Explicit methods for attribute weighting in multi-attribute decision-making: a review study

Julio Pena*, Gonzalo Nápoles, Yamisleydi Salgueiro

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

Abstract

Attribute weighting is a key aspect when modeling multi-attribute decision analysis problems. Despite the large number of proposals reported in the literature, reaching a consensus on the most convenient method for a certain scenario is difficult, if not impossible. As a first contribution of this paper, we propose a categorization of existing methodologies, which goes beyond the current taxonomy (subjective, objective, hybrid). As a second contribution, supported by the new categorization, we survey and critically discuss the explicit weighting methods (which are closely related to the subjective ones). The critical discussion allows evaluating how much a solution can deviate from the expected one if no foresight is taken. As a final contribution, we summarize the main drawbacks from a global perspective and propose some insights to correct them. Such a discussion attempts to improve the reliability of decision support systems involving human experts.
Original languageEnglish
Pages (from-to)3127-3152
Number of pages26
JournalArtificial Intelligence Review
Volume53
Issue number5
DOIs
Publication statusPublished - 2020

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