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Elicitation

E-BookPDF1 - PDF WatermarkE-Book
542 Seiten
Englisch
Springer International Publishingerschienen am16.11.20171st ed. 2018
This book is about elicitation: the facilitation of the quantitative expression of subjective judgement about matters of fact, interacting with subject experts, or about matters of value, interacting with decision makers or stakeholders. It offers an integrated presentation of procedures and processes that allow analysts and experts to think clearly about numbers, particularly the inputs for decision support systems and models. This presentation encompasses research originating in the communities of structured probability elicitation/calibration and multi-criteria decision analysis, often unaware of each other's developments.

Chapters 2 through 9 focus on processes to elicit uncertainty from experts, including the Classical Method for aggregating judgements from multiple experts concerning probability distributions; the issue of validation in the Classical Method; the Sheffield elicitation framework; the IDEA protocol; approaches following the Bayesian perspective; the main elements of structured expert processes for dependence elicitation; and how mathematical methods can incorporate correlations between experts.

Chapters 10 through 14 focus on processes to elicit preferences from stakeholders or decision makers, including two chapters on problems under uncertainty (utility functions), and three chapters that address elicitation of preferences independently of, or in absence of, any uncertainty elicitation (value functions and ELECTRE).  Two chapters then focus on cross-cutting issues for elicitation of uncertainties and elicitation of preferences: biases and selection of experts.

Finally, the last group of chapters illustrates how some of the presented approaches are applied in practice, including a food security case in the UK; expert elicitation in health care decision making; an expert judgement based method to elicit nuclear threat risks in US ports; risk assessment in a pulp and paper manufacturer in the Nordic countries; and elicitation of preferences for crop planning in a Greek region.



Luis C. Dias obtained a degree in Informatics Engineering from the School of Science and Technology at the University of Coimbra in 1992, a Ph.D. in Management by the University of Coimbra in 2001, and Habilitation in Decision Aiding Science in 2013 in the same university. He is currently Associate Professor and Vice-Dean for Research the Faculty of Economics, University of Coimbra (FEUC), where he has been teaching courses on decision analysis, operations research, informatics, and related areas. He held temporary invited positions at the University Paris-Dauphine and the University of Vienna. Luis is also a researcher at the CeBER and INESC Coimbra R&D centers, a member of the coordination board of U.Coimbra's Energy for Sustainability Initiative, and currently a Vice-President of APDIO, the Portuguese Operational Research Society. He is on the Editorial Board of the EURO Journal on Decision Processes and Omega. His research interests include multicriteria decision analysis, performance assessment, group decision and negotiation support, decision support systems, and applications in the areas of energy and environment.

John Quigley has a Bachelor of Mathematics in Actuarial Science from the University of Waterloo, Canada and a PhD in Management Science from the University of Strathclyde, where he is currently Professor.  He is an Industrial Statistician with extensive experience in elicitation of expert judgment to support model development and quantification through subjective probability distributions, having worked closely over the past 25 years with various engineering organizations on problems concerned with risk and reliability.  John has been involved in consultancy and applied research projects with, for example, Aero-Engine Controls, Rolls Royce, Airborne Systems, BAE SYSTEMS and the Ministry of Defense (MOD). His collaborative work on Bayesian model development as part of the Reliability Enhancement Methods and Models (REMM) project is included in the industry standard for reliability growth analysis methods.  John is a tutor for the European Food Safety Agency (EFSA) on Expert Knowledge Elicitation (EKE) as well as being an Associate of the Society of Actuaries, a Chartered Statistician, and a member of the Safety and Reliability Society. 


Alec Morton has degrees from the University of Manchester and the University of Strathclyde. He has worked for Singapore Airlines, the National University of Singapore, and the London School of Economics, has held visiting positions at Carnegie Mellon University in Pittsburgh, Aalto University in Helsinki, and the University of Science and Technology of China (USTC) in Hefei, and has been on secondment at the National Audit Office. His main interests are in decision analysis and health economics.  Alec has been active in the INFORMS Decision Analysis Society and the OR Society. He is on the Editorial Board of Decision Analysis and is an Associate Editor for the EURO Journal on Decision Processes, the Transactions of the Institute of Industrial Engineers, and OR Spectrum. His research has won awards from the International Society for Pharmacoeconomics and Outcomes Research and the Society for Risk Analysis and from the the INFORMS Decision Analysis Society publication award.mehr
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KlappentextThis book is about elicitation: the facilitation of the quantitative expression of subjective judgement about matters of fact, interacting with subject experts, or about matters of value, interacting with decision makers or stakeholders. It offers an integrated presentation of procedures and processes that allow analysts and experts to think clearly about numbers, particularly the inputs for decision support systems and models. This presentation encompasses research originating in the communities of structured probability elicitation/calibration and multi-criteria decision analysis, often unaware of each other's developments.

Chapters 2 through 9 focus on processes to elicit uncertainty from experts, including the Classical Method for aggregating judgements from multiple experts concerning probability distributions; the issue of validation in the Classical Method; the Sheffield elicitation framework; the IDEA protocol; approaches following the Bayesian perspective; the main elements of structured expert processes for dependence elicitation; and how mathematical methods can incorporate correlations between experts.

Chapters 10 through 14 focus on processes to elicit preferences from stakeholders or decision makers, including two chapters on problems under uncertainty (utility functions), and three chapters that address elicitation of preferences independently of, or in absence of, any uncertainty elicitation (value functions and ELECTRE).  Two chapters then focus on cross-cutting issues for elicitation of uncertainties and elicitation of preferences: biases and selection of experts.

Finally, the last group of chapters illustrates how some of the presented approaches are applied in practice, including a food security case in the UK; expert elicitation in health care decision making; an expert judgement based method to elicit nuclear threat risks in US ports; risk assessment in a pulp and paper manufacturer in the Nordic countries; and elicitation of preferences for crop planning in a Greek region.



Luis C. Dias obtained a degree in Informatics Engineering from the School of Science and Technology at the University of Coimbra in 1992, a Ph.D. in Management by the University of Coimbra in 2001, and Habilitation in Decision Aiding Science in 2013 in the same university. He is currently Associate Professor and Vice-Dean for Research the Faculty of Economics, University of Coimbra (FEUC), where he has been teaching courses on decision analysis, operations research, informatics, and related areas. He held temporary invited positions at the University Paris-Dauphine and the University of Vienna. Luis is also a researcher at the CeBER and INESC Coimbra R&D centers, a member of the coordination board of U.Coimbra's Energy for Sustainability Initiative, and currently a Vice-President of APDIO, the Portuguese Operational Research Society. He is on the Editorial Board of the EURO Journal on Decision Processes and Omega. His research interests include multicriteria decision analysis, performance assessment, group decision and negotiation support, decision support systems, and applications in the areas of energy and environment.

John Quigley has a Bachelor of Mathematics in Actuarial Science from the University of Waterloo, Canada and a PhD in Management Science from the University of Strathclyde, where he is currently Professor.  He is an Industrial Statistician with extensive experience in elicitation of expert judgment to support model development and quantification through subjective probability distributions, having worked closely over the past 25 years with various engineering organizations on problems concerned with risk and reliability.  John has been involved in consultancy and applied research projects with, for example, Aero-Engine Controls, Rolls Royce, Airborne Systems, BAE SYSTEMS and the Ministry of Defense (MOD). His collaborative work on Bayesian model development as part of the Reliability Enhancement Methods and Models (REMM) project is included in the industry standard for reliability growth analysis methods.  John is a tutor for the European Food Safety Agency (EFSA) on Expert Knowledge Elicitation (EKE) as well as being an Associate of the Society of Actuaries, a Chartered Statistician, and a member of the Safety and Reliability Society. 


Alec Morton has degrees from the University of Manchester and the University of Strathclyde. He has worked for Singapore Airlines, the National University of Singapore, and the London School of Economics, has held visiting positions at Carnegie Mellon University in Pittsburgh, Aalto University in Helsinki, and the University of Science and Technology of China (USTC) in Hefei, and has been on secondment at the National Audit Office. His main interests are in decision analysis and health economics.  Alec has been active in the INFORMS Decision Analysis Society and the OR Society. He is on the Editorial Board of Decision Analysis and is an Associate Editor for the EURO Journal on Decision Processes, the Transactions of the Institute of Industrial Engineers, and OR Spectrum. His research has won awards from the International Society for Pharmacoeconomics and Outcomes Research and the Society for Risk Analysis and from the the INFORMS Decision Analysis Society publication award.
Details
Weitere ISBN/GTIN9783319650524
ProduktartE-Book
EinbandartE-Book
FormatPDF
Format Hinweis1 - PDF Watermark
FormatE107
Erscheinungsjahr2017
Erscheinungsdatum16.11.2017
Auflage1st ed. 2018
ReiheInternational Series in Operations Research & Management Science
Reihen-Nr.261
Seiten542 Seiten
SpracheEnglisch
IllustrationenVIII, 542 p. 106 illus., 71 illus. in color.
Artikel-Nr.2532071
Rubriken
Genre9200

Inhalt/Kritik

Inhaltsverzeichnis
1;Contents;5
2;About the Editors;7
3;1 Elicitation: State of the Art and Science;9
3.1;1.1 Conceptual Background;10
3.2;1.2 The Need for and Barriers to Elicitation;12
3.2.1;1.2.1 The Need for Elicitation of Judgement;12
3.2.1.1;1.2.1.1 Case 1. Swine Flu;13
3.2.1.2;1.2.1.2 Case 2. Airport Location;13
3.2.1.3;1.2.1.3 Case 3. Assessment of the Risk of Earthquake;14
3.2.1.4;1.2.1.4 Case 4. Radioactive Waste Management;15
3.2.2;1.2.2 Why do People Resist Expressing Their Uncertainty and Values Quantitatively?;15
3.2.2.1;1.2.2.1 Deep Uncertainty Case 1: Deepwater Horizon;16
3.2.2.2;1.2.2.2 Deep Uncertainty Case 2: The Fukushima Disaster;16
3.2.2.3;1.2.2.3 Sacred Values Case 1: the Approval of New Drugs;16
3.2.2.4;1.2.2.4 Sacred Values Case 2: The Concept of Capability in Military Planning;17
3.3;1.3 Overview of the Book;18
3.4;1.4 Conclusions and Future Directions;20
3.5;References;21
4;2 Elicitation in the Classical Model;23
4.1;2.1 Introduction;23
4.2;2.2 Classical Model Basics;24
4.2.1;2.2.1 Elicitation Questions;25
4.2.2;2.2.2 Calibration Score;26
4.2.3;2.2.3 Distribution and Discrimination of Calibration Score;30
4.2.4;2.2.4 Information Score;31
4.2.5;2.2.5 Weights;34
4.2.5.1;2.2.5.1 Global Weights;34
4.2.5.2;2.2.5.2 Itemized Weights;35
4.2.5.3;2.2.5.3 Optimized Weights;35
4.2.6;2.2.6 Summary;36
4.3;2.3 Finding Seed Variables;36
4.4;2.4 Elicitation Styles;40
4.5;2.5 Discussion;42
4.6;References;43
5;3 Validation in the Classical Model;45
5.1;3.1 Introduction: Why Validate?;45
5.2;3.2 Mathematical Pooling: Harmonic, Geometric and Arithmetic Means;49
5.2.1;3.2.1 Analysis;49
5.2.2;3.2.2 Performance on Real Expert Data;50
5.3;3.3 Review of Expert Judgment Cross Validation Research;54
5.3.1;3.3.1 ROAT Bias;54
5.4;3.4 Post 2006 Data Sets and Applications Documentation;61
5.5;3.5 Conclusion;61
5.6;References;65
6;4 SHELF: The Sheffield Elicitation Framework;68
6.1;4.1 Introduction;68
6.2;4.2 The Elicitation Framework;70
6.2.1;4.2.1 Exercise Specification;71
6.2.2;4.2.2 Expert Selection;73
6.2.3;4.2.3 Training in EKE Process;75
6.2.4;4.2.4 Information Sharing;76
6.2.5;4.2.5 Individual Judgements;78
6.2.6;4.2.6 Distribution Fitting;81
6.2.7;4.2.7 Aggregation of Distributions;82
6.2.8;4.2.8 Feedback on Distributions;84
6.2.9;4.2.9 Completing the Exercise;85
6.3;4.3 Notable Applications of the Framework;86
6.3.1;4.3.1 Healthcare and Medicine;86
6.3.2;4.3.2 Environmental Sciences;87
6.3.3;4.3.3 Other Applications;88
6.4;4.4 Extensions of the Framework;89
6.4.1;4.4.1 Elicitation for Multivariate Quantities;90
6.4.2;4.4.2 Distributed Experts;91
6.5;4.5 Discussion;96
6.6;References;98
7;5 IDEA for Uncertainty Quantification;101
7.1;5.1 Introduction;101
7.2;5.2 The IDEA Protocol;103
7.2.1;5.2.1 Eliciting Probabilities;104
7.2.2;5.2.2 Eliciting Quantiles of Probability Distributions;105
7.3;5.3 Data Analysis;106
7.3.1;5.3.1 Measures of Performance;108
7.3.1.1;5.3.1.1 Accuracy;108
7.3.1.2;5.3.1.2 Calibration;109
7.3.1.3;5.3.1.3 Informativeness;111
7.3.1.4;5.3.1.4 Correlated Expert Judgements;112
7.3.2;5.3.2 The Merits of Discussion;113
7.3.3;5.3.3 Prior Performance as a Guide to Future Performance;114
7.4;5.4 A Guide to Facilitating the IDEA Elicitation Protocol;115
7.4.1;5.4.1 Preparing for an Elicitation;116
7.4.1.1;5.4.1.1 Key Documents;116
7.4.1.2;5.4.1.2 The Questions;117
7.4.1.3;5.4.1.3 The Experts;117
7.4.1.4;5.4.1.4 The Facilitator;118
7.4.2;5.4.2 Implementing the IDEA Protocol;118
7.4.2.1;5.4.2.1 The Initial Meeting;118
7.4.2.2;5.4.2.2 The Elicitation;119
7.5;5.5 Discussion;120
7.6;References;121
8;6 Elicitation and Calibration: A Bayesian Perspective;124
8.1;6.1 Introduction;124
8.2;6.2 Context;125
8.3;6.3 The Bayesian Approach to Structured Expert Judgement;128
8.4;6.4 Survey of Bayesian Models for Structured Expert Judgement;132
8.5;6.5 Practical Procedures;137
8.6;6.6 Conclusions;142
8.7;References;143
9;7 A Methodology for Constructing Subjective Probability Distributions with Data;146
9.1;7.1 Introduction;146
9.2;7.2 On the Nature of the Problem;147
9.2.1;7.2.1 Motivating Industry Challenges;147
9.2.2;7.2.2 Generalisation of the Problem;148
9.2.3;7.2.3 Implications of Inference Principles for Elicitation;149
9.2.4;7.2.4 Principles of Empirical Bayes Inference;151
9.3;7.3 General Methodological Steps;152
9.3.1;7.3.1 Characterise the Population DGP;152
9.3.2;7.3.2 Identify Candidate Sample DGPs Matching Population;153
9.3.3;7.3.3 Sentence Empirical Data to Construct Sample DGPs;154
9.3.4;7.3.4 Select Probability Model for Population DGP;155
9.3.5;7.3.5 Estimate Model Parameters to Obtain Prior Distribution;155
9.4;7.4 Example Applications of the Elicitation Process;155
9.4.1;7.4.1 Assessing Uncertainty in Supplier Quality;156
9.4.1.1;7.4.1.1 Characterise the Population DGP;156
9.4.1.2;7.4.1.2 Identify Candidate Sample DGPs Matching Population;157
9.4.1.3;7.4.1.3 Sentence Empirical Data to Construct Sample DGPs;158
9.4.1.4;7.4.1.4 Select Probability Model for Population DGP;159
9.4.1.5;7.4.1.5 Estimate Model Parameters to Obtain Prior Distribution;160
9.4.2;7.4.2 Assessing Uncertainty About Reliability of an Engineering Design;163
9.4.2.1;7.4.2.1 Characterise the Population DGP;164
9.4.2.2;7.4.2.2 Identify Candidate Sample DGP Matching Population;165
9.4.2.3;7.4.2.3 Sentence Empirical Data to Construct Sample DGPs;166
9.4.2.4;7.4.2.4 Select Probability Model for Population DGP;168
9.4.2.5;7.4.2.5 Estimate Model Parameters to Obtain Prior Distribution;169
9.5;7.5 Summary and Conclusions;170
9.5.1;7.5.1 Methodological Steps;170
9.5.2;7.5.2 Effect of Sample Size on Prior Distribution;172
9.5.3;7.5.3 Caveats and Challenges;172
9.6;Appendix;172
9.7;References;174
10;8 Eliciting Multivariate Uncertainty from Experts: Considerations and Approaches Along the Expert Judgement Process;176
10.1;8.1 Introduction;177
10.1.1;8.1.1 Objective and Structure of the Chapter;177
10.1.2;8.1.2 Dependence in the Subjective Probability Context;178
10.2;8.2 Structured Expert Judgement Processes: An Overview;178
10.3;8.3 Biases and Heuristics for Dependence Elicitation;181
10.3.1;8.3.1 Causal Reasoning and Inference;183
10.3.2;8.3.2 Biased Dependence Elicitation: An Overview;184
10.3.3;8.3.3 Implications of Biases for the Elicitation Process;191
10.4;8.4 Elicitation Process: Preparation/Pre-elicitation;192
10.4.1;8.4.1 Problem Identification and Modelling Context;192
10.4.2;8.4.2 Choice of Elicited Parameters;195
10.4.3;8.4.3 Specification of Marginal Distributions;199
10.4.4;8.4.4 Training and Motivation;199
10.5;8.5 Elicitation Process: Elicitation;201
10.5.1;8.5.1 Knowledge and Belief Structuring;201
10.5.2;8.5.2 Quantitative Elicitation;203
10.6;8.6 Elicitation Process: Post-elicitation;204
10.6.1;8.6.1 Aggregation of Expert Judgements;205
10.6.2;8.6.2 Feedback and Robustness Analysis;208
10.7;8.7 Conclusions;209
10.8;References;210
11;9 Combining Judgements from Correlated Experts;216
11.1;9.1 Introduction;216
11.2;9.2 Mathematical and Behavioural Aggregation;218
11.3;9.3 Sources of Correlation;220
11.4;9.4 Mathematical Aggregation Methods;222
11.4.1;9.4.1 Bayesian Methods;222
11.4.1.1;Multivariate Normal model;222
11.4.1.2;Copula Model;223
11.4.1.3;Empirical Bayes Model;223
11.4.2;9.4.2 Opinion Pooling Methods;224
11.4.2.1;Cooke's Classical Method;224
11.4.2.2;The Moment Method;225
11.4.2.3;Babuscia and Cheung Approach;225
11.4.2.4;Non-parametric Approach;226
11.4.3;9.4.3 Correlations in Mathematical Approaches;226
11.5;9.5 Correlations in Behavioural Approaches;226
11.6;9.6 Evaluation of Mathematical Approaches;231
11.6.1;9.6.1 Prediction;232
11.6.2;9.6.2 Uncertainty;234
11.7;9.7 Summary and Future Directions;239
11.8;Appendix 1;241
11.9;Appendix 2;242
11.10;Appendix 3;243
11.11;References;244
12;10 Utility Elicitation;246
12.1;10.1 Introduction;246
12.2;10.2 (Single Attribute) Utility Elicitation;248
12.2.1;10.2.1 Basic Utility Concepts;248
12.2.2;10.2.2 An Elicitation Protocol;249
12.2.3;10.2.3 Risk Attitudes and Utility Functional Forms;251
12.2.4;10.2.4 Behavioural Issues;255
12.3;10.3 (Multi-Attribute) Utility Elicitation;255
12.3.1;10.3.1 Multi-Attribute Hierarchies;255
12.3.2;10.3.2 Multi-Attribute Utilities;258
12.3.3;10.3.3 Time Dependent Utilities;260
12.3.4;10.3.4 An Elicitation Protocol;262
12.4;10.4 Eliciting Adversarial Preferences;264
12.5;10.5 Discussion;266
12.6;References;267
13;11 Elicitation in Target-Oriented Utility;270
13.1;11.1 Introduction;270
13.2;11.2 Default Decisions;273
13.3;11.3 Goals;277
13.4;11.4 Screening;281
13.5;11.5 Expectations;286
13.6;11.6 Conclusions;288
13.7;References;290
14;12 Multiattribute Value Elicitation;292
14.1;12.1 Background;292
14.2;12.2 Preferential Independence: A Foundational Concept of Multiattribute Value Theory;294
14.2.1;12.2.1 Generic Representation Theorem;294
14.2.2;12.2.2 Representation Theorem for the Existence of a Representing Function;294
14.2.3;12.2.3 Representation Theorem for the Existence of an Additive Representing Function;295
14.3;12.3 The Decision Analysis Process;298
14.3.1;12.3.1 Design and Planning;299
14.3.1.1;12.3.1.1 Step 1. Establish the Aims of the Analysis;299
14.3.1.2;12.3.1.2 Step 2. Identify Decision Makers, Stakeholders, and Persons with Relevant Expertise;299
14.3.1.3;12.3.1.3 Step 3. Design the Intervention;300
14.3.2;12.3.2 Structuring the Model;300
14.3.2.1;12.3.2.1 Step 4. Identify the Options;300
14.3.2.2;12.3.2.2 Step 5. Identify the Criteria;301
14.3.2.3;12.3.2.3 Step 6. Score the Options on the Criteria;303
14.3.2.4;12.3.2.4 Step 7. Weight the Criteria;307
14.3.3;12.3.3 Analysing the Model;309
14.3.3.1;12.3.3.1 Step 8. Compute Overall Rankings;309
14.3.3.2;12.3.3.2 Step 9. Conduct Sensitivity Analysis;310
14.3.4;12.3.4 Troubleshooting;312
14.4;12.4 Concluding Remarks;314
14.5;References;315
15;13 Disaggregation Approach to Value Elicitation;317
15.1;13.1 Introduction;317
15.2;13.2 A New Look on the UTA Method;320
15.2.1;13.2.1 Problem Statement and Notation;320
15.2.2;13.2.2 The UTASTAR Algorithm;321
15.2.2.1;13.2.2.1 Step 1;321
15.2.2.2;13.2.2.2 Step 2;322
15.2.2.3;13.2.2.3 Step 3;322
15.2.2.4;13.2.2.4 Step 4;322
15.3;13.3 Interactive Disaggregation and Robustness Control;323
15.3.1;13.3.1 Bipolar Robustness Control;323
15.3.2;13.3.2 Robustness Indices;324
15.3.2.1;13.3.2.1 Robustness Indices on the Disaggregation Pole;326
15.3.2.2;13.3.2.2 Robustness Indices on the Aggregation Pole;327
15.4;13.4 An Application Example;329
15.4.1;13.4.1 Problem Presentation;329
15.4.2;13.4.2 Reference Set and Preference Elicitation;330
15.4.3;13.4.3 Preference Disaggregation Using UTASTAR Method;331
15.4.3.1;13.4.3.1 Step 1;331
15.4.3.2;13.4.3.2 Step 2;331
15.4.3.3;13.4.3.3 Step 3;332
15.4.3.4;13.4.3.4 Step 4;332
15.4.4;13.4.4 Bipolar Robustness Control;333
15.4.4.1;13.4.4.1 UTASTAR Re-Activation (2nd Iteration);334
15.4.4.2;13.4.4.2 Step 1;335
15.4.4.3;13.4.4.3 Step 2;335
15.4.4.4;13.4.4.4 Step 3;336
15.4.4.5;13.4.4.5 2nd Request for Feedback (3rd Iteration);336
15.4.4.6;13.4.4.6 3rd Request for Feedback (4th Iteration);339
15.4.4.7;13.4.4.7 4th Request for Feedback (5th Iteration);341
15.4.4.8;13.4.4.8 5th Request for Feedback (6th Iteration);342
15.5;13.5 Brief Overview of Existing Applications;344
15.6;13.6 Conclusions;347
15.7;References;348
16;14 Eliciting Multi-Criteria Preferences: ELECTRE Models;353
16.1;14.1 Introduction;353
16.2;14.2 Preference Models with ELECTRE;355
16.2.1;14.2.1 Outranking Relations for a Single Criterion;355
16.2.2;14.2.2 Concordance Relation;356
16.2.3;14.2.3 Discordance Relations;356
16.2.4;14.2.4 Valued Outranking Relations;358
16.2.5;14.2.5 Exploitation of the Outranking Relation;358
16.3;14.3 Direct Elicitation;360
16.3.1;14.3.1 Single-Criterion Concordance Parameters;360
16.3.2;14.3.2 Weights and Cutting Level;362
16.3.3;14.3.3 Discordance Parameters;364
16.3.3.1;14.3.3.1 Parameters Defining dj(a,b);364
16.3.3.2;14.3.3.2 Parameters Defining dj(a,b) for Relation S'(a,b) or S''(a,b);365
16.3.4;14.3.4 Profiles in Sorting Problems;366
16.4;14.4 Indirect Elicitation (Regression);368
16.4.1;14.4.1 Inferring Weights and Cutting Level from S' Outranking Statements;369
16.4.2;14.4.2 Inferring Different Parameters for Sorting Problems;371
16.5;14.5 Elicitation Process;373
16.5.1;14.5.1 Elicitation Sequence;373
16.5.2;14.5.2 Numerical Precision;374
16.6;14.6 Concluding Remarks;376
16.7;References;377
17;15 Individual and Group Biases in Value and Uncertainty Judgments;380
17.1;15.1 Introduction;380
17.2;15.2 Relevant Individual Biases;381
17.2.1;15.2.1 Relevant Individual Cognitive Biases;381
17.2.2;15.2.2 Relevant Individual Motivational Biases;384
17.3;15.3 Relevant Group Biases;386
17.4;15.4 Conclusions;390
17.5;References;391
18;16 The Selection of Experts for (Probabilistic) Expert Knowledge Elicitation;396
18.1;16.1 Introduction;396
18.2;16.2 Part I: Defining, Identifying and Measuring Expertise;397
18.2.1;16.2.1 Defining Expertise;397
18.2.1.1;16.2.1.1 Expertise as Superior Knowledge and/or Ability;397
18.2.1.2;16.2.1.2 Socially Defined Expertise;398
18.2.1.3;16.2.1.3 Properties of Experts;398
18.2.1.4;16.2.1.4 Expertise Continuum;399
18.2.1.5;16.2.1.5 Granularity and Scope of Expertise;400
18.2.1.6;16.2.1.6 Types of Expertise;400
18.2.2;16.2.2 Identifying Expertise;403
18.2.2.1;16.2.2.1 Substantive Expertise;404
18.2.2.2;16.2.2.2 Normative Expertise;404
18.2.2.3;16.2.2.3 Social Expertise;405
18.2.3;16.2.3 Measuring Expertise;406
18.2.3.1;16.2.3.1 Reliability and Validity of Measurement;406
18.2.3.2;16.2.3.2 Measuring Substantive Expertise;408
18.2.3.3;16.2.3.3 Measuring Normative Expertise;413
18.2.4;16.2.4 The Nature of Expertise in Judgement of Uncertain Quantities;417
18.2.4.1;16.2.4.1 Judging Quantities;417
18.2.4.2;16.2.4.2 Assessing Uncertainty;421
18.2.4.3;16.2.4.3 Limits of Expertise;423
18.2.4.4;16.2.4.4 Determinants of the Quality of (Probability) Judgements;426
18.2.5;16.2.5 How Many Experts and How Many Judgements from Each?;427
18.2.5.1;16.2.5.1 How Many Experts?;427
18.2.5.2;16.2.5.2 How Many Judgements?;430
18.3;16.3 Part II: A Structured Approach to the Selection of Experts;432
18.3.1;16.3.1 The EKE Process;432
18.3.2;16.3.2 From Problem Identification to Long-Listing (Stage 1);434
18.3.3;16.3.3 From Short-Listing to Wrap-Up (Stage 2);438
18.3.3.1;16.3.3.1 Screening, Short-Listing, and Weighting;438
18.3.3.2;16.3.3.2 Training, Retention and Documentation;439
18.4;16.4 Conclusions;441
18.5;References;442
19;17 Eliciting Probabilistic Judgements for Integrating Decision Support Systems;447
19.1;17.1 Introduction;447
19.1.1;17.1.1 A Probabilistic IDSS: Its Genesis and Functionality;448
19.1.2;17.1.2 The Running Example of Food Security;450
19.2;17.2 Framing a Complex Dynamic System;453
19.3;17.3 An Agreed Picture of the Whole Probability Process;457
19.3.1;17.3.1 An Overarching Structure and Common Language;457
19.3.2;17.3.2 Defining the Features and Variables in a Problem;459
19.3.2.1;17.3.2.1 What Are the Centre's Attributes and Time Frames?;459
19.3.2.2;17.3.2.2 Who Can Inform These Attributes and How?;460
19.3.2.3;17.3.2.3 Firming Up Meaningful Inputs and Outputs;460
19.3.2.4;17.3.2.4 Iterations to Provide Causal Chains;461
19.3.2.5;17.3.2.5 Example;461
19.3.3;17.3.3 Listing Measurements in a Causal Order;463
19.3.4;17.3.4 Bayesian Networks and Dynamic Bayesian Networks;465
19.3.4.1;17.3.4.1 Defining a Graph;465
19.3.4.2;17.3.4.2 Feasible Graphical Models and Simplifying Structures;466
19.4;17.4 Bayesian Networks for a Component Model: A Case Study;470
19.4.1;17.4.1 Development of the Bayesian Network Structure;470
19.4.2;17.4.2 Eliciting Conditional Probability Tables;473
19.5;17.5 Communicating the Results;475
19.6;17.6 Quality Control of Integrating systems, Diagnostics and Robustness;477
19.7;17.7 Conclusions;478
19.8;References;479
20;18 Expert Elicitation to Inform Health Technology Assessment;481
20.1;18.1 Introduction;481
20.2;18.2 Representing Uncertainty in Adoption Decisions;482
20.3;18.3 Distinguishing Features of Health Care Decision Making and Requirements for Expert Elicitation;484
20.4;18.4 Methods for Expert Elicitation in Healthcare Decision Making;485
20.5;18.5 Examples of Applications in Health Care Decision Making;487
20.5.1;18.5.1 Negative Pressure Wound Therapy (Soares et al. 2011);487
20.5.2;18.5.2 Photo Acoustic Mammography (PAM) (Haakma et al. 2014);492
20.6;18.6 Conclusions and Requirements for Further Research;494
20.7;References;495
21;19 Expert Judgment Based Nuclear Threat Assessment for Vessels Arriving in the US;497
21.1;19.1 Introduction;497
21.2;19.2 Questionnaires;499
21.3;19.3 Analysis;502
21.4;19.4 Results;503
21.5;19.5 Representative Threat Predictions;508
21.6;19.6 Conclusions;509
21.7;References;510
22;20 Risk Assessment Using Group Elicitation: Case Study on Start-up of a New Logistics System;512
22.1;20.1 Introduction;512
22.2;20.2 North European Transport Supply System;514
22.3;20.3 Pre Workshop Preparation;515
22.3.1;20.3.1 Planning of Workshop Process;515
22.3.1.1;20.3.1.1 Definition of the Likelihood Scale;516
22.3.1.2;20.3.1.2 Definition of Consequence Types and Scales;516
22.3.2;20.3.2 Selection of Experts;518
22.4;20.4 Computer Assisted Expert Workshop;518
22.4.1;20.4.1 Introduction of the Workshop;519
22.4.2;20.4.2 Hazard Identification;520
22.4.3;20.4.3 Risk Estimation;521
22.4.3.1;20.4.3.1 Risk Index;522
22.4.3.2;20.4.3.2 Identification of Top Priority Risks;525
22.4.3.3;20.4.3.3 Top Priority Risks;525
22.4.4;20.4.4 Risk Control Ideas;526
22.4.5;20.4.5 Conclusion of the Workshop;526
22.5;20.5 Post Workshop Actions;526
22.6;20.6 Lessons Learned;527
22.7;References;528
23;21 Group Decision Support for Crop Planning: A Case Study to Guide the Process of Preferences Elicitation;529
23.1;21.1 Introduction;530
23.2;21.2 Problem Structuring;530
23.2.1;21.2.1 Problem Types;531
23.2.1.1;21.2.1.1 Crop Choice (Farm Level);531
23.2.1.2;21.2.1.2 Crop Acreage;532
23.3;21.3 Case Study;533
23.3.1;21.3.1 Background;533
23.3.2;21.3.2 Process Overview;533
23.4;21.4 The Process Instantiation;535
23.5;21.5 Discussion;539
23.6;References;540mehr

Autor

Luis C. Dias obtained a degree in Informatics Engineering from the School of Science and Technology at the University of Coimbra in 1992, a Ph.D. in Management by the University of Coimbra in 2001, and Habilitation in Decision Aiding Science in 2013 in the same university. He is currently Associate Professor and Vice-Dean for Research the Faculty of Economics, University of Coimbra (FEUC), where he has been teaching courses on decision analysis, operations research, informatics, and related areas. He held temporary invited positions at the University Paris-Dauphine and the University of Vienna. Luis is also a researcher at the CeBER and INESC Coimbra R&D centers, a member of the coordination board of U.Coimbra's Energy for Sustainability Initiative, and currently a Vice-President of APDIO, the Portuguese Operational Research Society. He is on the Editorial Board of the EURO Journal on Decision Processes and Omega. His research interests include multicriteria decision analysis, performance assessment, group decision and negotiation support, decision support systems, and applications in the areas of energy and environment.

John Quigley has a Bachelor of Mathematics in Actuarial Science from the University of Waterloo, Canada and a PhD in Management Science from the University of Strathclyde, where he is currently Professor.  He is an Industrial Statistician with extensive experience in elicitation of expert judgment to support model development and quantification through subjective probability distributions, having worked closely over the past 25 years with various engineering organizations on problems concerned with risk and reliability.  John has been involved in consultancy and applied research projects with, for example, Aero-Engine Controls, Rolls Royce, Airborne Systems, BAE SYSTEMS and the Ministry of Defense (MOD). His collaborative work on Bayesian model development as part of the Reliability Enhancement Methods and Models (REMM) project is included in the industry standard for reliability growth analysis methods.  John is a tutor for the European Food Safety Agency (EFSA) on Expert Knowledge Elicitation (EKE) as well as being an Associate of the Society of Actuaries, a Chartered Statistician, and a member of the Safety and Reliability Society. 

Alec Morton has degrees from the University of Manchester and the University of Strathclyde. He has worked for Singapore Airlines, the National University of Singapore, and the London School of Economics, has held visiting positions at Carnegie Mellon University in Pittsburgh, Aalto University in Helsinki, and the University of Science and Technology of China (USTC) in Hefei, and has been on secondment at the National Audit Office. His main interests are in decision analysis and health economics.  Alec has been active in the INFORMS Decision Analysis Society and the OR Society. He is on the Editorial Board of Decision Analysis and is an Associate Editor for the EURO Journal on Decision Processes, the Transactions of the Institute of Industrial Engineers, and OR Spectrum. His research has won awards from the International Society for Pharmacoeconomics and Outcomes Research and the Society for Risk Analysis and from the the INFORMS Decision Analysis Society publication award.