Value-driven preference disaggregation analysis for uncertain preference information

Multiple criteria decision aiding aims to recommend decisions that are consistent with the decision-maker's value system by making trade-offs among multiple criteria to measure the performance of al-ternatives. A challenge is that decision-makers may be unable to provide certain information about their preferences. This study focuses on the preference disaggregation analysis of uncertain pairwise compar-isons between reference alternatives familiar to the decision-maker. Probabilistic linguistic preference re-lations are introduced to portray uncertain preference information, in which linguistic terms and sub-jective probabilities are used to express the intensities of preferences and corresponding belief degrees, respectively. The preference information is converted into constraints on value functions by triangular fuzzy numbers, and the value functions compatible with the preference information are estimated by linear programming to model the decision-maker's value system. We conduct a consistency analysis of probabilistic linguistic preference information as well as a robustness analysis of compatible value func-tions based on Monte Carlo simulations. Based on the data collected from Amazon.com, the proposed model is applied to laptop recommendations where the decision-maker is a single person and treadmill recommendations where the decision-maker is a group. The comparative analysis verifies the effective-ness of the proposed model in dealing with the uncertainties and intensities of preferences. (c) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license ( http://creativecommons.org/licenses/by-nc-nd/4.0/ )

Automated summary

Multiple criteria decision aiding aims to recommend decisions consistent with the decision-maker's preferences. This study introduces probabilistic linguistic preference relations and value functions to handle uncertain preference information and conducts consistency and robustness analysis. The empirical application verifies the effectiveness of the proposed model.