Journal
EUROPEAN JOURNAL OF OPERATIONAL RESEARCH
Volume 299, Issue 2, Pages 600-620Publisher
ELSEVIER
DOI: 10.1016/j.ejor.2021.09.028
Keywords
Decision analysis; Ordinal regression; Bayesian inference; Stochastic acceptability analysis; Additive value function
Ask authors/readers for more resources
This paper proposes a novel Bayesian Ordinal Regression approach for multiple criteria choice and ranking problems. The approach utilizes an additive value function model to represent the Decision Maker's preferences in the form of pairwise comparisons. It applies the Bayesian rule to derive a posterior distribution over potential value functions and employs the Metropolis-Hastings method for summarizing the distribution and conducting robustness analysis.
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. (c) 2021 Elsevier B.V. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available