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Terje Aven University of Stavanger

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Black swans

in a risk context

Terje Aven

University of Stavanger

JRC, ISPRA

21 June 2013

Aven (2013) On the meaning of a black swan in a risk context. Safety Science

Creates a lot of enthusiasm

Hard negative words from some researchers

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

- Mediocristan (Normalistan)
- Extremistan (black swans)

Nassim N. Taleb

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.

Prior density for p: the chance of a white swan

1

1

Prior density for p: the chance of a white swan

1

What is the probability that p=1?

1

Prior density for p: the chance of a white swan

1

What is the probability that p=1?

It is zero!

1

Prior density for p: the chance of a white swan

1

There is a positive fraction of black swans out there !

1

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

Prior density for p: the chance of a white swan

0.8

x

x

0.2

0.99

1

Prior density for p: the chance of a white swan

0.8

x

x

0.2

0.99

1

the probability of a

black swan occurring is

close to zero

Depending on the assumptions made,

we get completely different conclusions about the probability of a

black swan occurring

Lindley’s example also fails to reflect the essence of the black

swan issue in another way

In real life the definition of a probability

model and chances cannot always be justified

P(attack)

1

Assumptionscanconcealimportantaspectsof risk and uncertainties

2

Presumeexistenceofprobabilitymodels

3

The probabilitiescan be the same buttheknowledgetheyarebuiltonstrong or weak

4

Surprises occur

Risk perspective

Probability-based

Historical data

Knowledge dimension

Surprises

+

+

P(head) = 0.5

P(attack) = 0.5

Strong

knowledge

Poor knowledge

What is your risk?

”1,2,3,4,5”: 6

”6”: -24

(C,P):

- 6 5/6
- -24 1/6
Is based on an important assumption – the die is fair

Assumption 1: …

Assumption 2: …

Assumption 3: …

Assumption 4: …

…

Assumption 50: The platform jacket structure will withstand

a ship collision energy of 14 MJ

Assumption 51: There will be no hot work on the platform

Assumption 52: The work permit system is adhered to

Assumption 53: The reliability of the blowdown system is p

Assumption 54: There will be N crane lifts per year

…

Assumption 100: …

…

Model: A very crude gas dispersion model is applied

Risk perspective

Probability-based

Historical data

Knowledge dimension

Surprises

+

+

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.

- A surprising extreme event relative to the expected occurrence rate
- An extreme event with a very low probability.
- A surprising, extreme event in situations with large uncertainties.
- An unknown unknown.

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.

- Events that were completely unknown to the scientific environment (unknown unknowns)
- Events that were not on the list of known events from the perspective of those who carried out a risk analysis (or another stakeholder)
- Events on the list of known events in the risk analysis but found to represent a negligible risk

Government building Oslo 22 July 2011

Known unknowns

Unknown unknowns, black swans

(A’, C’, Q, K)

- 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 adjustments

Statfjord A

Do we have black swans here?

Improved Risk Assessments

Robustness

Resilience

Antifragility

Taleb: propose to stand our current approaches to prediction, prognostication, and risk management

Improved Risk Assessments

Robustness

Resilience

Antifragility

- Events that were completely unknown to the scientific environment (unknown unknowns)
- Events that were not on the list of known events from the perspective of those who carried out a risk analysis (or another stakeholder)
- Events on the list of known events in the risk analysis but found to represent a negligible risk

Not seeing what is coming, when we should have seen it

- - Preoccupation with failure
- - Reluctance to simplify
- Sensitivity to operations
- Commitment to resilience
- Deference to expertise

2

Mindfulness

(Collective)

2

Quality management

New way of thinking about risk

1

Risk analysis and management

1

Concepts and principles

Aven and Krohn (2013) RESS.

Analysis

Management

Risk analysis

Describing

uncertainties, …

Management

review and

judgment

Decision

Risk-informed decision making

Extra

(A,C,U)

(C,U)

A: Event, C: Consequences

U: Uncertainty

(A,C,U)

(C,U)

Q

K

C’

Q: Measure of uncertainty (e.g. P)

K: Background knowledge

C’: Specific consequences

K: background knowledge

- 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).

Analysis

Management

Risk analysis

Cost-benefit analysis,

Risk acceptance criteria

…

Management

review and

judgment

Decision

Risk-informed decision making