Elicitation of Preferences


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Elicitation Techniques - CABA

Buy Softcover. FAQ Policy. About this book Economists and psychologists have, on the whole, exhibited sharply different perspectives on the elicitation of preferences.

Elicitation of Preferences under Ambiguity - Research Database, The University of York

Show all. Rationality for Economists? Pages McFadden, Daniel et al. Volume 46 Issue 1 January-February Volume 45 Issue 6 November-December Volume 45 Issue 5 September-October Volume 45 Issue 4 July-August Volume 45 Issue 3 May-June Volume 45 Issue 2 March-April Volume 45 Issue 1 January-February Volume 44 Issue 6 November-December Volume 44 Issue 5 September-October Volume 44 Issue 4 July-August Volume 44 Issue 3 May-June Volume 44 Issue 2 March-April Volume 44 Issue 1 January-February Volume 43 Issue 6 November-December Volume 43 Issue 5 September-October Volume 43 Issue 4 July-August Volume 43 Issue 3 May-June Volume 43 Issue 2 March-April Volume 43 Issue 1 January-February Volume 42 Issue 6 November-December Volume 42 Issue 5 September-October Volume 42 Issue 4 July-August Volume 42 Issue 3 May-June Volume 42 Issue 2 March-April Volume 42 Issue 1 January-February Volume 41 Issue 6 November-December Volume 41 Issue 5 September-October Volume 41 Issue 4 July-August Volume 41 Issue 3 May-June Volume 41 Issue 2 March-April Volume 41 Issue 1 January-February Volume 40 Issue 6 November-December Volume 40 Issue 5 September-October Volume 40 Issue 4 July-August Volume 40 Issue 3-supplement-2 May-June Volume 40 Issue 3 May-June Volume 40 Issue 2 March-April Volume 40 Issue 1-supplement-1 January-February Volume 40 Issue 1 January-February Volume 39 Issue 6 November-December Volume 39 Issue 5 September-October Volume 39 Issue 4 July-August Volume 39 Issue 3 May-June Volume 39 Issue 2 March-April Volume 39 Issue 1 January-February Volume 38 Issue 6 November-December Volume 38 Issue 5 September-October Volume 38 Issue 4 July-August Volume 38 Issue 3 May-June Volume 38 Issue 2 March-April Volume 38 Issue 1 January-February Volume 37 Issue 6 November-December Volume 37 Issue 5 September-October Volume 37 Issue 4 July-August Volume 37 Issue 3 May-June Volume 37 Issue 2 March-April Volume 37 Issue 1 January-February Volume 36 Issue 6 November-December Volume 36 Issue 5 September-October Volume 36 Issue 4 July-August Volume 36 Issue 3 May-June Volume 36 Issue 2 March-April Volume 36 Issue 1 January-February Volume 35 Issue 6 November-December Volume 35 Issue 5 September-October Volume 35 Issue 4 July-August Volume 35 Issue 3 May-June Volume 35 Issue 2 March-April Volume 35 Issue 1 January-February Volume 34 Issue 6 November-December Volume 34 Issue 5 September-October Volume 34 Issue 4 July-August Volume 34 Issue 3 May-June Volume 34 Issue 2 March-April Volume 34 Issue 1 January-February Volume 33 Issue 6 November-December Volume 33 Issue 5 September-October Volume 33 Issue 4 July-August Volume 33 Issue 3 May-June Volume 33 Issue 2 March-April Volume 33 Issue 1 January-February Volume 32 Issue 6 November-December Volume 32 Issue 5 September-October Volume 32 Issue 4 July-August Volume 32 Issue 3 May-June Volume 32 Issue 2 March-April Volume 32 Issue 1 January-February Volume 31 Issue 6 November-December Volume 31 Issue 5 September-October Volume 31 Issue 4 July-August Volume 31 Issue 3 May-June Volume 31 Issue 2 March-April Volume 31 Issue 1 January-February Volume 30 Issue 6 November-December Volume 30 Issue 5 September-October Volume 30 Issue 4 July-August Volume 30 Issue 3 May-June Volume 30 Issue 2 March-April Volume 30 Issue 1 January-February Volume 29 Issue 6 November-December Volume 29 Issue 5 September-October Volume 29 Issue 4 July-August Volume 29 Issue 3 May-June Volume 29 Issue 2 March-April Volume 29 Issue 1 January-February Volume 28 Issue 6 November-December Volume 28 Issue 5 September-October Volume 28 Issue 4 July-August Volume 28 Issue 3-part-ii May-June Volume 28 Issue 3-part-i May-June Volume 28 Issue 2 March-April Volume 28 Issue 1 January-February Volume 27 Issue 6 November-December Volume 27 Issue 5 September-October Volume 27 Issue 4 July-August Volume 27 Issue 3 May-June Volume 27 Issue 2 March-April Volume 27 Issue 1 January-February Volume 26 Issue 6 November-December Volume 26 Issue 5 September-October Volume 26 Issue 4 July-August Volume 26 Issue 3 May-June Volume 26 Issue 2 March-April Volume 26 Issue 1 January-February Volume 25 Issue 6 November-December Volume 25 Issue 5 September-October Volume 25 Issue 4 July-August Volume 25 Issue 3 May-June Volume 25 Issue 2 March-April Volume 25 Issue 1 January-February Volume 24 Issue 6 November-December Volume 24 Issue 5 September-October Volume 24 Issue 4 July-August Volume 24 Issue 3 May-June Volume 24 Issue 2 March-April Volume 24 Issue 1 January-February Volume 23 Issue 6 November-December Volume 23 Issue 5 September-October Volume 23 Issue 4 July-August Volume 23 Issue 3 May-June Volume 23 Issue 2 March-April Volume 23 Issue 1 January-February Volume 22 Issue 6 November-December A common challenge is that uncertainties are typically not isolated but interlinked which introduces complex and often unexpected effects on the model output.

Therefore, dependence needs to be taken into account and modelled appropriately if simplifying assumptions, such as independence, are not sensible. Similar to the case of univariate uncertainty, which is described elsewhere in this book, relevant historical data to quantify a dependence model are often lacking or too costly to obtain.

DESIGN PREFERENCE ELICITATION: EXPLORATION AND LEARNING

Then, specifying dependence between the uncertain variables through expert judgement is the only sensible option. A structured and formal process to the elicitation is essential for ensuring methodological robustness.

This chapter addresses the main elements of structured expert judgement processes for dependence elicitation. Further, we review findings from the behavioural judgement and decision making literature on potential cognitive fallacies that can occur when assessing dependence as mitigating biases is a main objective of formal expert judgement processes. Given a practical focus, we reflect on case studies in addition to theoretical findings. Thus, this chapter serves as guidance for facilitators and analysts using expert judgement.

When combining the judgements of experts, there are potential correlations between the judgements. This could be as a result of individual experts being subject to the same biases consistently, different experts being subject to the same biases or experts sharing backgrounds and experience. In this chapter we consider the implications of these correlations for both mathematical and behavioural approaches to expert judgement aggregation.

We introduce the ideas of mathematical and behavioural aggregation and identify the possible dependencies which may exist in expert judgement elicitation.

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We describe a number of mathematical methods for expert judgement aggregation, which fall into two broad categories; opinion pooling and Bayesian methods. We qualitatively evaluate which of these methods can incorporate correlations between experts. We also consider behavioural approaches to expert judgement aggregation and the potential effects of correlated experts in this context. We discuss the results of an investigation which evaluated the correlation present in 45 expert judgement studies and the effect of correlations on the resulting aggregated judgements from a subset of the mathematical methods.

Elicitation of Preferences for Improvements in Ostomy Pouches – A Discrete Choice Experiment

We see that, in general, Bayesian methods which incorporate correlations outperform mathematical methods which do not. This chapter introduces key concepts in modelling preferences under uncertainty, focusing on utility elicitation, both in single and multiple attribute problems.


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We also discuss issues in relation with adversarial preference assessment. We illustrate all concepts with a case combining aspects of energy and homeland security. Target-oriented utility theory interprets the utility of a consequence as the probability of the consequence exceeding some benchmark random variable. This shifts the focus of utility assessment to the identification of the benchmark and the sources of uncertainty in that benchmark.

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Identification of the benchmark is often easy when the benchmark is based on a status quo outcome, a preferred outcome or an undesirable outcome. Benchmarks are generally easy to communicate and easy to track. Once identified, data and models can then be used to describe the uncertainty in the benchmark.

This approach can be useful in those applications where the utility function needs to be justified to others. Multiattribute Value Theory MAVT methods are perhaps the most intuitive multicriteria methods, and have the most theoretically well-understood basis. They are employ a divide-and-conquer modelling strategy in which the value of an option is conceptualised as a function typically the sum of the scores associated with the performance of the option on different attributes.

This chapter outlines the concept of preferential independence, which has a critical underpinning role of elicitation within the MAVT paradigm. I outline some of the main practical methods for arriving at the partial values and weighting them to arrive at an overall value score, including both traditional methods relying on cardinal assessment, and the MACBETH approach which uses qualitative difference judgements. A running example of a house choice problem is used to illustrate the different elicitation approaches. This chapter presents a new outlook on the well-known UTA method, which is devoted to the elicitation of values through the inference of multiple additive value models.

On top of that, it incorporates the latest theoretical developments, related to the robustness control of both the decision model and the surfacing decision aiding conclusions.


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An application example on job evaluation is elaborated as an educative example, while other potential areas for future use applications of the methodological framework are listed. The chapter concludes with several promising directions for future research.


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Outranking methods are a specific type of Multi-Criteria Decision Aiding methods. In decision processes using these methods, the analyst should interact with the decision maker in order to elicit values for the parameters that define a preference model. This can be done either directly or through a disaggregation procedure that infers parameter values from holistic judgements provided by the decision maker. Behavioral decision research has demonstrated that value and uncertainty judgments of decision makers and experts are subject to numerous biases. Individual biases can be either cognitive, such as overconfidence, or motivational, such as wishful thinking.

In addition, when making judgements in groups, decision makers and experts might be affected by group-level biases. These biases can create serious challenges to decision analysts, who need judgments as inputs to a decision or risk analysis model, because they can degrade the quality of the analysis. This chapter identifies individual and group biases relevant for decision and risk analysis and suggests tools for debiasing judgements for each type of bias.

Several different EKE protocols are reviewed in this volume, each with their pros and cons, but any is only as good as the quality of the experts and their judgments.

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