Bayesian Estimation of Mixed Multinomial Logit Models: Advances and Simulation-Based Evaluations

Variational Bayes (VB) methods have emerged as a fast and computationally-efficient alternative to Markov chain Monte Carlo (MCMC) methods for scalable Bayesian estimation of mixed multinomial logit (MMNL) models. It has been established that VB is substantially faster than MCMC at practically no compromises in predictive accuracy. In this paper, we address two critical gaps concerning the usage and understanding of VB for MMNL. First, extant VB methods are limited to utility specifications involving only individual-specific taste parameters. Second, the finite-sample properties of VB estimators and the relative performance of VB, MCMC and maximum simulated likelihood estimation (MSLE) are not known. To address the former, this study extends several VB methods for MMNL to admit utility specifications including both fixed and random utility parameters. To address the latter, we conduct an extensive simulation-based evaluation to benchmark the extended VB methods against MCMC and MSLE in terms of estimation times, parameter recovery and predictive accuracy. The results suggest that all VB variants with the exception of the ones relying on an alternative variational lower bound constructed with the help of the modified Jensen's inequality perform as well as MCMC and MSLE at prediction and parameter recovery. In particular, VB with nonconjugate variational message passing and the delta-method (VB-NCVMP-Delta) is up to 16 times faster than MCMC and MSLE. Thus, VB-NCVMP-Delta can be an attractive alternative to MCMC and MSLE for fast, scalable and accurate estimation of MMNL models

Exploring Heterogeneity in Consumers’ Meat Store Choices in an Emerging Market

Chinese consumers’ choices among meat stores are examined through a model that can capture consumer heterogeneities both in their opinion of various store attributes and in how much weight they attach to each attribute. This approach not only informs store managers as to what attributes should receive focus for improving their store images, but also provides insight about which specific attribute could be improved to achieve the most effective result. Based on the individual-level parameters obtained through an empirical Bayes analysis, managers or competitors are able to strategically target their store promotions to specific individual consumers based on their demographic characteristics.heterogeneity, individual-level parameters, logit models, meat store, Livestock Production/Industries, Research and Development/Tech Change/Emerging Technologies,

Active inference, evidence accumulation, and the urn task

Deciding how much evidence to accumulate before making a decision is a problem we and other animals often face, but one that is not completely understood. This issue is particularly important because a tendency to sample less information (often known as reflection impulsivity) is a feature in several psychopathologies, such as psychosis. A formal understanding of information sampling may therefore clarify the computational anatomy of psychopathology. In this theoretical letter, we consider evidence accumulation in terms of active (Bayesian) inference using a generic model of Markov decision processes. Here, agents are equipped with beliefs about their own behavior--in this case, that they will make informed decisions. Normative decision making is then modeled using variational Bayes to minimize surprise about choice outcomes. Under this scheme, different facets of belief updating map naturally onto the functional anatomy of the brain (at least at a heuristic level). Of particular interest is the key role played by the expected precision of beliefs about control, which we have previously suggested may be encoded by dopaminergic neurons in the midbrain. We show that manipulating expected precision strongly affects how much information an agent characteristically samples, and thus provides a possible link between impulsivity and dopaminergic dysfunction. Our study therefore represents a step toward understanding evidence accumulation in terms of neurobiologically plausible Bayesian inference and may cast light on why this process is disordered in psychopathology

Bayesian Analysis of Consumer Choices with Taste, Context, Reference Point and Individual Scale Effects

This paper adopts an approach based on the concepts of random utility maximization and builds on the general theoretical framework of Lancaster and on the conceptual and econometric innovations of McFadden. Recent research in this area explores models that account for context effects, as well as methods for characterizing heterogeneity, response variability and decision strategy selection by consumers. This makes it possible to construct much richer empirical models of individual consumer behavior. A Bayesian approach provides a useful way to estimate and interpret models that are difficult to accomplish by conventional maximization/minimization algorithms. The application reported in the paper involves analysis of reference dependence and product labeling as context effects and the assessment of heterogeneity and response variability.Consumer/Household Economics,

Hyperbolic Discounting of Public Goods

This article examines revealed rates of time preference for public goods, using environmental quality as the case study. A nationally representative panel-based sample of 2,914 respondents considered a series of 5 conjoint policy choices, yielding 14,570 decisions. Both the conditional fixed effect logit estimates of the random utility model and mixed logit estimates implied that the rate of time preference is very high for immediate improvements and drops off substantially thereafter, which is inconsistent with exponential discounting but consistent with hyperbolic discounting. The implied marginal rate of time preference declines and then rises. Estimates of the quasi-hyperbolic discounting parameter range from 0.48 to 0.61. People who are older are especially likely to have a high disutility from delays in improving water quality.