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Interactive Multicriteria Methods in Portfolio Decision Analysis

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Part of the book series: International Series in Operations Research & Management Science ((ISOR,volume 162))

Abstract

Decision Analysis is a constructive, learning process. This is particularly true of Portfolio Decision Analysis (PDA) where the number of elicitation judgements is typically very large and the alternatives under consideration are a combinatorial set and so cannot be listed and examined explicitly. Consequently, PDA is to some extent an interactive process. In this chapter we discuss what form that interactivity might take, discussing first of all how the process of asking for judgements should be staged and managed, and secondly what assumptions should be made about the substantive value models which the analyst assumes. To make the discussion concrete, we present two interactive procedures based on extended dominance concepts which assume linear additive and concave piecewise-linear additive value functions, respectively.

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Acknowledgements

The authors gratefully acknowledge the support of the RAMS grant from the Council of Rectors of Portuguese Universities and the British Council under the Treaty of Windsor programme, and COST Action Research grant IC0602 on Algorithmic Decision Theory. José Rui Figueira also acknowledges a RENOIR research grant from FCT (PTDC/GES/73853/2006) and financial support from LORIA (project: Multiple Criteria in ROAD). Alec Morton wishes to thank the Instituto Superior Técnico at the Technical University of Lisbon for the hospitality shown to him over several visits. We are grateful to Mara Airoldi and Jenifer Smith for permission to use the data on which our worked example is based. Thanks to Nuno Gonçalves for proof reading an earlier version and Ahti Salo and Jeff Keisler for helpful comments.

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Correspondence to Alec Morton .

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Argyris, N., Figueira, J.R., Morton, A. (2011). Interactive Multicriteria Methods in Portfolio Decision Analysis. In: Salo, A., Keisler, J., Morton, A. (eds) Portfolio Decision Analysis. International Series in Operations Research & Management Science, vol 162. Springer, New York, NY. https://doi.org/10.1007/978-1-4419-9943-6_5

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