Inducing probability distributions on the set of value functions by Subjective Stochastic Ordinal Regression

Ordinal regression methods of Multiple Criteria Decision Aiding (MCDA) take into account one, several, or all value functions compatible with the indirect preference information provided by the Decision Maker (DM). When dealing with multiple criteria ranking problems, typically, this information is a series of holistic and certain judgments having the form of pairwise comparisons of some reference alternatives, indicating that alternative a is certainly either preferred to or indifferent with alternative b. In some decision situations, it might be useful, however, to additionally account for uncertain pairwise comparisons interpreted in the following way: although the preference of a over b is not certain, it is more credible than preference of b over a. To handle certain and uncertain preference information, we propose a new approach that builds a probability distribution over the space of all value functions compatible with the DM’s certain holistic judgments. This distribution is parametrized to reflect different credibility levels of the supplied preferences. A didactic example shows the applicability of the proposed approach. Email addresses: [email protected] (Salvatore Corrente), [email protected] (Salvatore Greco), [email protected] (Mi losz Kadziński), [email protected] (Roman S lowiński ) [Post-print] Please cite as: Corrente Salvatore, Greco Salvatore, Kadzinski Milosz, S lowiński Roman, Inducing probability distributions on the set of value functions by Subjective Stochastic Ordinal Regression, Knowledge-Based Systems, 112, 26-36