TY - JOUR
T1 - Explicit methods for attribute weighting in multi-attribute decision-making: a review study
AU - Pena, Julio
AU - Nápoles, Gonzalo
AU - Salgueiro, Yamisleydi
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.1007/s10462-019-09757-w
DO - 10.1007/s10462-019-09757-w
M3 - Article
SN - 1573-7462
VL - 53
SP - 3127
EP - 3152
JO - Artificial Intelligence Review
JF - Artificial Intelligence Review
IS - 5
ER -