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

Black swans

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

Our knowledge is poor or wrong

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

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

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

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

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

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

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

P(A) = 0.01 P(A) ≤ 0.01

Expressing uncertainty Probability

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

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

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.

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

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

Kjerag bolt

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)

? Risk analysis and management Resilience analysis and management

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

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

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

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

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

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: 1-13. Open access. Aven, T. (2014) Risk, surprises and black swans. Routledge. Aven, T. (2019) The science of risk analysis. Routledge

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

Extra

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

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

Subjective probability

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)

Imprecise 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 or more are red

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

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

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.

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