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Talking about black swans
• Creates a lot of enthusiasm
• Hard negative words from some
researchers
Aven (2013) On the meaning of a black swan in a risk context. Safety Science
Professor Dennis Lindley
Taleb talks nonsense
He lampoons Taleb’s distinction between the lands of Mediocristan
and Extremistan, the former capturing the placid randomness
as in tosses of a coin, and the latter covering the dramatic
randomness that provides the black swans
No need to see beyond probability
Lindley example
A sequence of independent trials with a
constant unknown chance p of success (white
swan)
Lindley shows that a black swan is almost
certain to arise if you are to see a lot of swans,
although the probability that the next swan
observed is white, is nearly one.
1
Prior density for p: the chance of a white swan
1
There is a positive fraction of black swans out there !
The probability-based approach to treating the risk and uncertainties is based on a background knowledge that could hide critical assumptions and therefore provide a misleading risk description
1
Prior density for p: the chance of a white swan
0.8 x
0.99
x0.2
the probability of a black swan occurring isclose to zero
Depending on the assumptions made,we get completely different conclusions about the probability of ablack swan occurring
Lindley’s example also fails to reflect the essence of the blackswan issue in another way
In real life the definition of a probabilitymodel and chances cannot always be justified
P(attack)
Main problems with the probability based approach
15
4Surprises occur
1Assumptions can conceal important aspects of risk and
uncertainties
3The probabilities can be the same
but the knowledge they
are built on strong or weak
2Presume
existence of probability
models
Probability-based
Historical data
Probability-based
Historical data
Knowledge
dimension +
+
Surprises
Risk perspective
Assumption 1: …Assumption 2: …Assumption 3: …Assumption 4: ……Assumption 50: The platform jacket structure will withstand
a ship collision energy of 14 MJAssumption 51: There will be no hot work on the platformAssumption 52: The work permit system is adhered toAssumption 53: The reliability of the blowdown system is pAssumption 54: There will be N crane lifts per year…Assumption 100: ……
“Background knowledge”
Model: A very crude gas dispersion model is applied
Probability-based
Historical data
Probability-based
Historical data
Knowledge
dimension +
+
Surprises
Risk perspective
Black swan (Taleb 2007)
• Firstly, it is an outlier, as it lies outside the realm of regular
expectations, because nothing in the past can convincingly
point to its possibility.
• Secondly, it carries an extreme impact.
• Thirdly, in spite of its outlier status, human nature makes
us concoct explanations for its occurrence after the fact,
making it explainable and predictable. 22
Aven (2013) questions whether a black swan is
1. A surprising extreme event relative to the
expected occurrence rate
2. An extreme event with a very low probability.
3. A surprising, extreme event in situations with
large uncertainties.
4. An unknown unknown.
Black swan (Aven 2013)
A surprising extreme event relative to the present
knowledge/beliefs.
Hence the concept always has to be viewed in relation
to whose knowledge/beliefs we are talking about, and at
what time.
Unforeseen/surprising events:
A. Events that were completely unknown to the scientific environment (unknown unknowns)
B. Events that were not on the list of known events from the perspective of those who carried out a risk analysis (or another stakeholder)
C. Events on the list of known events in the risk analysis but found to represent a negligible risk
It is not about assigning correct probabilities
• But to provide – a proper understanding of the total system– means to identify many of these B and C
events – measures to me meet them, in particular
resilient measures – means to read signals and warnings to
make adjustments28
How to confront black swans
• Improved Risk Assessments
• Robustness
• Resilience
• Antifragility
Taleb: propose to stand our current approaches to prediction, prognostication, and risk management
PETROMAKS project: Improved risk assessments- to better reflect the knowledge dimension and surprises
Unforeseen/surprising events:
A. Events that were completely unknown to the scientific environment (unknown unknowns)
B. Events that were not on the list of known events from the perspective of those who carried out a risk analysis (or another stakeholder)
C. Events on the list of known events in the risk analysis but found to represent a negligible risk
New way of thinking about
risk
1Risk analysis
and management
2Mindfulness(Collective)
2Quality
management
1Concepts and
principles
- Preoccupation with failure- Reluctance to simplify -Sensitivity to operations-Commitment to resilience -Deference to expertise
Aven and Krohn (2013) RESS.
Risk analysis Describing
uncertainties, …
Managementreview and judgment
Decision
Analysis Management
Risk-informed decision making
Risk description
(A,C,U)
Q: Measure of uncertainty (e.g. P)
K: Background knowledge
C’: Specific consequences
(C,U)
C’
Q
K
Subjective/knowledge-based probability
• P(A|K) =0.1
• The assessor compares his/her uncertainty (degree
og belief) about the occurrence of the event A
with drawing a specific ball from an urn that
contains 10 balls (Lindley, 2000. Kaplan and Garrick 1981).
K: background knowledge