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Article


Title

Bayesian ordinal regression for multiple criteria choice and ranking

Authors

[ 1 ] Instytut Informatyki, Wydział Informatyki i Telekomunikacji, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.3] Information and communication technology

Year of publication

2022

Published in

European Journal of Operational Research

Journal year: 2022 | Journal volume: vol. 299 | Journal number: no. 2

Article type

scientific article

Publication language

english

Keywords
EN
  • decision analysis
  • ordinal regression
  • Bayesian inference
  • stochastic acceptability analysis
  • additive value function
Abstract

EN We propose a novel Bayesian Ordinal Regression approach for multiple criteria choice and ranking problems. It employs an additive value function model to represent indirect Decision Maker’s (DM’s) preferences in the form of pairwise comparisons of reference alternatives. By defining a likelihood for the provided preference information and specifying a prior of the preference model, we apply the Bayesian rule to derive a posterior distribution over a set of all potential value functions, not necessarily compatible ones. This distribution emphasizes the potential differences in the abilities of these models to reconstruct the DM’s pairwise comparisons. Hence a distinctive character of our approach consists of characterizing the uncertainty in consequence of applying indirect preference information. We also employ a Markov Chain Monte Carlo algorithm, called the Metropolis-Hastings method, to summarize the posterior distribution of the value function model and quantify the outcomes of robustness analysis in the form of stochastic acceptability indices. The proposed approach’s performance is investigated in a thorough experimental study involving real-world and artificially generated datasets.

Date of online publication

28.08.2021

Pages (from - to)

600 - 620

DOI

10.1016/j.ejor.2021.09.028

URL

https://www.sciencedirect.com/science/article/abs/pii/S0377221721008031

Points of MNiSW / journal

140.0

Impact Factor

5.334 [List 2020]

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