Abstract
This paper reports an investigation linking Raiffa’s (1969) analysis of preference for multi-attributed alternatives with a technique for the elicitation of attribute dimensions known as the ‘repertory grid’.
Providing the assumptions underlying the repertory grid were reasonably upheld, people’s actual preference orderings were closely replicated by preference orderings predicted by a technique developed from Raiffa’s Model. The basis for such replication was examined by comparing the accuracy of predictions based on the Raiffa analysis with those based on several other analyses which did not incorporate the complete set of operations prescribed by the Raiffa Model. These comparisons indicated that: (i) Models for deriving uni-dimensional preference orderings from multi-dimensional data which do not take utilities into account were unsuccessful. (ii) Techniques for converting multi-dimensional representations of utility into preference orderings based on additive rules, such as that in the Raiffa Model yielded considerably better results than did a ‘selection’ rule. (iii) Given the adoption of an additive model, the use of a lottery technique for assessing the relative ‘value-wise importance’ of the various attribute dimensions resulted, in most cases, in an improvement of the predictive efficiency of the model over models which assign equal weights to all dimensions.
This paper is based on an investigation originally reported by Alison Humphreys as ‘A Study of People’s Preference Orderings for Multi-attributed Behavioural Outcomes’ (B.Sc. project, Department of Psychology, University College London).
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© 1975 D. Reidel Publishing Company, Dordrecht, Holland
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Humphreys, P., Humphreys, A. (1975). An Investigation of Subjective Preference Orderings for Multi-Attributed Alternatives. In: Wendt, D., Vlek, C. (eds) Utility, Probability, and Human Decision Making. Theory and Decision Library, vol 11. Springer, Dordrecht. https://doi.org/10.1007/978-94-010-1834-0_6
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DOI: https://doi.org/10.1007/978-94-010-1834-0_6
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