1. Terje Aven University of Stavanger JRC, ISPRA 21 June 2013 Black swans in a risk context

2. 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

3. 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

4. Mediocristan (Normalistan) Extremistan (black swans) Nassim N. Taleb

5. 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.

6. 1 Prior density for p: the chance of a white swan 1

7. 1 1 What is the probability that p=1?

8. 1 Prior density for p: the chance of a white swan 1 What is the probability that p=1? It is zero!

9. 1 Prior density for p: the chance of a white swan 1 There is a positive fraction of black swans out there !

10. 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

11. 1 Prior density for p: the chance of a white swan 0.8 x 0.99 x 0.2

12. 1 Prior density for p: the chance of a white swan 0.8 x 0.99 x 0.2 the probability of a black swan occurring is close to zero

13. Depending on the assumptions made, we get completely different conclusions about the probability of a black swan occurring

14. 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)

15. Main problems with the probability based approach 15 4 Surprises occur 1 Assumptions can conceal important aspects of risk and uncertainties 3 The probabilities can be the same but the knowledge they are built on strong or weak 2 Presume existence of probability models

16. Probability- based Historical data Probability- based Historical data Knowledge dimension + + Surprises Risk perspective

17. P(head) = 0.5 P(attack) = 0.5 Strong knowledge Poor knowledge

18. John offers you a game: throwing a die ”1,2,3,4,5”: 6 ”6”: -24 What is your risk?

19. Risk (C,P): 6 5/6 -24 1/6 Is based on an important assumption – the die is fair

20. 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: … … “Background knowledge” Model: A very crude gas dispersion model is applied

21. Probability- based Historical data Probability- based Historical data Knowledge dimension + + Surprises Risk perspective

22. 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

23. 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.

24. 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.

25. 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

26. Government building Oslo 22 July 2011

27. Threats Known unknowns Unknown unknowns, black swans (A’, C’, Q, K)

28. 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 28

29. Statfjord A Do we have black swans here?

30. How to confront black swans Improved Risk Assessments Robustness Resilience Antifragility

31. How to confront black swans Improved Risk Assessments Robustness Resilience Antifragility Taleb: propose to stand our current approaches to prediction, prognostication, and risk management

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

33. 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

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

35. New way of thinking about risk 1 Risk analysis and management 2 Mindfulness (Collective) 2 Quality management 1 Concepts and principles - Preoccupation with failure - Reluctance to simplify -Sensitivity to operations -Commitment to resilience -Deference to expertise Aven and Krohn (2013) RESS.

37. Risk analysis Describing uncertainties, … Management review and judgment Decision Analysis Management Risk-informed decision making

38. Extra

39. Risk (A,C,U) A: Event, C: Consequences U: Uncertainty (C,U)

40. Risk description (A,C,U) Q: Measure of uncertainty (e.g. P) K: Background knowledge C’: Specific consequences (C,U) C’ Q K

41. 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

42. Risk analysis Cost-benefit analysis, Risk acceptance criteria … Management review and judgment Decision Analysis Management Risk-informed decision making