Risk and uncertainty: a range of approaches

1. Risk and uncertainty: a range of approaches David Spiegelhalter winton professor of the public understanding of risk, university of cambridge SAPPUR, Bristol 2009 With thanks to Mike Pearson, Ian Short , Hauke Riesch, Owen Smith, Arciris Garay, etc etc

2. When building risk models, we can acknowledge uncertainty at different levels: Specific future events Quantities/parameters in a model Assumptions underlying the �best� model (both internal and external) Inadequacies of our �best� model

3. But what about unacknowledged uncertainties?

4. Classify into uncertainty about : Specific future events

6. Text? Numbers? Graphics? Animations? We may be concerned with what people � like understand / can reproduce are influenced by But These are not necessarily the same formats! Formats are influential (framing) People vary hugely in their preferences and understanding

7. Benefits of Tamiflu

9. Classify into uncertainty about: Specific future events Quantities in a model Missing data Fixed or random effects Parameters (including systematic biases)

10. UKPDS risk engine

11. Problems with confidence limits Suggestion that all points in interval are equally likely Media can report �up to X people might have Hepatitis C�

13. Express as a probability distribution, such as Bank of England fan charts for GDP

14. Bank of England �fan charts�

15. Hepatitis C prevalence in UK

16. Classify into uncertainty about : Specific future events Quantities in a model Assumptions underlying the �best� model (both internal and external) Which model selection criterion: AIC, BIC, DIC etc etc? Can we put weights on models? Does it make sense to talk of probabilities of models?

17. IPCC projections

18. Some epistemic uncertainties about swine flu Infectiousness (R0) Severity (�case fatality ratio�) Risk of recombination with other flu viruses Pattern of re-emergence etc

19. Government response? Standard approach in face of major epistemic uncertainties Play it safe Worst case planning scenarios 65,000 deaths now down to 19,000 (3rd Sept) Based on 30% clinical cases, 1/1000 die

20. NICE Complex cost-effectiveness models used to help decide which treatments NHS should fund Only interested in mean response Prior distributions to express parameter uncertainty Alternative competing models Informal acknowledgement of model inadequacy

21. Classify into uncertainty about : Specific future events Quantities in a model The structure of the �best� model (Recognised) inadequacies of our �best� model It�s only a �guide-book�, not the truth! Doubt about many assumptions But can we quantify these limitations?

22. �extra-model� uncertainties So far examined 4 levels of (potentially) quantifiable �intra-model� uncertainties What about �unquantifiable� extra-model sources? �unknown unknowns�: possibilities that have not been thought of unrecognised major scientific error unacknowledged cultural assumptions ambiguities in meaning unrecognised implicit value judgements as to what is �important� �indeterminacy� � human element beyond modelling (Wynne) Not a clear division with acknowledged inadequacies Is this the responsibility of the modeller or risk manager?

23. Some responses to �deeper� uncertainties Frank Knight Donald Rumsfeld (+ Zizek) Oliver Cromwell Brian Wynne Ulrich Beck / Anthony Giddens RAND Renn Etc etc [almost all concerned with environmental risk] Risk assessment to risk management

24. Frank Knight (1885-1972) Risk, Uncertainty, and Profit (1921) The essential fact is that 'risk' means in some cases a quantity susceptible of measurement, while at other times it is something distinctly not of this character; and there are far-reaching and crucial differences in the bearings of the phenomena depending on which of the two is really present and operating.... It will appear that a measurable uncertainty, or 'risk' proper, as we shall use the term, is so far different from an unmeasurable one that it is not in effect an uncertainty at all

25. Memorable quote #325

27. Brian Wynne (1992) Risk know the odds Uncertainty parameter/structure Ignorance �don�t know what we don�t know�, scientific errors Indeterminacy Causal chains open due to human element Emphasises that modelling conclusions are contingent upon truth of assumptions

28. Ulrich Beck / Anthony Giddens Risk Society �where we increasingly live on a high technological frontier which no one completely understands and which generates a diversity of possible futures �Manufactured risk� is risk created by the very progression of human development .. We often don�t really know what the risks are, let alone how to calculate them accurately in terms of probability tables �organised irresponsibility� � �a diversity of humanly created risks for which people and organisations are certainly �responsible� in a sense that they are its authors but where no one is held specifically accountable.

29. RAND (Bob Lempert etc) �Robust decision-making� Neither optimal nor precautionary Trades some optimal performance for less sensitivity to assumptions Satisficing over a wide range of futures Keeping options open

30. Ortwin Renn (2004)

32. Damocles � dams, nuclearCyclops � earthquakes, volcanoesPythia � sudden catastrophic climate events Pandora � unintended man-made effectCassandra � climate changeMedusa - EMR

33. �Conclusions� Statisticians/modellerd tend to have (or at least are taught) a rather narrow view of uncertainty Different communities approach the hierarchy of uncertainty from opposite ends Impact of different sources of uncertainty needs to be clearly communicated Robust use of quantitative methods, with due humility, is of huge value