Learning Multicriteria Utility Functions with Random Utility Models (original) (raw)

In traditional multicriteria decision analysis, decision maker evaluations or comparisons are considered to be error-free. In particular, algorithms like UTA*, ACUTA or UTA-GMS for learning utility functions to rank a set of alternatives assume that decision maker(s) are able to provide fully reliable training data in the form of e.g. pairwise preferences. In this paper we relax this assumption by attaching a likelihood degree to each ordered pair in the training set; this likelihood degree can be interpreted as a choice probability (group decision making perspective) or, alternatively, as a degree of confidence about pairwise preferences (single decision maker perspective). Since binary choice probabilities reflect order relations, the former can be used to train algorithms for learning utility functions. We specifically address the learning of piecewise linear additive utility functions through a logistic distribution; we conclude with examples and use-cases to illustrate the validity and relevance of our proposal.