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. Talking about black swans. 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.

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Terje Aven University of Stavanger

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Terje aven university of stavanger

Black swans

in a risk context

Terje Aven

University of Stavanger

JRC, ISPRA

21 June 2013


Talking about black swans

Talking about black swans

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


Professor dennis lindley

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


Terje aven university of stavanger

  • Mediocristan (Normalistan)

  • Extremistan (black swans)

Nassim N. Taleb


Lindley example

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.


Terje aven university of stavanger

Prior density for p: the chance of a white swan

1

1


Terje aven university of stavanger

Prior density for p: the chance of a white swan

1

What is the probability that p=1?

1


Terje aven university of stavanger

Prior density for p: the chance of a white swan

1

What is the probability that p=1?

It is zero!

1


Terje aven university of stavanger

Prior density for p: the chance of a white swan

1

There is a positive fraction of black swans out there !

1


Terje aven university of stavanger

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


Terje aven university of stavanger

Prior density for p: the chance of a white swan

0.8

x

x

0.2

0.99

1


Terje aven university of stavanger

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


Terje aven university of stavanger

Depending on the assumptions made,

we get completely different conclusions about the probability of a

black swan occurring


Terje aven university of stavanger

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)


Main problems with the probability based approach

Main problems with the probability based approach

1

Assumptionscanconcealimportantaspectsof risk and uncertainties

2

Presumeexistenceofprobabilitymodels

3

The probabilitiescan be the same buttheknowledgetheyarebuiltonstrong or weak

4

Surprises occur


Terje aven university of stavanger

Risk perspective

Probability-based

Historical data

Knowledge dimension

Surprises

+

+


Terje aven university of stavanger

P(head) = 0.5

P(attack) = 0.5

Strong

knowledge

Poor knowledge


John offers you a game throwing a die

John offers you a game: throwing a die

What is your risk?

”1,2,3,4,5”: 6

”6”: -24


Terje aven university of stavanger

Risk

(C,P):

  • 6 5/6

  • -24 1/6

    Is based on an important assumption – the die is fair


Background knowledge

“Background knowledge”

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


Terje aven university of stavanger

Risk perspective

Probability-based

Historical data

Knowledge dimension

Surprises

+

+


Black swan taleb 2007

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.


Aven 2013 questions whether a black swan is

Aven (2013) questions whether a black swan is

  • 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.


Black swan aven 2013

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

Unforeseen/surprising events:

  • 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


Terje aven university of stavanger

Government building Oslo 22 July 2011


Threats

Threats

Known unknowns

Unknown unknowns, black swans

(A’, C’, Q, K)


It is not about assigning correct probabilities

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 adjustments


Terje aven university of stavanger

Statfjord A

Do we have black swans here?


How to confront black swans

How to confront black swans

Improved Risk Assessments

Robustness

Resilience

Antifragility


How to confront black swans1

How to confront black swans

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

Improved Risk Assessments

Robustness

Resilience

Antifragility


Petromaks project improved risk assessments to better reflect the knowledge dimension and surprises

PETROMAKS project: Improved risk assessments- to better reflect the knowledge dimension and surprises


Unforeseen surprising events1

Unforeseen/surprising events:

  • 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


Terje aven university of stavanger

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


Terje aven university of stavanger

  • - 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.


Terje aven university of stavanger

Analysis

Management

Risk analysis

Describing

uncertainties, …

Management

review and

judgment

Decision

Risk-informed decision making


Terje aven university of stavanger

Extra


Terje aven university of stavanger

Risk

(A,C,U)

(C,U)

A: Event, C: Consequences

U: Uncertainty


Risk description

Risk description

(A,C,U)

(C,U)

Q

K

C’

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

K: Background knowledge

C’: Specific consequences


Subjective knowledge based probability

Subjective/knowledge-based probability

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).


Terje aven university of stavanger

Analysis

Management

Risk analysis

Cost-benefit analysis,

Risk acceptance criteria

Management

review and

judgment

Decision

Risk-informed decision making


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