A ‘black swan’ event is a metaphor that describes an event that comes as a surprise. When the phrase was coined, the black swan was presumed non-existed and must reinterpreted after black swans were discovered. The importance of the metaphor lies in its analogy to the fragility of any system of thought. A set of conclusions is potentially undone that followed a presumed, underlying logic. A ‘black swan’ (Taleb) is an unexpected event with a large magnitude and big consequences for history. Such events, considered extreme outliers, collectively play vastly larger roles than regular occurrences. The term connotes the idea that a perceived impossibility might later be disproven.
A ‘black swan’ event following three attributes: rarity, extreme ‘impact’, and retrospective (though not prospective) predictability. A ‘black swan’ event is an incident that occurs randomly and unexpectedly and has wide-spread ramifications. The event could have been expected based on the available, relevant data but were unaccounted for risk mitigation programs or maintenance tasks.
But not all black swans turn out to be black!
A typical event that was unjust called a ‘black swan’ by several experts was the blackout of the Dutch 112 network on the 24 of June 2019. 112 is a common emergency telephone number in order to reach emergency services (ambulance, fire and rescue, police). In reality 112 has been affected by a failure more often. In 2012 that was the case several times. According to KPN (the network operator, these were isolated incidents, including during maintenance work.
Such an important network may and will eventually show random failures. However, what distinguishes this blackout from a ‘black swan’ event is that it could have been foreseen and therefore mitigating measures could be taken in advance. This is because the failure of such a network has far-reaching disruptive social consequences. The possible incorrect or timely performance of maintenance cannot be used to classify failure as a ‘black swan’. More generally, decision based on a fixed universe or a model of possible outcomes, ignores and minimizes the effect of events that are ‘outside the model’. A fixed model considers the ‘known unknowns’, but ignores the ‘unknown unknowns’.
So, the role of maintenance is not to predict events which are unpredictable, but to help build robustness against negative events. Asset are very vulnerable to hazardous blackouts and are exposed to unpredictable losses. The robustness of a concept must be a central topic in the prevention of black swans by identifying areas of vulnerability in order to turn the black swans white.