Representation and Inference of Lexicographic Preference Models and Their Variants

Abstract

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 then: (i) characterize the measurement properties of the parameters in the representations; (ii) propose a nonmetric procedure for inferring each lexicographic rule from pairwise comparisons of multiattribute alternatives; (iii) describe a method for distinguishing among different lexicographic rules, and between lexicographic and linear preference models; and (iv) suggest how individual lexicographic rules can be combined to describe hierarchical market structures. The authors illustrate each of these aspects using data on personal-computer preferences. They find that two-thirds of the subjects in the sample use some kind of lexicographic rule. In contrast, only one in five subjects use a standard lexicographic rule. This suggests that lexicographic rules are more widely used by consumers than one might have thought in the absence of the lexicographic variants described in the paper. The authors report a simulation assessing the ability of the proposed inference procedure to distinguish among alternative lexicographic models, and between linear-compensatory and lexicographic models.lexicographic preferences, noncompensatory preference models, linear models, optimization techniques, greedy algorithm, approximation algorithms, utility theory, conjoint analysis, hierarchical clustering, market segmentation, hierarchical market structures

From recognition to decisions: Extending and testing recognition-based models for multialternative inference

“…Even just a single exposure can lead people to consider buying novel brands (Coates, Butler, & Berry, 2004, 2006. The hypothesis that people rely on the recognition heuristic is also consistent with theories of consideration-set identification that assume noncompensatory heuristics (Kohli & Jedidi, 2007). In fact, the recognition heuristic was initially proposed as the first step in take-the-best (Gigerenzer & Goldstein, 1996), a noncompensatory heuristic for two-alternative decisions.…”

Heuristic Decision Making

“…When John Hauser, a proponent of conjoint analysis, began to test models of heuristics, he found to his surprise that sequential heuristics predict consumer choices well (Hauser et al 2009). Examples are decisions between computers (Kohli & Jedidi 2007) and smartphones (Yee et al 2007). In particular, heuristics are important early in the decision process to form a consideration set, which consists of eliminating most products from further consideration.…”

An Interdisciplinary Review of Research in Conjoint Analysis: Recent Developments and Directions for Future Research

“…Generally speaking, linear compensatory choice models do not address simplifying choice heuristics such as truncation and level focus that can result in an abrupt change in choice probability. Yet, noncompensatory simple heuristics are often more or at least equally accurate in predicting new data compared to linear models that are criticized for over-fitting the data [67,89,103]. While the linear utility model has been the mainstay in conjoint research, Bayesian methods, including data augmentation, can easily accommodate nonlinear models and can deal with irregularities in the likelihood surface [6].…”

“…While the linear utility model has been the mainstay in conjoint research, Bayesian methods, including data augmentation, can easily accommodate nonlinear models and can deal with irregularities in the likelihood surface [6]. Recently, Kohli and Jedidi [103] and Yee et al [190] propose dynamic programming methods (using greedy algorithm) to estimate lexicographic preference structures. Noncompensatory processes are particularly relevant in the context of consideration sets, an issue typically ignored by the traditional conjoint research (e.g., [67,91]).…”