TL;DR
This paper discusses the Logarithmic Least Squares method for the Analytic Hierarchy Process, providing an exact solution for individual and group decision-making scenarios, and linking it to divergence minimization.
Contribution
It introduces the LLS method for AHP and group-AHP, demonstrating its equivalence to divergence minimization and its ability to produce unique priority vectors.
Findings
LLS method yields exact, unique priority vectors for AHP.
For group-AHP, LLS minimizes weighted Kullback-Leibler divergences.
The approach enhances decision-making consistency in multi-criteria problems.
Abstract
The Analytic Hierarchy Process (AHP) is a procedure for establishing priorities in multi-criteria decision making problems. Here we discuss the Logarithmic Least Squares (LLS) method for the AHP and group-AHP, which provides an exact and unique solution for the priority vector. Also, we show that for the group-AHP, the LLS method is equivalent with the minimization of the weighted sum of generalized Kullback-Leibler divergences, between the group-priority vector and the priority vector of each expert.
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Taxonomy
TopicsMulti-Criteria Decision Making · Distributed Sensor Networks and Detection Algorithms · Bayesian Modeling and Causal Inference