Modelling Human Decision Behaviour with Preference Learning

Preferences provide a means for specifying the desires of a decision maker (DM) in a declarative way. In this paper, based on a DM’s pairwise preferences, we infer the DM’s unique decision model. We capture (a) the attitudinal character, (b) relative criteria importance, and (c) the criteria interaction, all of which are specific to the DM. We make use of the preference-learning (PL) technique to induce predictive preference models from empirical data. Because PL is emerging as a new subfield of machine learning, we could use standard machine-learning methods to accomplish our learning objective. We consider the DM’s exemplary preference information in the form of pairwise comparisons between alternatives as the training information. The DM’s decision model is captured in terms of (a), (b), and (c), through the parameters of an attitudinal Choquet integral operator. The proposed learning approach is validated through an experimental study on 16 standard data sets. The superiority of the proposed method in terms of predictive accuracy and easier interpretability is shown both theoretically as well as empirically.