Methods Using Surrogate Weights

Adiel Teixeira de Almeida (), Eduarda Asfora Frej (), Lucia Reis Peixoto Roselli (), Jonatas Araújo de Almeida (), Ana Paula Cabral Seixas Costa () and Danielle Costa Morais ()
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Adiel Teixeira de Almeida: Federal University of Pernambuco
Eduarda Asfora Frej: Federal University of Pernambuco
Lucia Reis Peixoto Roselli: Federal University of Pernambuco
Jonatas Araújo de Almeida: Federal University of Pernambuco
Ana Paula Cabral Seixas Costa: Federal University of Pernambuco
Danielle Costa Morais: Federal University of Pernambuco

Chapter 5 in Multi-Criteria Decision Making with Partial Preference Information, 2026, pp 79-90 from Springer

Abstract: Abstract This chapter presents surrogate weighting techniques and their application in MCDM/A methods using partial preference information, considering both additive models related to Multi-Attribute Value Theory (MAVT) and also outranking methods, with emphasis on the former. Using Surrogate Weights is a useful approach for dealing with situations in which the Decision Maker (DM) either does not want to or is not able to elicit the values of criteria weights. Instead, a minimum amount of information is provided: commonly, the ranking of criteria weights, which may consider in some methods the use of some cardinal information. To conclude, a discussion is entered into about possible advantages and disadvantages of using surrogate weights when solving a multi-criteria decision problem under partial preference information.

Date: 2026
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DOI: 10.1007/978-3-032-19284-4_5

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