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Summary on black swans
Topic: Black swans
by Tim, 2018 Cohort
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
Before the discovery of Australia, people in the Old World were convinced that all swans were white, an unassailable belief as it seemed completely confirmed by empirical evidence.[1] At the time, it was unimaginable that a swan could be black.
According to scholar and risk analyst Nassim Nicholas Taleb, in his book The Black Swan: The Impact of the Highly Improbable, a Black Swan is a metaphor for a significant and very unlikely event that is later rationalised as having been predictable. The notion of a Black Swan came about after the inference that all swans were white was invalidated by the discovery of black swans in Australia. This phenomenon reveals that one single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans.[3]
A Black Swan has three criteria:
- A significant impact
- It is extremely unlikely
- Its occurrence is later rationalised as having been predictable
The reflection on a Black Swan, and its rationalisation in hindsight, highlights that we often attempt to justify the occurrence of an event and its predictability after the fact; this is, despite the fact that we did not prepare for, expect or predict that the event would even take place.
Black Swans are important because they help us to realise that the events that can have the greatest impact on our lives may be those that are the least predictable. The bell-curve, or normal distribution, is a probability model used to predict the likelihood of different events occurring. We spend most of our time analysing events that are likely to happen the events lying within the thicker sections of the curve we often fail to consider the events at extremities of the curve. It is at the ends of the curve where the Black Swans exist: World War I, the rise of the Internet, 9/11, Trump all were extremely unlikely, had (or are having) an enormous impact, and we have rationalised them all as predictable.
The central idea of Taleb’s book, The Black Swan, is that we are blind to randomness (particularly the large deviations) despite our lives being the cumulative effect of several significant shocks. As such, it is asserted that Black Swan logic makes what we dont know far more relevant than what we do know. When confronted with complexity we must be cautious to not make inferences from known data points and believe that we have explained the unknowns, but appreciate that the outcome may be one that we are yet to consider. Where the gap between what you know and what you think you know becomes dangerously wide, it is here where Taleb asserts the Black Swan is produced.
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This content has been contributed by a student as part of a learning activity.
If there are inaccuracies, or opportunities for significant improvement on this topic, feedback is welcome on how to improve the resource.
You can improve articles on this topic as a student in "Unravelling Complexity", or by including the amendments in an email to: Chris.Browne@anu.edu.au
Note: This entry was created in 2018, when the task was to “summarise a key reading”, and so may not represent a good example to model current primer entries on.
Before the discovery of Australia, people in the Old World were convinced that all swans were white, an unassailable belief as it seemed completely confirmed by empirical evidence.[1] At the time, it was unimaginable that a swan could be black.
According to scholar and risk analyst Nassim Nicholas Taleb, in his book The Black Swan: The Impact of the Highly Improbable, a Black Swan is a metaphor for a significant and very unlikely event that is later rationalised as having been predictable. The notion of a Black Swan came about after the inference that all swans were white was invalidated by the discovery of black swans in Australia. This phenomenon reveals that one single observation can invalidate a general statement derived from millennia of confirmatory sightings of millions of white swans.[3]
A Black Swan has three criteria:
- A significant impact
- It is extremely unlikely
- Its occurrence is later rationalised as having been predictable
The reflection on a Black Swan, and its rationalisation in hindsight, highlights that we often attempt to justify the occurrence of an event and its predictability after the fact; this is, despite the fact that we did not prepare for, expect or predict that the event would even take place.
Black Swans are important because they help us to realise that the events that can have the greatest impact on our lives may be those that are the least predictable. The bell-curve, or normal distribution, is a probability model used to predict the likelihood of different events occurring. We spend most of our time analysing events that are likely to happen the events lying within the thicker sections of the curve we often fail to consider the events at extremities of the curve. It is at the ends of the curve where the Black Swans exist: World War I, the rise of the Internet, 9/11, Trump all were extremely unlikely, had (or are having) an enormous impact, and we have rationalised them all as predictable.
The central idea of Taleb’s book, The Black Swan, is that we are blind to randomness (particularly the large deviations) despite our lives being the cumulative effect of several significant shocks. As such, it is asserted that Black Swan logic makes what we dont know far more relevant than what we do know. When confronted with complexity we must be cautious to not make inferences from known data points and believe that we have explained the unknowns, but appreciate that the outcome may be one that we are yet to consider. Where the gap between what you know and what you think you know becomes dangerously wide, it is here where Taleb asserts the Black Swan is produced.