CUT: A Multicriteria Approach for Concavifiable Preferences

Argyris, Nikolaos; Morton, Alec; Figueira, José Rui

doi:10.1287/opre.2014.1274

Cited by 22 publications

(14 citation statements)

References 38 publications

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“…Our method does not rely on the assumption of an additive evaluation function and can handle non-additive preference models unlike stochastic dominance based approaches. It would in principle be possible to build a theory of conditional dominance in an environment where non-additive social evaluation functions are allowed using the machinery of Argyris, Morton, and Figueira (2014) . But this method cannot capture symmetry and it is not obvious and it is beyond the scope of this paper how one should do it.…”

Section: Discussionmentioning

confidence: 99%

Capturing preferences for inequality aversion in decision support

Karsu

Morton²,

Argyris

2018

European Journal of Operational Research

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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 pref- erences. 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 an- other) 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 as- sumption 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

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Section: Discussionmentioning

confidence: 99%

Capturing preferences for inequality aversion in decision support

Karsu

Morton²,

Argyris

2018

European Journal of Operational Research

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“…These methods concentrated on eliciting weights and parameter values within models, but more recently attention has switched to interactively eliciting the shape and functional behaviour of the model itself (Greco et al 2012, Argyris et al 2014. We believe that such approaches will have a role to play in avoiding and defusing some of the tensions between the different, parallel inputs to the political process, focusing attention on where agreements lie.…”

Section: Further Development Of Sensitivity Techniquesmentioning

confidence: 99%

Decision Analysis and Political Processes

French

Argyris

2018

Decision Analysis

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Decision analysis has been with us for at least half a century. Over that time it has developed from a theoretical paradigm for individual rational choice to a practical tool for individuals, small groups and 'unitary' organisations, which helps them towards a sound decision-making mindful of the behavioural characteristics of individuals and group dynamics. Decision analysis has also shown its worth in the context of stakeholder engagement and public participation. The time is right for it to be more widely used in making societal decisions. However, to achieve that we need to realise that in many circumstances it will only be one input to the political process that leads to the actual decision. Recognising that suggests that our community of decision analysts needs to deconstruct our paradigm and attend more to communicating the result of the analysis in comparison with other inputs to the societal decision.

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“…possible rankings, the maximum possible entropy is (m 2 + m)/2, and a single ranking can be represented through a rank vector (e.g. for m = 3, y could be [1,3,2]). The maximum entropy corresponds to all m!…”

Section: Conditional Entropymentioning

confidence: 99%

Entropy-optimal weight constraint elicitation with additive multi-attribute utility models

Valkenhoef

Tervonen

2016

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We consider the elicitation of incomplete preference information for the additive utility model in terms of linear constraints on the weights. Eliciting incomplete preferences using holistic pair-wise judgments is convenient for the decision maker, but selecting the best pair-wise comparison is difficult. We propose a framework for comparing holistic preference elicitation questions based on their expected information gain, and introduce a procedure for approximating the optimal question. We extend the basic approach to generate reference alternatives that differ on only a few attributes, and to determine when further preference information is unlikely to reduce decision uncertainty. We present results from computational experiments that assess the performance of the procedure and assess the impact of limiting the number of attributes on which the reference alternatives differ. The tests show that the proposed method performs well, and when implemented in a decision support system it may substantially improve on-line elicitation using pair-wise comparisons.

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CUT: A Multicriteria Approach for Concavifiable Preferences

Cited by 22 publications

References 38 publications

Capturing preferences for inequality aversion in decision support

Capturing preferences for inequality aversion in decision support

Decision Analysis and Political Processes

Entropy-optimal weight constraint elicitation with additive multi-attribute utility models

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