Risk and Decisions

1. Risk and Decisions • Intuitively applied in every day life • All this is Bayesian • What makes for a good decision? • How do we know we made a bad one? • How do we approach rare events? • How many kinds of uncertainties are there? Consider all the above in a hierarchy of risk significance and the context of methods for controlling risk (insurance…)

2. Punditry and Uncertainty

3. Risk and Regulatory Decisions • How to manage other's risks? • How to do the risk-managing job for the “boss”? • Collective decision making (many bosses)? • Balancing competing risks? • Measures of success in decision making? • Robustness of decisions? • Tensions between regulator and licensee? • How we deal with special interests? Contrast for all these between observable and rare risks in the context of incentives and controls

4. Risk Assessment vs. Risk Management

5. Decomposition of the Decision ProblemDefense-in-depth

6. Framing a Decision • A proper starting point for making a decision under uncertainty is not Risk Assessment (RA) but the Framing of the Decision Problem (FDP) • The FDP is unique to classes of problems and involves a value system and a cognizance of the kinds of uncertainty involved • A proper Risk Assessment & Management Methodology (RAMM) is derivative of the so-defined FDP, and the kinds of driving uncertainties • There is a major distinction to be observed between empirically-based (E-B) RAMM and science-based (S-B) RAMM • E-B RAMM is for systems whose components are well-known and characterized statistically…the remaining issue is about “common cause failure” which can be severe for complex systems • S-B RAMM is for systems whose behavior is dependent upon physical phenomena and processes whose complexity overwhelms definitive mathematical modeling and prediction … the key issue now is model, not parameter uncertainty

7. R1 Risk: Basic Ideas and Definitions Decisions, Uncertainty, Anticipation 1/7 Hazard….vs…Realization of Hazard Idea of Chance Decision Making under Uncertainty: With Risk we measure uncertainty and inform Decisions Using Decision Theory we Structure Decisions and Valuate Risks/Benefits Defense-in-Depth: Prevention, Mitigation, Emergency Response A triple-nested hierarchy of reducing chance. Thinking of the Unthinkable: Piper Alpha, Seveso, Bhopal, etc Anticipation…… Systematic Consideration, Persistence, Expertise.

8. R1 Risk: Basic Ideas and Definitions Scenarios, Initiators, Sequences, Consequences 2/7 Independent Influences Our System Lead to Abnormal Behavior Various Sequences Possible Various Consequences Possible Initiator Scenarios….Sequences or Trajectories….Consequences { Sj }……………………..{ Ti }……………….{ Ci } Initiator plus Independent Influences

9. R1 Risk: Basic Ideas and Definitions Scenarios, Initiators, Sequences, Consequences 2a/7 Independent Influences Our System Lead to Abnormal Behavior Various Sequences Possible Various Consequences Possible Initiator • Trajectories are made up by different Realizations • Chance of a trajectory is the grouped occurrence rate over many Realizations • In Piper Alpha the wind blowing in a direction that made the helicopter pad unusable is part of the scenario that occurred

10. R1 Risk: Basic Ideas and Definitions Measure of Chance, Definition of Risk, Decisions 3/7 The most natural interpretation of Chance and Risk is Man’s ancient invention and practice of Games of Chance. First let us realize that all the above (scenarios, system, Influences, consequences) apply. Then we need to appreciate that in an honest game of chance the odds should be neutral, and chance can be quantitatively identified with Probability. R = { Sj , pi, Ci } j=1, 2 ..m i=1, 2, …n

11. R1 Risk: Basic Ideas and Definitions Measure of Chance, Definition of Risk, Decisions Examples 3a/7 The game of aleus (sheep’s knuckles)… aleatory uncertainty (uncertainty due to randomness). System: fair or loaded dice, platform, person tossing. Scenario: tossing energy, game, odds taken, limits (bets, time) Consequences: Amount of Gains/Losses Single toss of a fair coin. ph=pt =0.5 an even bet would be fair In the long run nobody wins. But for any finite length of playing we are subject to fluctuations Reality is determined by random fluctuations, and time controls. Infinite play Expected Risk, ER = 0.5 W + 0.5 (-L) =0 if W=L where W/L is amounts of money won/lost if heads or tails. When odds are not equal, we can even out for fairness by selecting values for W and L so the ER=0.

12. R1 Risk: Basic Ideas and Definitions Risk Valuation and Management. Communication. Stakeholders 4/7 R = { Sj , pi, Ci} j=1, 2 ..m i=1, 2, …n The set {S} must be complete. Any “leftover” scenarios, all together, must have a sufficiently low probability of occurrence….then called Residual Risk (RR). Risk Management: Modify system until R and RR are sufficiently low. But how low is low enough? Think of Games of Chance! Know the odds and chances of gains/losses. Here we also need to communicate with, and involve the stakeholders (recipients of Risk).

13. R1 Risk: Basic Ideas and Definitions Risk Valuation and Management. Communication. Stakeholders Examples 4a/7 R = { Sj , pi, Ci } j=1, 2 ..m i=1, 2, …n On scenario completeness: Nuclear Power Plants near airports? Far away? Drilling in a waste repository many years after closure? Catastrophic failure of an LNG storage vessel? Tanker? • On Risk Management in Nuclear Power Reactors: • Prevent meltdown (in ESBWR this is 10-7 per year) • Prevent containment failure • Limit health effects by exposure to radioactivity • Limit ground contamination