Modeling Multimodal Continuous Heterogeneity in Conjoint Analysis — A Sparse Learning Approach

Consumers\u27 preferences can often be represented using a multimodal continuous heterogeneity distribution. One explanation for such a preference distribution is that consumers belong to a few distinct segments, with preferences of consumers in each segment being heterogeneous and unimodal. We propose an innovative approach for modeling such multimodal distributions that builds on recent advances in sparse learning and optimization. We apply the model to conjoint analysis where consumer heterogeneity plays a critical role in determining optimal marketing decisions. Our approach uses a two-stage divide-and-conquer framework, where we first divide the consumer population into segments by recovering a set of candidate segmentations using sparsity modeling, and then use each candidate segmentation to develop a set of individual-level heterogeneity representations. We select the optimal individual-level heterogeneity representation using cross-validation. Using extensive simulation experiments and three field data sets, we show the superior performance of our sparse learning model compared to benchmark models including the finite mixture model and the Bayesian normal component mixture model

Rationality or irrationality of preferences? A quantitative test of intransitive decision heuristics

In this paper, I present a comprehensive analysis of two decision heuristics that permit intransitive preferences: the lexicographic semiorder model and the similarity model. I also compare these two intransitive decision heuristics with transitive linear order models and two simple transitive heuristics. For each decision theory, I use two types of probabilistic specifications: distance-based models (which assume deterministic preferences and probabilistic response processes), and mixture models (which assume probabilistic preferences and deterministic response processes). I test 26 such probabilistic models on datasets from three different experiments using both frequentist and Bayesian order-constrained statistical methods. The frequentist goodness-of-fit tests show that the distance-based models with modal choice and the mixture models for all of the decision heuristics explain the participants' data fairly well for all stimulus sets. The frequentist analysis generates little evidence against transitivity. Model selection using Bayes factors suggests extensive heterogeneity across participants and stimulus sets

Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes

In this paper we propose a Bayesian nonparametric model for clustering partial ranking data. We start by developing a Bayesian nonparametric extension of the popular Plackett-Luce choice model that can handle an infinite number of choice items. Our framework is based on the theory of random atomic measures, with the prior specified by a completely random measure. We characterise the posterior distribution given data, and derive a simple and effective Gibbs sampler for posterior simulation. We then develop a Dirichlet process mixture extension of our model and apply it to investigate the clustering of preferences for college degree programmes amongst Irish secondary school graduates. The existence of clusters of applicants who have similar preferences for degree programmes is established and we determine that subject matter and geographical location of the third level institution characterise these clusters.Comment: Published in at http://dx.doi.org/10.1214/14-AOAS717 the Annals of Applied Statistics (http://www.imstat.org/aoas/) by the Institute of Mathematical Statistics (http://www.imstat.org

Inferring Population Preferences via Mixtures of Spatial Voting Models

Understanding political phenomena requires measuring the political preferences of society. We introduce a model based on mixtures of spatial voting models that infers the underlying distribution of political preferences of voters with only voting records of the population and political positions of candidates in an election. Beyond offering a cost-effective alternative to surveys, this method projects the political preferences of voters and candidates into a shared latent preference space. This projection allows us to directly compare the preferences of the two groups, which is desirable for political science but difficult with traditional survey methods. After validating the aggregated-level inferences of this model against results of related work and on simple prediction tasks, we apply the model to better understand the phenomenon of political polarization in the Texas, New York, and Ohio electorates. Taken at face value, inferences drawn from our model indicate that the electorates in these states may be less bimodal than the distribution of candidates, but that the electorates are comparatively more extreme in their variance. We conclude with a discussion of limitations of our method and potential future directions for research.Comment: To be published in the 8th International Conference on Social Informatics (SocInfo) 201

The impact of relative position and returns on sacrifice and reciprocity: an experimental study using individual decisions

We present a comprehensive experimental design that makes it possible to characterize other-regarding preferences and their relationship to the decision maker’s relative position. Participants are faced with a large number of decisions involving variations in the trade-offs between own and other’s payoffs, as well as in other potentially important factors like the decision maker’s relative position. We find that: (1) choices are responsive to the cost of helping and hurting others; (2) The weight a decision maker places on others’ monetary payoffs depends on whether the decision maker is in an advantageous or disadvantageous relative position; and (3) We find no evidence of reciprocity of the type linked to menu-dependence. The results of a mixture-model estimation show considerable heterogeneity in subjects’ motivations and confirm the absence of reciprocal motives. Pure selfish behavior is the most frequently observed behavior. Among the subjects exhibiting social preferences, social-welfare maximization is the most frequent, followed by inequality-aversion and by competitiveness

Generalized Group Profiling for Content Customization

There is an ongoing debate on personalization, adapting results to the unique user exploiting a user's personal history, versus customization, adapting results to a group profile sharing one or more characteristics with the user at hand. Personal profiles are often sparse, due to cold start problems and the fact that users typically search for new items or information, necessitating to back-off to customization, but group profiles often suffer from accidental features brought in by the unique individual contributing to the group. In this paper we propose a generalized group profiling approach that teases apart the exact contribution of the individual user level and the "abstract" group level by extracting a latent model that captures all, and only, the essential features of the whole group. Our main findings are the followings. First, we propose an efficient way of group profiling which implicitly eliminates the general and specific features from users' models in a group and takes out the abstract model representing the whole group. Second, we employ the resulting models in the task of contextual suggestion. We analyse different grouping criteria and we find that group-based suggestions improve the customization. Third, we see that the granularity of groups affects the quality of group profiling. We observe that grouping approach should compromise between the level of customization and groups' size.Comment: Short paper (4 pages) published in proceedings of ACM SIGIR Conference on Human Information Interaction and Retrieval (CHIIR'16