Importance of Social Network Structures in Influencer Marketing

As collaborations between brands and influencers become increasingly popular, predicting the capacity of an influencer to generate engagement has garnered increasing attention from researchers. Traditionally, managers have been relying on follower-based statistics to identify individuals with potential to reach a vast number of users on social-media. However, this approach may often direct managers to accounts with millions of followers accompanied with high recruiting costs. In this paper, we argue that the network structure of influencers is a useful measure for capturing an influencer’s ability to generate engagement. Using Instagram data, we perform a deep-learning analysis on the social network of influencers and show that the network structure explains a large share of the variations in user engagement, even outperforming traditionally used variables such as the number of followers in the case of comments. This study contributes to the emergent literature on the importance of social ties in the digital environmen

Reflecting on the Evidence: A Reply to Knight, McShane, et al. (2020)

Knight, McShane, et al. (2020) report three experiments on testosterone’s effect on the Cognitive Reflection Test (CRT). The experiments were designed and executed independently of each other and of our previous work (Nave, Nadler, Zava & Camerer, 2017). We thank Knight, McShane, et al. for conducting these experiments and summarizing their results, and we agree that one experiment is obviously not enough for establishing an empirical fact. The individual experiments and their meta-analytic summary are consistent with both the null hypothesis and Nave et al.’s conclusions (see Table S6 in Knight, McShane, et al.’s Supplemental Material), and there is evidence for variation in effects across experiments. In what follows, we reflect on design differences among the experiments and the collective evidence that their data contain

Reflecting on the Evidence: A Reply to Knight, McShane, et al. (2020)

Knight, McShane, et al. (2020) report three experiments on testosterone’s effect on the Cognitive Reflection Test (CRT). The experiments were designed and executed independently of each other and of our previous work (Nave, Nadler, Zava & Camerer, 2017). We thank Knight, McShane, et al. for conducting these experiments and summarizing their results, and we agree that one experiment is obviously not enough for establishing an empirical fact. The individual experiments and their meta-analytic summary are consistent with both the null hypothesis and Nave et al.’s conclusions (see Table S6 in Knight, McShane, et al.’s Supplemental Material), and there is evidence for variation in effects across experiments. In what follows, we reflect on design differences among the experiments and the collective evidence that their data contain

Methods for statistical analysis and prediction of discrete choices

The increasing availability of individual-level consumer data has facilitated the development of new methods for analyzing and predicting people's product choices. This thesis contributes to the existing body of literature with three chapters advancing the statistical analysis of discrete choice. In Chapter 1, I propose a new model seeking to elaborate on the role that choice set composition, known as context, plays in a discrete choice problem. Specifically, I generalize a state-of-the-art class of models stemming from recent research on neural normalization to the multi-attribute setting. I impose a structural model composition based on the brain synaptic plasticity literature, allowing for a particular form of correlation between product utilities. I highlight the properties of the model with a series of experiments and real-world empirical applications. In Chapter 2, I propose a new Monte Carlo method, called Hamiltonian Sequential Monte Carlo (HSMC), for the purpose of nonparametric estimation of unobserved consumer heterogeneity in discrete choice problems. HSMC combines the advantages of Sequential Monte Carlo (SMC) and Hamiltonian transition dynamics. SMC exploits gains from parallelization very efficiently as the core computational load involving the model likelihood is performed by many individual particles independently of one another. At the same time, by using first-derivative information, Hamiltonian transition dynamics has been shown to yield substantial gains in Markov chain mixing properties relative to the standard random walk proposal steps outside of the SMC context. I compare the performance of HSMC with SMC on a discrete choice model of consumer purchases with nonparametric consumer heterogeneity and show the favorable properties of HSMC. In Chapter 3, I propose a Bayesian method called Sequential Optimal Inference (SOI) providing an optimal sequence of questions in experiments. Experiments are frequently used to elicit preferences of potential consumers. Using SOI, the questions are adaptively designed to maximize the expected information and take into account the subjects' previous answers. The method also allows for real-time inference and provides updated posterior distributions while the experiment is performed. Existing methods were created for specific sub-cases. SOI is more general, nesting a large class of models and allowing for a number of inference objectives.Ph.D

Genetic underpinnings of risky behaviour relate to altered neuroanatomy

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 in distinct brain regions, including amygdala, ventral striatum, hypothalamus and dorsolateral prefrontal cortex (dlPFC). These effects are replicated 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 grey-matter volume in dlPFC, putamen and hypothalamus. This relation mediates roughly 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

Associations between alcohol consumption and gray and white matter volumes in the UK Biobank

Heavy alcohol consumption has been associated with brain atrophy, neuronal loss, and poorer white matter fiber integrity. However, there is conflicting evidence on whether light-to-moderate alcohol consumption shows similar negative associations with brain structure. To address this, we examine the associations between alcohol intake and brain structure using multimodal imaging data from 36,678 generally healthy middle-aged and older adults from the UK Biobank, controlling for numerous potential confounds. Consistent with prior literature, we find negative associations between alcohol intake and brain macrostructure and microstructure. Specifically, alcohol intake is negatively associated with global brain volume measures, regional gray matter volumes, and white matter microstructure. Here, we show that the negative associations between alcohol intake and brain macrostructure and microstructure are already apparent in individuals consuming an average of only one to two daily alcohol units, and become stronger as alcohol intake increases

Associations between alcohol consumption and gray and white matter volumes in the UK Biobank.

Heavy alcohol consumption has been associated with brain atrophy, neuronal loss, and poorer white matter fiber integrity. However, there is conflicting evidence on whether light-to-moderate alcohol consumption shows similar negative associations with brain structure. To address this, we examine the associations between alcohol intake and brain structure using multimodal imaging data from 36,678 generally healthy middle-aged and older adults from the UK Biobank, controlling for numerous potential confounds. Consistent with prior literature, we find negative associations between alcohol intake and brain macrostructure and microstructure. Specifically, alcohol intake is negatively associated with global brain volume measures, regional gray matter volumes, and white matter microstructure. Here, we show that the negative associations between alcohol intake and brain macrostructure and microstructure are already apparent in individuals consuming an average of only one to two daily alcohol units, and become stronger as alcohol intake increases