Representation and Inference of Lexicographic Preference Models and Their Variants

The authors propose two variants of lexicographic preference rules. They obtain the necessary and sufficient conditions under which a linear utility function represents a standard lexicographic rule, and each of the proposed variants, over a set of discrete attributes. They characterize the measurement properties of the parameters in the representations, propose a non-metric procedure for inferring lexicographic rules from pairwise-comparison data, and describe how the method can be used to construct hierarchical market structures using conjoint data. The authors illustrate the methods with data on personal-computer preferences. They report a simulation assessing the ability of the proposed inference procedure to distinguish among alternative lexicographic models, and between linear-compensatory and lexicographic models.