Dynamic Heterogeneous Choice Heuristics: A Bayesian Hidden Markov Mixture Model Approach

Standard choice models implicitly assume that consumers, in order to maximize their expected utilities, compare each of the alternatives in their choice sets in terms of all available attributes. Consumer-level utility functions are frequently taken as linear, and overwhelmingly so as compensatory. However, due to limitations in information process capacity, characteristics of choice task environment and other internal or external constraints, consumers may search for satisfying alternatives rather than optimal ones by invoking other non-compensatory heuristics which free them from arduous attribute-by-attribute comparison. The question arises as to how often these non-compensatory rules are applied, and whether researchers can detect them using only standard data sources. This study aims to address two main issues regarding consumers use of decision-rules and heuristics in the real world: (1) whether they are heterogeneous across consumers and (2) whether they are changing for individual consumers over time. To these ends, we extend the standard linear compensatory rule assumption to more faithfully capture dynamic heuristic usage for each consumer. There are three reference heuristics studied in this paper, the well-known linear compensatory, disjunctive and conjunctive rules. Conditional on this known set of possible heuristics, a dynamic heterogeneous hidden Markov mixture choice model is developed to capture heuristic dynamics at the individual-level. When estimated on detergent scanner data, the proposed model offers strong evidence supporting both heterogeneity and dynamics in heuristics usage

Identifying Subject-Specific Relevant Explanatory Variables in Choice-Based Conjoint Studies

It is customary in conjoint studies to introduce the same set of potential explanatory variables for each subject, so as best to allow any possible trade-offs to be made. However, this presumption can mask the possibility of some subjects considering only a subset of the presented attributes. Moreover, such subsets of relevant attributes can vary considerably across the population. This paper presents a model which allows researchers to identify relevant explanatory variables for each subject separately. This is accomplished via a solution to the well-known variable selection problem in the context of discrete choice models; the proposed solution can be widely applied throughout choice studies and in fact to other response types, such as ratings, direct paired comparisons, and ranks, with appropriate changes in likelihood function. When estimated on a choice-based conjoint data for dial-readout scale products, the proposed model is strongly preferred to the traditional random-effect specification for choice-based conjoint. A sizeable group of subjects, approximately 63%, were found to consider proper subsets of all attributes presented. There was a great deal of heterogeneity in attributes deemed relevant across subjects: the proportion of subjects who did not consider a given attribute among the six used in the study ranged from 17.4% to 41.3%. For those who did consider a given attribute, estimated attribute level part-worths were essentially identical for the proposed model and the traditional random-effect conjoint model; but this was not the case for non-considered attributes. In fact, the traditional model was found to suffer from systematic biases in aggregate part-worth magnitudes. Finally, and most important for marketing practice, allowing for the possibility that some subject may not consider particular attributes can lead to substantial design and revenue differences in supposedly optimal products, at both the individual- and the aggregate-level

Choosing Optimal Designs for Pair-Wise Metric Conjoint Experiments

This paper presents an approach to the optimal fixed experimental design for pair-wise metric conjoint studies. We first propose a linear model suitable for metric paired comparison conjoint analysis. Following Bayesian decision theory, optimal design problems in pairwise metric conjoint analysis is then formally defined. Given the formal definition of the experimental design problems, algorithms for the identification for the optimal design are developed. The proposed methodology is applied to a hypothetical conjoint experimental design problem and major findings are discussed

Modeling parametric evolution in a random utility framework

Abstract Random Utility models have become standard econometric tools, allowing parameter inference for individual-level categorical choice data. Such models typically presume that changes in observed choices over time can be attributed to changes in either covariates or unobservables. We study how choice dynamics can be captured more faithfully by additionally modeling temporal changes in parameters directly, using a vector autoregressive process and Bayesian estimation. This approach offers a number of advantages for theorists and practitioners, including improved forecasts, prediction of long-run parameter levels, and correction for potential aggregation biases. We illustrate the method using choices for a common supermarket good, where we find strong support for parameter dynamics.

Usefulness of serum lactate as a predictor of successful discontinuation of continuous atropine infusion in patients with severe acute organophosphate poisoning

Objective In severe organophosphate (OP) poisoning, administration of atropine via continuous intravenous infusion is typically considered. To date, there have been no studies on predicting successful atropine discontinuation through plasma cholinesterase (PChE) and serum lactate levels, which are monitored during critical care in severe acute OP poisoning. Therefore, we retrospectively evaluated the usefulness of serum lactate and PChE as predictors of successful discontinuation of atropine infusion. Methods This retrospective observational study was performed on consecutive adult patients treated for severe acute OP poisoning between March 2011 and December 2016. We sequentially evaluated serum lactate and PChE levels on emergency department arrival and before a discontinuation trial of atropine infusion. Discontinuation of atropine intravenous infusion was attempted in patients after clearance of respiratory secretions and cessation of bronchoconstriction. Discontinuation of atropine infusion attempts were divided into successful and failed trials. Results A total of 95 trials were conducted in 62 patients. Serum lactate levels before trials were significantly different between patients with successful and failed trials. The area under the curve for prediction of successful atropine discontinuation using serum lactate levels before trial discontinuation were 0.742 (95% confidence interval, 0.638 to 0.846). PChE level was not significantly different between two groups. Conclusion Serum lactate levels before the discontinuation trial of atropine infusion served to predict successful discontinuation in severe acute OP poisoning

Telomerase and Apoptosis in the Placental Trophoblasts of Growth Discordant Twins

In an effort to investigate the molecular basis of growth discordance in embryos that experience the same uterine environment, we compared telomerase activity and apoptosis in placental trophoblasts obtained from growth discordant twins. Between January 2003 and February 2005, placental tissue from twenty pairs of twins was obtained within thirty minutes of delivery. Eleven cases were classified as growth discordant, with birth weight discordance greater than 20%. Nine cases comprised the control group, with less than 20% discordance. Telomerase and apoptotic activities in placental trophoblasts were analyzed by ELISA and immunoblot. Statistical significance was analyzed by a paired t-test, chisquared test, and ANOVA (SPSS ver 11.0). The average growth discordance was 26.8% in the growth discordant group and 14.4% in the control group. There were no significant differences in maternal age, week of gestation at delivery, parity, or chorionisity between the two groups. In the growth discordant group, the larger twin showed significantly higher telomerase activity (p < 0.01), whereas no significant difference was observed in the control group (p = 0.36). In addition, there was no definitive correlation between telomerase activity and the degree of growth discordance in the larger or smaller twins (R = -0.521 and -0.399, p = 0.15 and 0.25, respectively). The apoptosis proteins Bax and Bcl 2 were detected in both the larger and smaller twins in the growth discordant and control groups. There was no statistically significant difference in Bax expression between the larger and smaller twins (p = 0.25 and 0.92, respectively) for either the growth discordant or the control groups. Bcl 2 expression also showed no significant difference between groups. In conclusion, a tendency toward reduced telomerase activity and increased apoptosis was discovered in placental trophoblasts of the smaller growth-discordant twin, possibility resulting in delayed fetal growth