Journal ArticleDOI
A comparison of three weight elicitation methods: good, better, and best
TLDR
Three weight elicitation methods are shown to have very distinct "signatures", that is profiles relating weights to rank position, and people actually preferred using Max100 and DR rather than Min10, an important pragmatic consideration.Abstract:
This paper compares the properties and performance of three weight elicitation methods It is in effect a second round contest in which the Bottomley et al (2000) champion, direct rating (DR), locks horns with two new challengers People using DR rate each attribute in turn on a scale of 0–100, whilst people using Max100 first assign to the most important attribute(s) a rating of 100, and then rate the other attributes relative to it/them People using Min10 first assign the least important attribute(s) a rating of 10, and then rate the other attributes relative to it/them
The weights produced by Max100 were somewhat more test–retest reliable than DR Both methods were considerably more reliable than Min10 Using people's test–retest data as attribute weights on simulated alternative values in a multi-attribute choice scenario, the same alternative would be chosen on 91% of occasions using Max100, 87% of occasions using DR, but only 75% of occasions using Min10 Moreover, the three methods are shown to have very distinct “signatures�, that is profiles relating weights to rank position Finally, people actually preferred using Max100 and DR rather than Min10, an important pragmatic considerationread more
Citations
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Journal ArticleDOI
Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making
Ying-Ming Wang,Ying Luo +1 more
TL;DR: A correlation coefficient (CC) and standard deviation (SD) integrated approach for determining the weights of attributes in multiple attribute decision making (MADM) and a global sensitivity analysis to the weights determined are proposed to ensure the stability of the best decision alternative or alternative ranking.
Journal ArticleDOI
Strategic weight manipulation in multiple attribute decision making
Yucheng Dong,Yating Liu,Haiming Liang,Francisco Chiclana,Enrique Herrera-Viedma,Enrique Herrera-Viedma +5 more
TL;DR: In this paper, a series of mixed 0-1 linear programming models (MLPMs) are proposed to design a strategic attribute weight vector in order to defend against the strategic weight manipulation of the MADM problems.
Journal ArticleDOI
Rank Ordering Criteria Weighting Methods – a Comparative Overview
TL;DR: In this paper, a grant from the Polish National Sciencce Center (DEC-2011/03/======B/HS4/03857) was used to support the work of the authors.
Journal ArticleDOI
A framework for weighting of criteria in ranking stage of material selection process
TL;DR: In this paper, a framework for determining importance degree of criteria to overcome the shortcomings of this subject in material selection is presented, and the suggested framework covers the situation of interdependent relationship between the criteria which has not been surveyed in the material selection.
Journal ArticleDOI
State-of-the-Art Prescriptive Criteria Weight Elicitation
TL;DR: This paper provides a survey of state-of-the-art weight elicitation methods in a prescriptive setting and suggests several techniques for deriving criteria weights from preference statements.
References
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Journal ArticleDOI
Dimensions of Consumer Expertise
TL;DR: In this paper, a review of empirical results from the psychological literature in a way that provides a useful foundation for research on consumer knowledge is provided by two fundamental distinctions: consumer expertise is distinguished from product-related experience and five distinct aspects, or dimensions, of expertise are identified.
Book
Decision analysis and behavioral research
D. Von Winterfeldt,Ward Edwards +1 more
TL;DR: In this article, the authors present an integrative presentation of the principles of decision analysis in a behavioral context, including sensitivity analysis, value-utility distinction, multistage inference, attitudes toward risk, and attempt to make intuitive sense out of what have been treated in the literature as endemic biases and other errors of human judgement.
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Data Analysis and Regression: A Second Course in Statistics
TL;DR: The assumption is made in this volume devoted to data analysis and regression that the student has had a 1st course in statistics and that attitudes and approaches are more important than the techniques this book can teach.
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