The Black Swan

The Black Swan

by Nassim Nicholas Taleb

About the Author

Nassim Nicholas Taleb is a Lebanese born American writer, statistician, former trader, and risk analyst. His works deal with the implication of probability and uncertainty in the world. Taleb wrote 'The Black Swan' in 2007. This book is considered to be one of the most influential books since World War II. He is also the author of 'Fooled by Randomness', another bestseller. 

What to Expect? Expect the unexpected!

The Black Swan focuses on events which are impossible to predict, and which change the course of history. No past data can indicate their occurrence, however, due to retrospective rationalization, we find simple explanations for such events after they have happened.  Could anyone have predicted the invention of the internet, and how it is going to change the world? Did people anticipate Google's success? Had anyone in their farthest imagination, ever thought that someone would fly a plane into the WTC towers one day? The answer is no. These events were black swan events because they were completely unanticipated, and changed the world in different ways. Looking back, we can always say, that with the algorithm that Google had developed, they were bound to succeed, or with the growing menace of Al Qaida and some sporadic intelligence reports, the United States should have anticipated the September 11 attacks, however, hindsight always has 20/20 vision!

What you will learn in this book:

1)     What is a Black Swan.

2)     Why are statistical models overrated

3)     Why to know what you don't know is important.

4)     Why uncertainty can give meaning to your life.

You can't see me! The black swan.

Often, our opinions and beliefs are formed based on our experiences and observations, which we proclaim to be "evidences" for the truth. Before Australia was discovered, you would be looked at with dismissive incredulity, if you said that Swans can be black. After all, no one had ever seen a black swan. But after spotting black swans down under, the "truth" was turned on its head. It didn't matter if there were a million observations of white swans, just one observation of a black one, was enough to change the truth. These outliers, or outside the norm observations are the ones which may have extreme impact. Taleb defines black swan events as those which are extremely improbable, have a disruptive impact, and are explainable only in retrospect. Since these events can only be explained while looking back post facto, our 'predictive models' which base their predictions on past data are useless in such cases.

Social scientists have operated under the assumption that black swans don’t exist, with many developing false tools to measure uncertainty, under the guise of ‘risk models’. The truth is that our focus on the pennies and not the dollars of possibly significant events or phenomena, in spite of the obvious evidence of their huge impact, results in blindness with respect to large deviations.

"It ain't what you don't know that gets you in to trouble. It's what you know for sure, that just ain't so"!

Had the risk of a terror attack been reasonably conceivable before September 11, 2011, the 9/11 attack would not have taken place, because perhaps, fighter planes would have been circling the sky above the twin towers, or other security measures would have been in place. The Same can be said for the November 2008 terror attacks in Mumbai. The government and hotel establishments were caught unawares, and were ill prepared for an attack by terrorists who came in by the sea and wreaked havoc in the city. No hotel had robust security measures for such an attack. Even the law enforcement agencies were ill prepared for such an attack, simply because such an event was inconceivable, despite post facto emergence of some prior intelligence reports indicative of such a possibility. The beefing up of security infrastructure post the attacks has ensured better preparedness for such audacious attacks.

Even in the business world, ideas which lie far outside the normal range of focus, are generally the ones which disrupt the industry and change the world. Who would have thought earlier that the largest taxi Company in the world would not own a single taxi! How many people had bet on Google, when it was just a search engine, albeit driven by an exceptional and efficient algorithm? The more unexpected the success of such a venture, the smaller the number of competitors, and the more successful and impactful the entrepreneurs who implement the idea.

  

"Bias and impartiality is in the eye of the beholder". How our experiences can mislead!

The need for an answer, where none exists, has held Humanity in good stead over thousands of years. The evolutionary processes have made our brain quick to categorize information and make judgements based on them. Our perception is driven by linear modes. It is hard for us to think in non-linear terms, and this is dangerous in today's complex world. Often our predisposition to 'linear narratives', presents an over simplistic picture of the truth. The narratives formed in our minds, discount and ignore a lot of information which is not considered to be important by the brain, and lead to a 'narrative fallacy'. The urge to find causality in a chain of events works in a retrospective mode, and often small and insignificant events which we ignore play a big role in the final outcome. The narrative fallacy emerges from a process of 'reverse engineering' from the final outcome. In reality, the causal chain of events which we ascribe to as the causes which lead to the final outcome, are at best, anybody's guess!

The importance of narrative becomes easy to understand with an example of a goat up for being butchered on Eid. While the goat is fed and taken care off all year round, it has no reason to believe based on the narrative in its own mind, that the day of 'butchering' would be any different. But when the day comes, to its unpleasant surprise, it is killed and consumed, falling prey to the false narrative of its own mind!

Biases also exist in the way we seek information. If you believe that God exists, you would discount any information or evidence which presents views to the contrary. You would discount strong arguments by 'atheists' by resorting to the argument of 'faith', and even seek out information which confirms your own bias. You may google "strong arguments against atheism" to find articles and information which confirm what you already believe.

 

Summarizing Statistics. The limitations of the Bell Curve!

All of us would have heard of the bell curve, especially during one's appraisal process at work. But is the application of this approach beneficial in all situations? The Gaussian bell curve employs normal distribution of data, and implies that most observations center around the mean, or the average. The outliers have a very small probability of occurrence and thus do not have any significant impact on the overall data. For certain data sets which have physical limitations or scalability challenges, for example height and weight of people, a bell curve would be an appropriate application, since the observations would lie in a small range, with most observations being centered around the mean. Even if there are a few people over 7 feet tall, or those who weigh more than 300 kilograms, they would not be able to skew the mean significantly. How many people can you find who are 100 feet tall, and weigh 10000 kilograms? The answer is none, because there are physical limits to what a human being can grow to. However, for certain data sets which are scalable, normal distribution may not be correct, since a single data point maybe so overwhelmingly deviated from the normal, it can skew the average significantly and misrepresent the true picture. Take the example movie ticket grosses. A single movie can gross many times more than all the movies released during a particular period of time. Thus, the bell curve distribution in this case would not represent a true picture of the movie business, since one data point is large enough to skew the entire curve. In these cases, the bell curve ignores the massive implications of the occurrence of outlying events.

Fractal mathematics can show us a better picture of the truth in situations and environments where outlying events can cause significant disruption. Fractal maths can create models which factor in uncertainty, and allow for large deviations from the norm. This helps in preserving the robustness of the model across various types of data sets. Adopting this approach rather than a Gaussian one, can help us in focusing on the possible rather than the likely.

If Black swans are a matter of when, what then do we do? The meaning of life

Since black swans are prospectively impossible to predict with extant models, and carry a big impact, we need to build robust mechanisms to be able to absorb their impacts. In doing so, we need to set ourselves up to collect 'positive' black swans. Positive black swans are those events which have been beneficial for humankind. For example, the 'invention' of the internet, which led to unprecedented access to information via the world wide web, is such an event. While the 'negativity' or 'positivity' of a black swan event is a matter of perspective, in hindsight whichever black swan event has changed things for good and ushered in a new era, can be considered as a positive black swan. Indeed, the inability to predict outliers implies the inability to predict the course of history, and excessive focus on what we already know, only leads to more precise information of that which we already know, not that which we do not know or which we have not focused on! The French, after the Great War, built a wall along the previous German invasion route to prevent reinvasion, but Hitler simply and almost effortlessly went around the wall and invaded them anyway. The lesson here is that while the French had learned from the first invasion, they reacted by building the wall as a measure of avoiding a re-invasion. However, they were too focused on the previous mode of invasion, and did not think of other possibilities.

While we may not be able to predict events because of the inherent uncertainty of things, but this is not an argument for being nihilistic or depressed. You will be mentally impacted by the unlikely only if you let yourself be dictated by circumstance, so by 'choosing' to focus on the positive, you would be able to look at things like a missed opportunity with stoic disdain. By not worrying about small insignificant failures compared to the big terminal ones, we can have a sense of control over things, and still appreciate the beauty of uncertainty. In fact, all of us have a strong reason to be happy, because the odds were stacked against us when we emerged as a species. The odds of life itself emerging may be extremely miniscule, and hence, all of us are little black swans ourselves! Each one of us can embrace uncertainty in our lives and by doing so, become above the impact of events which occur.

 

Final Summary

The central idea presented in this book is that our methods of 'predicting' are flawed, and do not apply to scalable data. Our reliance on past data, coupled with our cognitive and other biases, is futile in predicting Black Swan events, since these events can only be explained in retrospect. Identifying what one doesn't know, helps in creating better risk models so to speak, in all fields. By only focusing on the known knowns, we discount the disruptive power of the unknowns, and thus, we must develop an approach to at least establish what one doesn't know. While our lack of understanding of what randomness or uncertainty implies, is a roadblock for creating predictive models even in our own brain, this should not restrain us from leading a more satisfied and happy life. It should be noted that randomness, though sometimes bad, can also produces great life experiences and positive accidents make us appreciate the beauty of uncertainty. By focusing on the positive swans and learning from them, and just being aware of the unknowns, we can lead a more fruitful life.