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Advances in molecular genetics have led to the exponential growth of the direct-to-consumer genetic testing industry, resulting in the assembly of massive privately owned genetic databases. This article explores the potential impact of this new data type on the field of marketing. Drawing on findings from behavioral genetic research, the authors propose a framework that incorporates genetic influences into existing consumer behavior theory and use it to survey potential marketing uses of genetic data. Applications include business strategies that rely on genetic variants as bases for segmentation and targeting, creative uses that develop consumers’ sense of community and personalization, use of genetically informed study designs to test causal relations, and refinement of consumer theory by uncovering biological mechanisms underlying behavior. The authors further evaluate ethical challenges related to autonomy, privacy, misinformation, and discrimination that are unique to the use of genetic data and are not sufficiently addressed by current regulations. They conclude by proposing an agenda for future research.

The importance of the choice set composition on individual preferences has been of wide interest in decision models. We propose a model seeking to elaborate on the role that choice set composition plays in a discrete choice problem through a normalization of the perceived value of each product's attributes. Our model extends the comprehension of context effects beyond the classical three-option cases of decoy, compromise and similarity. We apply a state-of-the-art class of models stemming from recent research on neural normalization to a multi-attribute choice setting. We also investigate the construction of the reference point by comparing different models, from simple cases to flexible neural networks. We highlight the performance of the model with an experimental application to credit card choices,and discuss the implications for product portfolio optimization. We find decisive evidence for attribute-based normalizing behavior. Understanding this normalization phenomenon will allow firms to optimize their portfolio with options whose main purpose is to increase the sales of their other products.

Previous research points to the heritability of risk-taking behaviour. However, evidence on how genetic dispositions are translated into risky behaviour is scarce. Here, we report a genetically-informed neuroimaging study of real-world risky behaviour across the domains of drinking, smoking, driving, and sexual behaviour, in a European sample from the UK Biobank (N= 12,675). We find negative associations between risky behaviour and grey matter volume (GMV) in distinct brain regions, including amygdala, ventral striatum, hypothalamus, and dorsolateral prefrontal cortex (dlPFC). These effects replicate in an independent sample recruited from the same population (N=13,004). Polygenic risk scores for risky behaviour, derived from a genome-wide association study in an independent sample (N=297,025), are inversely associated with GMV in dlPFC, putamen, and hypothalamus. This relation mediates ~2.2% of the association between genes and behaviour. Our results highlight distinct heritable neuroanatomical features as manifestations of the genetic propensity for risk taking.One Sentence SummaryRisky behaviour and its genetic associations are linked to less grey matter volume in distinct brain regions.

Practical use of Hierarchical Bayes models require the availability of efficient methods for posterior inference. Sequential Monte Carlo methods have appeared as an extremely robust way to simulate complicated Bayesian posteriors. The simplest version successively weights and resample draws called particles from a sequence of target distributions. The method has a main weakness keeping it from being used in complex hierarchical models and large data sets: sample impoverishment. This issue is usually alleviated through the use of a "refreshing" MCMC step. However, to preserve effectiveness, the computational costs are quadratically increasing with the total number of observations. We propose a new SMC-within-SMC method. In a first step, each individual-level parameter is estimated separately using standard SMC and a non-hierarchical auxiliary prior. In a second step, we use weighting methods to replace the auxiliary prior with the hierarchical one without the need to recompute any likelihood. In addition to allowing for the separate processing of individual data, this approach drastically reduces the computational costs. A MATLAB package is provided.

In behavioral experiments, carefully choosing the stimuli is critical for success. Recently, several "adaptive" Bayesian methods gained popularity by proposing to optimally select the stimulus in each trial based on the results of the preceding trials. However, current methods are computationally expensive and might require a long waiting period between each question. Moreover, they are often tailored to a particular model and a particular objective, such as parameter estimation, prediction or model selection. It is left to the researcher to extend these approaches to other models by providing a suitable Bayesian inference method. We propose to apply the Sequential Monte Carlo (SMC) framework to solve both the inference problem and the optimal experimental design problem. This new method, called Sequential Optimal Inference (SOI) provides gains in computational efficiency and allows for the use of a broad class of complex models and objectives. We demonstrate its validity with simulation studies. An implementation of the method in MATLAB and Python is provided.

We implement a “Double Decoy” experiment designed to separate two competing accounts of the asymmetric dominance effect in choice behaviour. In our experiment, we place an additional decoy alternative within the range of existing alternatives, therefore a theory which weights attributes by their range would predict a null effect on relative choice probabilities. Instead, we observe a significant decrease in the relative proportion of targets chosen (on average) in our sample. We also observe considerably more variation in individual behaviour than expected under the null hypothesis. To address these features of the data, we consider an alternative theory in which attributes values are compared two by two and normalized. Using a hierarchical Bayesian framework, we apply this pairwise normalization model both to our Double Decoy data and a standard discrete choice experiment. We find that it captures the variation in behaviour that we observe in both datasets better than range normalization and the standard linear additive Logit model, both in-sample and in an out-of-sample prediction exercise. We therefore propose this model as a useful empirical tool for researchers in applied settings.

The paper proposes a new Monte-Carlo simulator combining the advantages of Sequential Monte Carlo simulators and Hamiltonian Monte Carlo simulators. The result is a method that is robust to multimodality and complex shapes to use for inference in presence of difficult likelihoods or target functions. Several examples are provided.