct picks in a row. That investors knows the probability of 12 correct picks in a row is very low (1 / 2^12) so they conclude that it isn’t chance alone that makes him a great stock picker — they conclude he is a genius, so they invest a bunch of money with him, and tell all their friends to as well.

While the sham newsletter is a well known scam, in Fooled by Randomness, Taleb points out it doesn’t require such an actively deceptive strategy to make 12 correct guesses in a row. Given enough investment advisors independently guessing each month’s market direction at random, say some number greater than 8096, one or two will surely make 12 lucky guesses in a row. And in fact, when we say they make their predictions randomly, we don’t mean that they don’t have a method — maybe they read tea leaves, counted sun spots or compared the month’s average temperature in Jackson Hole, Wyoming versus than the average for the month over the last 20 years. As long as these methods are independent, distributed widely, and uncorrelated with the actual factors that drive the market, there will still be a few lucky winners who will get it right. But now their results will be shown in Morningstar or Consumer Reports, and people will think that given their past track record was so good, so will their future be equally good! But because actually these were just random correlations, their futures aren’t always so rosy.

The point of Fooled by Randomness is that these investment advisors not only fooled unsophisticated small investors into thinking that they were brilliant (rather than just lucky and later unlucky), but they fooled their superiors at the trading firms, the financial press, and themselves as well. So all of these people were unprepared when the wheel of chance turned again — this time against them. This sets the stage for the Black Swan — not merely should we realize that we are prisoners of a chaotic environment full of randomness, but we should also realize that we actors in this environment and can use our improved appreciation of uncertainty to better shape our destinies.

In The Black Swan, Taleb tackles how to take advantage of the deliberately take advantage of improbable, but not impossible events, not by being smarter about when they will happen, but simply by embracing randomness and the impossibility of making the right prediction in the next moment but also the certainty that ultimately the unlikely even will happen. He draws on a wide range of historical events that seemed unpredictable in advance — from the world of war, to big finance, to individual career decisions to illustrate the breadth of application of this theory.

Taleb’s appreciation of this theory and how to apply it goes much deeper than typical applications of this theory in business and economics.

For instance, we are all familiar with one major financial industry built on a probabilistic understanding of an event which is unlikely in the next moment, but which in the long run becomes increasingly certain: namely Life Insurance. When we buy life insurance we are essentially making a bet — we are betting that we will die so much sooner than our remaining life expectancy that our estate will make a bigger return on paying our premium to the insurance company and collecting the value at our death, compared to what we could earn if we just put that same amount of money into some other investment (e.g. an S&P index fund) and watching it grow ourselves. Of course the Insurance company uses actuarial charts and its expectations of future investment returns to calculate a premium that will be sufficient to earn it a healthy profit over time. Yes, the Black Swan underlies this business, but the beauty of Taleb’s analysis of the Black Swan is to see that this thought process can be applied to other things as well. Should you work at a “stable” middle of the road salary job for you life? Or should you take a high risk series of high risk / high reward jobs in the hope of hitting it big?

If you live in California, should you stockpile emergency supplies in San Francisco and LA to sell immediately to unprepared homeowners after the next big earthquake hits? How about stockpiling goods in Miami for unprepared Florida coast homeowners hit by the next hurricane?

Thinking about the Black Swan can give you some insight into some of these questions, but they can also give you some insight about the eventual failure of most any institution built to profit on them too. What are your estimates of frequency/probability and magnitude of the catastrophe or subsequent profit opportunity based on? Their frequencies and magnitudes in the past? If so, how much do they reflect all the uncertain ways that the past might be different from the future.

What if global climate change occurs, and the homes you were prepared to supply after a hurricane are now permanently abandoned and under water due to rising sea levels? Or what if a small asteroid hits the Pacific plate off the California coast: instantly releasing the pressure along the entire San Andreas fault — but also creating a huge Tsunami that wipes out the large portions of San Francisco and Los Angeles. Did your model take these unlikely events into consideration? What other possible events are missing from our oversimplified models?

For all of the above reasons, I find The Black Swan a book of timeless value. But ironically it is also a book of very timely value.

As we watch the unfolding aftermath of a global credit crisis that even former Federal Reserve Chairman, Alan Greenspan, admits that he never thought possible, the Black Swan theory is a great guide: It was not the stupidity of regulators and central bankers, nor the greed of bankers, investors, and prospective homeowners that caused such a precipitous turn of events — rather it was our reliance on models that simplify complex processes enough that we can understand them and predict them MOST of the time — and our failure to distinguish MOST of the time from ALL of the time. In particular, investors and regulators failed to anticipate the risks of the Credit Default Swap market and subprime mortgage crisis can be tied to an unreasoning belief in the power of DIVERSIFICATION in the standard investment MODEL to eliminate major risks. Diversification works MOST of the time, because most of the time institutions and individuals default on loans for reasons that are largely individual, or that at worst affect only a local or regional economy or just one industrial sector of an economy. That is, these defaults are mostly independent of each other. So at any given time some subgroup is affected and default rates in that group might be higher than normal, but some other region or sector is growing and default rates there are lower — therefore if you diversify widely enough, the standard investment risk models says you should be safe from large swings in the average value of your portfolio.

That is of course only true as long as the assumption or model is based upon — that failures and loan defaults are INDEPENDENT of each other — is still true.

And historically, independence of events has been a pretty good assumption to make. In 1900, if a shipper of grain in China lost a ship full of rice due to a severe storm at sea while it was on its way to Japan, his loss might affect the farmers that grew the grain in China, the merchants who were going to sell the rice in Japan, and perhaps even the fishermen who were going to buy and eat the rice in Japan. But that loss was almost assuredly independent of whether a shipload of leather sent by train from cattle ranches in Wyoming reached shoemakers in Boston.

But the world of finance and commerce today is very different. There are multinational corporations with revenues that exceed the Domestic National Product of more than 100 countries. There are multinational financial institutions which finance and insure these companies, and their are investors who seek to diversify their risk worldwide by investing outside their own native countries. There are financial institutions that have lent so much money to so many companies around the world that if they were to pull their loans in millions of jobs would be lost overnight, And they have also borrowed so much of this money from other financial institutions that if they were to become bankrupt overnight, then 100s of of lenders would no longer be solvent either.

With all this interconnected borrowing and lending, buying and selling, saving and investing among people all around the world — all connected by fiber cable trading systems that literally work at the speed of light, we have become more INTERDEPENDENT, not INDEPENDENT. And because of that interdependence, there is a greater possibility that some large shock could suddenly affect players all around the globe and in all kinds of industries, thereby undermining our assumption of independence. So instead of being like random waves where crests colide with troughs, and troughs with crests dampening the magnitude of each and cancelling each other out, these impulses become synchronized waves whose crests intersect with crests, and troughs with troughs to amplify themselves to even larger crests and troughs like a tsunami. Soon the entire world’s financial structure and economies are at risk of a simultaneous shutdown.

The occurrence of such an alignment may be infrequent and its likelihood improbably, but the magnitude when it does hit can be devastating, and all the more so because we failed to distinguish the improbable (the Black Swan) from the impossible and so we did not prepare for it at all.

If we all better understood the theory of the Black Swan, as well as its application to our own daily life choices, we could all profit more from unlikely events, and better prepare for the dangerous Black Swans that threaten to wipe us out.

Taleb deserves great credit, as a philosopher (his preferred vocation), psychologist, sociologist, financier, economist, mathematician and engaging author for establishing a powerful highly explanatory model, and a language and framework for further analysis. This is not merely an epistemological model of what we can know and what we cannot. It is a powerful model of how we can identify what we cannot know, and the how the means we use to systematize what we know blind us to risks from what we do not know.

Judged as a work of philosophy it surely will rank with both Wittgenstein’s earlier work (Tractatus Logico-Philosophicus) and his later (Philosophical Investigations) in terms of a crystallization of how our models of the world define us and limit us in what we allow ourselves to perceive; but as a work of literature he does this with far more wit, charm, entertainment and erudition.

Kudos to Nassim Nicholas Taleb on another insighfult book, for his exceptional view into randomness how it forms mankind’s greatest capabilities and greatest limitations, and for his ability to explain so much complexity so simply, in a way accessible to us all.