The Black Swan

One of my favorite things about working with testers is that they read a wide variety of interesting books - this is one of them. “The Black Swan,” by Nassim Taleb, is one of those books that has generated a considerable buzz within several communities of thought, including testing. I found that I could not resist reading this book as an investor on first pass, so I may need to re-read it as a software tester (perhaps another blog post to follow). Here is my summary of, and reaction to, The Black Swan...

A Black Swan event has three characteristics. It is an outlier, it has extreme impact, and it is later thought to have been predictable or even predicted. "...Rarity, extreme impact, and retrospective (though not prospective) predictability." September 11th, several market crashes, and WWI are examples of large scale Black Swan events. Black Swans may also be smaller in scale, or even personal, such as the beginning or ending of a romantic relationship; and they can also have positive impacts, such as an unexpected inheritance. At the time of this writing, I am 43 years old, and I would agree there have been several Black Swan events during my lifetime, though, unfortunately, no long lost rich uncles.

Taleb’s point is not just that Black Swans are real; it’s that they actually drive the course of history (and the courses of our lives), much more so than ‘normal’ events. Furthermore, many of our current methods of forecasting the future and managing risk are not only ineffective, they actually incubate Black Swans as they exacerbate our exposure to them.

Really? How can this be? Is this guy just saying something sensational to sell books?

Taleb discusses at length institutionalized misunderstandings of the nature of uncertainty, decrying the Gaussian bell curve as the “Great Intellectual Fraud.” One of the problems with the bell curve is the nature of outliers. The bell curve suggests that their rarity practically eliminates the significance of their effect, allowing us to predict with false confidence; while Taleb holds that outliers in some important fields, like finance and history, profoundly affect the nature of all subsequent events.

Another problem with the bell curve is that of regress. In other words, we need more data to better define the shape of the curve, but we assume the shape of the curve before we plot the data. Try to explain this to someone who is not a statistician; they will be asleep before the second wave of your hand.

Silent evidence is a more general problem with our modeling tools. The data that we observe most easily is likely to be produced by winners or survivors of some process. The losers are usually harder to see, but they may be much more numerous, giving us an overly optimistic view.

Taleb does not eschew all mathematical tools, however. He compliments the concept of scalable randomness and the related work of Mandelbrot in the field of fractals. He claims that markets, for example, are better modeled as fractals because of the model’s ability to ‘blow up,’ but that their exponential factors are, alas, still not knowable with any useful level of precision to allow prediction.

“...scalable randomness is unusually counter-intuitive.”

“There is no such thing as a “long run” in practice; what matters is what happens before the long run.”

Taleb also discusses some psychological factors that expose us to Black Swans, for example, confirmation bias. This is our tendency to accept confirmation of our beliefs and ignore contradicting evidence. Narrative fallacy is another such psychological factor. This is our tendency to organize data into stories, to imagine causal links between events, to ‘fill in the blanks.’ This makes it easier for us to remember more information, but the imagined links may be phony.

Information itself is not as valuable as we might think. Given the aforementioned problems with our understanding of uncertainty, and our psychological tendencies, the addition of more information to our situation may only serve to solidify our grip on dangerously flawed models.

Let’s just pretend for a second that I buy all this, what does it mean to me? How should it affect my behavior?

Taleb does offer a piece of semi-concrete investment advice, and that is to use a “Barbell Strategy.” Put ninety percent of your money in very stable investments, and the remaining small fraction in highly speculative vehicles. You gain exposure to positive Black Swans without risking the substantial impact of negative ones. (As of this writing, I am considering, but do not feel compelled by, this suggestion.)

Otherwise, despite Taleb’s appreciation of the pragmatic, and distaste for the theoretical and academic, practical advice was admittedly sparse in this book. However, I think it is safe to say if you try to change your predictive models to account for Black Swans, you’ve missed the point. I imagine Taleb is simply telling us, "DON’T BE THE TURKEY." - STOP PREDICTING. Or, if you must predict, please be aware that you are likely to do so horribly. STOP RELYING ON THE PREDICTIONS OF OTHERS. Or, if you must do so, please protect yourself from the fallout. And finally, BE ROBUST AGAINST DISASTER, AND OPEN TO OPPORTUNITY (whatever that means to you).