Elicitation is the process of extracting expert knowledge about some unknown quantity or quantities, and formulating that information as a probability distribution. Elicitation is important in situations, such as modelling the safety of nuclear installations or assessing the risk of terrorist attacks, where expert knowledge is essentially the only source of good information. It also plays a major role in other contexts by augmenting scarce observational data, through the use of Bayesian statistical methods. However, elicitation is not a simple task, and practitioners need to be aware of a wide range of research findings in order to elicit expert judgements accurately and reliably. Uncertain Judgements introduces the area, before guiding the reader through the study of appropriate elicitation methods, illustrated by a variety of multi-disciplinary examples. This is achieved by: Presenting a methodological framework for the elicitation of expert knowledge incorporating findings from both statistical and psychological research. Detailing techniques for the elicitation of a wide range of standard distributions, appropriate to the most common types of quantities. Providing a comprehensive review of the available literature and pointing to the best practice methods and future research needs. Using examples from many disciplines, including statistics, psychology, engineering and health sciences. Including an extensive glossary of statistical and psychological terms. An ideal source and guide for statisticians and psychologists with interests in expert judgement or practical applications of Bayesian analysis, Uncertain Judgements will also benefit decision-makers, risk analysts, engineers and researchers in the medical and social sciences.

Elicitation is the process of extracting expert knowledge aboutsome unknown quantity or quantities, and formulating thatinformation as a probability distribution. Elicitation is importantin situations, such as modelling the safety of nuclearinstallations or assessing the risk of terrorist attacks, whereexpert knowledge is essentially the only source of goodinformation. It also plays a major role in other contexts byaugmenting scarce observational data, through the use of Bayesianstatistical methods. However, elicitation is not a simple task, andpractitioners need to be aware of a wide range of research findingsin order to elicit expert judgements accurately and reliably.Uncertain Judgements introduces the area, before guiding thereader through the study of appropriate elicitation methodsillustrated by a variety of multi-disciplinary examples.This is achieved by:* Presenting a methodological framework for the elicitation ofexpert knowledge incorporating findings from both statistical andpsychological research.* Detailing techniques for the elicitation of a wide range ofstandard distributions, appropriate to the most common types ofquantities.* Providing a comprehensive review of the available literatureand pointing to the best practice methods and future researchneeds.* Using examples from many disciplines, including statisticspsychology, engineering and health sciences.* Including an extensive glossary of statistical andpsychological terms.An ideal source and guide for statisticians and psychologistswith interests in expert judgement or practical applications ofBayesian analysis, Uncertain Judgements will also benefitdecision-makers, risk analysts, engineers and researchers in themedical and social sciences.

Preface.1. Fundamentals of Probability and Judgement.1.1 Introduction.1.2 Probability and elicitation.1.3 Uncertainty and the interpretation of probability.1.4 Elicitation and the psychology of judgement.1.5 What use are such judgements?1.6 Conclusions.2. The Elicitation Context.2.1 How and who?2.2 What is an expert?2.3 The elicitation process.2.4 Conventions in Chapters 3 to 9.2.5 Conclusions.3. The Psychology of Judgement Under Uncertainty.3.1 Introduction.3.2 Understanding the task and the expert.3.3 Understanding research on human judgement.3.4 The heuristic and biases research programme. 3.4.6 Theaffect heuristic.3.5 Experts and expertise.3.6 Three meta theories of judgement.3.7 Conclusions.4. The Elicitation of Probabilities.4.1 Introduction.4.2 The Calibration of Subjective Probabilities.4.3 The calibration in subjective probabilities: theories andexplanations.4.4 Representations and methods.4.5 Debiasing.4.6 Conclusions.5. Eliciting Distributions - General.5.1 From probabilities to distributions.5.2 Eliciting univariate distributions.5.3 Eliciting multivariate distributions.5.4 Uncertainty and imprecision.5.5 Conclusions.6. Eliciting and Fitting a Parametric Distribution.6.1 Introduction.6.2 Outline of this chapter.6.3 Eliciting opinion about a proportion.6.4 Eliciting opinion about a general scalar quantity.6.5 Eliciting opinion about a set of proportions.6.6 Eliciting opinion about the parameters of amultivariate normal distribution.6.7 Eliciting opinion about the parameters of a linearregression model.6.8 Eliciting opinion about the parameters of a generalizedlinear model.6.9 Elicitation methods for other problems.6.10 Deficiencies in existing research.6.11 Conclusions.7. Eliciting Distributions - Uncertainty andImprecision.7.1 Introduction.7.2 Imprecise probabilities.7.3 Incomplete information.7.4 Summary.7.5 Conclusions.8. Evaluating Elicitation.8.1 Introduction.8.2 Scoring rules.8.3 Coherence, feedback and overfitting.8.4 Conclusions.9. Multiple Experts.9.1 Introduction.9.2 Mathematical aggregation.9.3 Behavioural aggregation.9.4 Discussion.9.5 Elicitation practice.9.6 Research questions.10. Published Examples of the Formal Elicitation of ExpertOpinion.10.1 Some applications.10.2 An example of an elicitation interview - eliciting enginesales.10.3 Medicine.10.4 The nuclear industry.10.5 Veterinary science.10.6 Agriculture.10.7 Meteorology.10.8 Business studies, economics and finance.10.9 Other professions.10.10 Other examples of the elicitation of subjectiveprobabilities.11. Guidance on Best Practice.12. Areas for Research.Glossary.Bibliography.Author Index.Index.