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.
Autorentext
Professor Anthony O'Hagan is the Director of The Centre for Bayesian Statistics in Health Economics at the University of Sheffield. The Centre is a collaboration between the Department of Probability and Statistics and the School of Health and Related Research (ScHARR). The Department of Probability and Statistics is internationally respected for its research in Bayesian statistics, while ScHARR is one of the leading UK centres for economic evaluation.
Prof O'Hagan is an internationally leading expert in Bayesian Statistics.
Co-authors:
Professor Paul Gathwaite - Open University, Prof of Statistics, Maths and Computing
Dr Jeremy Oakley - Sheffield University
Professor John Brazier - Director of Health Economics Group, University of Sheffield
Dr Tim Rakow - University of Essex, Psychology Department
Dr Alireza Daneshkhah - University of Sheffield, Medical Statistics Department
Dr Jim Chilcott - School of Health Research, University of Sheffield, Department of OR
Klappentext
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.
Inhalt
Preface xi
1 Fundamentals of Probability and Judgement 1
1.1 Introduction 1
1.2 Probability and elicitation 1
1.2.1 Probability 1
1.2.2 Random variables and probability distributions 3
1.2.3 Summaries of distributions 5
1.2.4 Joint distributions 7
1.2.5 Bayes' Theorem 8
1.2.6 Elicitation 9
1.3 Uncertainty and the interpretation of probability 10
1.3.1 Aleatory and epistemic uncertainty 10
1.3.2 Frequency and personal probabilities 11
1.3.3 An extended example 12
1.3.4 Implications for elicitation 14
1.4 Elicitation and the psychology of judgement 14
1.4.1 Judgement - absolute or relative? 15
1.4.2 Beyond perception 18
1.4.3 Implications for elicitation 20
1.5 Of what use are such judgements? 20
1.5.1 Normative theories of probability 21
1.5.2 Coherence 21
1.5.3 Do elicited probabilities have the desired interpretation? 22
1.6 Conclusions 24
1.6.1 Elicitation practice 24
1.6.2 Research questions 24
2 The Elicitation Context 25
2.1 How and who? 25
2.1.1 Choice of format 25
2.1.2 What is an expert? 26
2.2 The elicitation process 27
2.2.1 Roles within the elicitation process 28
2.2.2 A model for the elicitation process 28
2.3 Conventions in Chapters 3 to 9 31
2.4 Conclusions 31
2.4.1 Elicitation practice 31
2.4.2 Research question 31
3 The Psychology of Judgement Under Uncertainty 33
3.1 Introduction 33
3.1.1 Why psychology? 33
3.1.2 Chapter overview 34
3.2 Understanding the task and the expert 35
3.2.1 Cognitive capabilities: the proper view of human information processing? 35
3.2.2 Constructive processes: the proper view of the process? 36
3.3 Understanding research on human judgement 37
3.3.1 Experts versus the rest: the proper focus of research? 37
3.3.2 Early research on subjective probability: 'conservatism' in Bayesian probability revision 38
3.4 The heuristics and biases research programme 38
3.4.1 Availability 39
3.4.2 Representativeness 41
3.4.3 Do frequency representations remove the biases attributed to availability and representativeness? 46
3.4.4 Anchoring-and-adjusting 47
3.4.5 Support theory 49
3.4.6 The affect heuristic 51
3.4.7 Critique of the heuristics and biases approach 52
3.5 Experts and expertise 52
3.5.1 The heuristics and biases approach 53
3.5.2 The cognitive science approach 53
3.5.3 'The middle way' 54
3.6 Three meta-theories of judgement 55
3.6.1 The cognitive continuum 56
3.6.2 The inside versus the outside view 56
3.6.3 The naive intuitive statistician metaphor 58
3.7 Conclusions 58
3.7.1 Elicitation practice 58
3.7.2 Research questions 59
4 The Elicitation of Probabilities 61
4.1 …