Terje Aven

University of Stavanger

JRC, ISPRA

21 June 2013

Black swans in a risk context

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

  

•Mediocristan (Normalistan)

•Extremistan (black swans)

Nassim N. Taleb

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

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 !

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

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

P(head) = 0.5

P(attack) = 0.5

Strong knowledge

Poor knowledge

John offers you a game: throwing a die

• ”1,2,3,4,5”: 6

• ”6”: -24

What is your risk?

Risk

(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 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

Government building Oslo 22 July 2011

Threats

Known unknowns

Unknown unknowns, black swans

(A’, C’, Q, K)

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

Statfjord A

Do we have black swans here?

How to confront black swans

• Improved Risk Assessments

• Robustness

• Resilience

• Antifragility

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

• Not seeing what is coming, when we should

have seen it

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

• Extra

Risk

(A,C,U)

A: Event, C: Consequences

U: Uncertainty

(C,U)

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

Risk analysisCost-benefit analysis,

Risk acceptance criteria

…

Managementreview and judgment

Decision

Analysis Management

Risk-informed decision making