Why “Black Swan”?

Louie Dy
3 min readJan 17, 2021

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Logo designed by Winnie Dy (Copyright 2020).

When we think of swans, we’d usually think of white swans, not black swans, because we almost never see these black swans. Yet black swans do exist. You can go to a lake and see over 1000 white swans, 10000 white swans, or even perhaps a million white swans, yet you cannot say that black swans don’t exist. You cannot disprove the nonexistence of a black swan.

Therefore, a black swan exemplifies the fact that the absence of evidence is not evidence of its absence. Just because one has not seen something, it does not mean that it does not exist and is beyond our concern. In fact, we should be cautious of what we do not see or know. Black swans also represent an overconfidence in our own common knowledge, as well as things that we simply just cannot know or predict, e.g. timing of earthquakes.

The concept of a “black swan” also deals with extreme impact. These events almost never happen, but once they do, they change the entire course of history. Some examples include wars, pandemics, earthquakes, major stock market crashes, etc. In health and medicine, some good examples are aplastic anemia from the antibiotic chloramphenicol, Steven-Johnson Syndrome in several antibiotics, and rare diseases, rare cancers, rare genetic mutations.

Thus, Black Swans redefine our reality. The wake of the COVID-19 pandemic in the world is said to be a “Black Swan”, at least according to many commentaries around the world. We even compare this to the 1918 Influenza Pandemic.

The first question we need to ask is, “Why is this a Black Swan?” Major epidemics occur at least once in every 5 or 10 years; they are ubiquitous. Since the year 2000 up to this day, we have seen the Severe Acute Respiratory Syndrome (SARS) coronavirus outbreak of 2003, Influenza AH1N1 pandemic of 2009, Middle East Respiratory Syndrome (MERS) coronavirus outbreak of 2012, Zika virus outbreak of 2015–2016, Ebola virus outbreak of 2013–2016. On top of these outbreaks, one has to note the ongoing Human Immunodeficiency Virus (HIV) outbreak since its discovery in the 1980s and the reeruption of Measles outbreaks due to vaccine hesitancy.

Saying that COVID-19 is a “Black Swan” means that we are too ignorant that we did not see this coming. Governments that have long neglected and did not invest in their country’s health care systems suddenly expect building up their health capacity overnight or over a few days or weeks. No. That is impossible. You don’t prepare for major disasters when they occur. You don’t build houses easily destroyed by earthquakes or floods. It takes a long time — years, or perhaps even decades — to become fully prepared for a disaster.

If anything, Black Swans do tell us that there’s a lot that we don’t know, and the limits of our scientific knowledge, scientific method, statistical tools, and even our personal opinions.

To quote Nassim Nicholas Taleb in his book “The Black Swan: The Impact of the Highly Improbable”:

“One single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans. All you need is one single (and, I am told, quite ugly) black bird.

I push one step beyond this philosophical-logical question into an empirical reality, and one that has obsessed me since childhood. What we call here a Black Swan (and capitalize it) is an event with the following three attributes.

First, it is an outlier, as it lies outside the realm of regular expectations, because nothing in the past can convincingly point to its possibility.

Second, it carries an extreme impact (unlike the bird).

Third, in spite of its outlier status, human nature makes us concoct explanations for its occurrence after the fact, making it explainable and predictable.

I stop and summarize the triplet: rarity, extreme impact, and retrospective (though not prospective) predictability.”

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Louie Dy

A medical doctor and internist-in-training who is deeply interested in epidemiology, informatics, mathematics, statistics, complex systems, and data science