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Please use this identifier to cite or link to this item: https://dspace.lboro.ac.uk/2134/25831

Title: Capturing preferences for inequality aversion in decision support
Authors: Karsu, Ozlem
Morton, Alec
Argyris, Nikolaos
Keywords: Multiple criteria analysis
Equitable preferences
Generalized Lorenz dominance
Conditional dominance
Interactive approaches
Issue Date: 2017
Publisher: © Elsevier
Citation: KARSU, O., MORTON, A. and ARGYRIS, N., 2017. Capturing preferences for inequality aversion in decision support. European Journal of Operational Research, 264 (2), pp. 686-706.
Abstract: We investigate the situation where there is interest in ranking distributions (of income, of wealth, of health, of service levels) across a population, in which individuals are considered preferentially indistinguishable and where there is some limited information about social preferences. We use a natural dominance relation, generalized Lorenz dominance, used in welfare comparisons in economic theory. In some settings there may be additional information about preferences (for example, if there is policy statement that one distribution is preferred to another) and any dominance relation should respect such preferences. However, characterising this sort of conditional dominance relation (specifically, dominance with respect to the set of all symmetric increasing quasiconcave functions in line with given preference information) turns out to be computationally challenging. This challenge comes about because, through the assumption of symmetry, any one preference statement (“I prefer giving $100 to Jane and $110 to John over giving $150 to Jane and $90 to John”) implies a large number of other preference statements (“I prefer giving $110 to Jane and $100 to John over giving $150 to Jane and $90 to John”; “I prefer giving $100 to Jane and $110 to John over giving $90 to Jane and $150 to John”). We present theoretical results that help deal with these challenges and present tractable linear programming formulations for testing whether dominance holds between any given pair of distributions. We also propose an interactive decision support procedure for ranking a given set of distributions and demonstrate its performance through computational testing.
Description: This paper is closed access until 10th July 2019.
Version: Accepted for publication
DOI: 10.1016/j.ejor.2017.07.018
URI: https://dspace.lboro.ac.uk/2134/25831
Publisher Link: https://doi.org/10.1016/j.ejor.2017.07.018
ISSN: 0377-2217
Appears in Collections:Closed Access (Business)

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