Black swan events are so fascinating that they are a common source of discussion in many risk management forums. The variety of perspectives coming from all directions never ceases to amaze me.
To a risk manager, ‘black swan’ phenomena are improbable events that have massive impacts on a business or society on rare occasions. It means the event is unexpected but has enormous consequences (Ferguson, 2014). Unfortunately, there is no scientific way to predict black swan events reasonably and acceptably.
I tend to question the result of the research, which suggested that by exploiting many types of data, risk managers can help prevent (or at least contain) the damage related to black swan events and other risky blind spots. How can any data be helpful without the process of correlation? Black swan events cannot be accurately quantified or calculated. They are unknown unknowns.
The exciting part mentioned in one study points to using integrated data to point to potential risks. The mere mention of integrated data underlines correlation; i.e., we have to correctly associate one datum to the next, or one set of information to the others, for them to be of value. That can prove impossible when we have nothing to start with. So how do we start working on something we do not know? There is an immense number of data points where one can start. Only by scratching the surface of knowledge that “knowing” begins.
Unknown unknowns (black swans) might be in the room, or even an elephant, for all we know, but we cannot see them until circumstances make them visible. Once we see that the risk exists, we surmise that it no longer qualifies as a black swan event because we are now aware of the risk, and the element of surprise is no longer there. It is now the typical type of risk that you and many risk managers are already familiar with, the known unknowns.
Bill Pieroni, Chief Operating Officer at insurance giant Marsh, and a few others contend that the best way to manage risk, even black swans, is to use big data. He explains that some events occur with more and more regularity, suggesting that some seemingly unknowable events are becoming more or less predictable. He claims that this big data will give way to shades-of-grey swans.
In my mind, he may be talking about the transition from being unknown to more or less known. Although it sounds logical, shades of grey will be a doubtful state, a ghost of something that will not present any solid evidence but introduce vagueness to nothingness. It might only serve as an uncertainty generator.
In the present age and time, black swan events can only be addressed by intuition, despite being labeled as one of the cognitive biases that underlie human flaws in decision-making. We can all agree that if anyone has the proper perspective, understanding, and tools to process universal data and integrate them into coherent information, to predict a black swan event is theoretically possible.
The problem with this concept is that people have yet to find a way to make it practically possible. Contrary to what the author implies, the real-world application of Pieroni’s ideas is still impossible. The risk universe is immense, yet each component can affect the results regardless of how small it might be. If we put a bracket to what data we analyze, we only have part of the picture.
If we do not put a bracket of limitation to what we evaluate, then we are analyzing infinity and will not arrive at an answer. We are talking about a significant and expansive risk network that trumps common comprehension. Many risk drivers lie so far outside the boundaries of what we tend to consider that it is futile to predict a potential outcome. Tracing the cause of a black swan event that has already happened can lead us to the most seemingly insignificant occurrence.
It is easy to posit real-life examples of how some insignificant events result in significant events, spawning other effects in a never-ending fashion. Some of you might even trace a problem to when a person was born, arguing that things would have turned out differently if he had not come into being.
Well, I must tell you, the iterations are endless. The good thing is that it is an excellent mental exercise.
Source: Frago, R. (2015). Risk-based Management in the World of Threats and Opportunities: A Project Controls Perspective. ISBN 978-0-9947608-0-7 (Canada). Section 1.
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About the Author
Rufran C. Frago is the Founder of PM Solution Pro, a Calgary consulting, product, and training services firm focusing on project and business management solutions. He is passionate about providing advice, mentorship, education and training through consultation, collaboration, and what he uniquely calls, student-led training.
Our website: www.pmsolutionpro.com
BOOKS AUTHORED BY RUFRAN FRAGO
- Risk-based Management in the World of Threats and Opportunities: A Project Controls Perspective.ISBN 978-0-9947608-0-7.Canada
- Plan to Schedule, Schedule to Plan.ISBN 978-0-9947608-2-1.Canada
- How to Create a Good Quality P50 Risk-based Baseline Schedule.ISBN 978-0-9947608-1-4.Canada
- Schedule Quantitative Risk Analysis (Traditional Method).ISBN 978-0-9947608-3-8.Canada
- RISK, What are you? The Risk Manager’s Poem: Children’s Book for all Professionals.ISBN 978-0-9947608-4-5 (Canada)
OTHER BOOK AUTHORED BY RUFRAN
