1. University of Stavanger, Norway
Risk, surprises and black swans: What is the appropriate management approach? 
Terje Aven,
University of Stavanger, Norway
Loss Prevention Symposium 2019
June Delft

2. Black swans

3. Not a surprise for others
A surprise for some
Not a surprise for others
Unknown knowns

4. Our knowledge is
poor or wrong

5. The black swan metaphor
Surprise
Focus on the knowledge
Assumptions
Signals and warnings
How can the knowledge be strengthened

6. Improve risk management
Black swan metaphor
Improve risk management
‘Surprise risk’ need to be acknowledged and confronted
exam

7. Risk, surprises and black swans: What is the appropriate management approach ? 

8. Risk assessment informed Robustness, resilience-based strategies
Main strategies for handling risk
Codes and standards – simple problems
Risk assessment informed
Robustness, resilience-based strategies
Cautionary/
precautionary principles
Dialogue
Robustness, resilience Redundancy BAT Substitution Defence in depth
Balancing other concerns

9. Development and protection
Balance
Development and protection
Develop,
creating values
Take risk
Reduce the risks
and uncertainties
development, protection
Question: ALARP
ALARP
Cautionary-precautionary
Cost-benefit analyses
Risk acceptance criteria

10. ALARP The burden of proof is reversed
Risk shall be reduced to a level that is As Low As Reasonably Practicable (ALARP)
”Gross disproportion”
A risk reducing measure should be implemented provided it cannot be demonstrated that the costs are grossly disproportionate relative to the gains obtained
Costs
Benefits
The burden of proof is reversed

11. Risk assessment What can happen? (i.e., what can go wrong?)
If it does happen, what are the consequences?
How likely? (expressing uncertainties)

12. P(A) = 0.01
P(A) ≤ 0.01

13. Expressing uncertainty
Probability

14. Expressing uncertainty
Probability
Knowledge
P(A) = 0.01
P(A) ≤ 0.01
Strength of knowledge judgments

15. Expressing uncertainty
Probability (interval) P
Knowledge K
Challenge 1 either subjective jugment P or more objective transofmration imprecse prob
The interval prob does not remove the sub of K, K may be to varying degreee strong, and even wrong
Challenge 1
Challenge 2

16. Strength of knowledge The reasonability of assumptions
Amount of reliable data and information
Degree of agreement/consensus among experts (coming from different ‘schools’)
The degree to which relevant phenomena involved are considered well understood, how well models are justified
The degree to which the knowledge basis has been thoroughly examined
Different systems, many analysts do nt like them, beczuse it is qualititative and they think quantificaion s moe science and better.
We know the problems wit dilemma
That is your choice.

17. Risk assessment informed Robustness, resilience-based strategies
Main strategies for handling risk
Codes and standards – simple problems
Risk assessment informed
Robustness, resilience-based strategies
Cautionary/
precautionary principles
Dialogue
BAT, redundancy Defense in depth, substi
Balancing other concerns

18. Is cautionary/precautionary thinking wise (rational)?
Ask ?

19. Kjerag bolt

20. From risk to resilience
Resilience is the ability of the system to sustain or restore its basic functionality following a risk source or an event (even unknown) (SRA 2015)

21. ?
Risk analysis and management
Resilience analysis and management

22. Consequence of an activity
Effects C
Events A
Values
Vulnerability, resilience
Uncertainties U

23. Risk = (Consequences,U) = (A,C,U) = (A,U) + (C,U|A)
Risk = ‘event contribution’ AND vulnerability

24. the effect of uncertainty on objectives
‘Risk’
ISO 31000
the effect of uncertainty on objectives
An effect is a deviation from the expected (positive and/or negative)
Aven, T. and Ylonen, M. (2019) The strong power of standards in the safety and risk fields: A threat to proper developments of these fields?
Reliability Engineering and System Safety. Open Access.
Risk is not the effect of uncertainty
But the effects and consequences of activities are uncertain – and that is why we face risk

25. the effect of uncertainty on objectives
‘Risk’
ISO 31000
the effect of uncertainty on objectives
An effect is a deviation from the expected (positive and/or negative)
The effects – consequences - of an activity are uncertain
Risk is not the effect of uncertainty
But the effects and consequences of activities are uncertain – and that is why we face risk

26. Risk, surprises and black swans
Acknowledge the ‘surprise risk’
Develop suitable frameworks, approaches and methods

27. Literature
Society for Risk Analysis Glossary, Core Subjects, Key Principles and Foundations of Risk Analysis (sra.org/resources)
Aven, T. (2018) An Emerging New Risk Analysis Science: Foundations and Implications. Risk Analysis. Open access
Aven, T. (2016) Risk assessment and risk management: review of recent advances on their foundation. European Journal of Operational Research, 25: Open access.
Aven, T. (2014) Risk, surprises and black swans. Routledge.
Aven, T. (2019) The science of risk analysis. Routledge

28. University of Stavanger, Norway
Risk, surprises and black swans: What is the appropriate management approach ? 
Terje Aven,
University of Stavanger, Norway
Loss Prevention Symposium 2019
June Delft

29. Extra

30. Risk = (A,C,U) = (A,U) + (C,U|A)
Risk description = (A’,C’,Q,K) = (A’,Q,K) + (C’,Q,K|A)
Risk = (A,C,U) = (A,U) + (C,U|A)
‘event contribution’ + vulnerability contribution
Q: ‘measure’ of uncertainty, K: knowledge
Q = P + Strength of knowledge judgements

31. Probability is used for all !
Subjective/knowledge-based probability
Imprecision interval
Uncertainty
Imprecision
Probability is used for all !
Variation
Frequentist probability, probability models

32. Subjective probability

33. Subjective/knowledge-based/judgmental probability
P(A) = 0.95
The assessor compares his/her uncertainty (degree of belief) about the event A to be true (occur) with drawing a red ball from an urn that contains 100 balls where 95 are red (Kaplan and Garrick 1981, Lindley, 2000).
P(A|K)

34. Imprecise probability
P(A) ≥
The assessor compares his/her uncertainty (degree of belief) about the event A to be true (occur) with drawing a red ball from an urn that contains 100 balls where 95 or more are red

35. Subjective probabilities
The probability of the event A, P(A), equals the amount of money that the assigner would be willing to put on the table if he/she would receive a single unit of payment in the case that the event A were to occur, and nothing otherwise …
Many other such interpretations exists (Ramsey, Savage …)
Common in the economic literature and among decision analysts
1930
Bruno de Finetti

36. Subjective probabilities
A mixture of uncertainty assessments and value judgments
It should not be used !!!

37. Risk 2020 1980 Risk is expected value
Risk = uncertainty/potential/possibility
Risk = objective probability distributions
Risk = event …
2020
1980
Risk is expected value
Risk is consequences and probability
Many applications uses definition of risk which is rjected by risk science
Risk is consequences and uncertainties
Aven, T. (2012) The risk concept. Historical and recent development trends. Reliability Engineering and System Safety. 115, 136–145.

38. Risk = (C,U)
Risk is the consequences of an activity and associated uncertainties
Risk is uncertainty about and severity of the consequences of an activity with respect to something that humans value
Society for Risk Analysis Glossary 2015
sra.org/resources