Majority Report

The book is curated by Wiemer Snijders, partner at Commercial Works. This chapter discusses the most fundamental patterns in buyer behaviour. Simple, almost universal rules with far-reaching implications for everything we do in marketing. The evidence presented here is the result of almost six decades of scientific research investigating the repeat buying of thousands of brands in hundreds of categories and in many countries. It includes recent results from two loyalty studies revealing the role that vast numbers of light buyers play in building sales over time. The aim of this chapter is to focus attention on this forgotten majority. Without them, you would not have a brand

Controlling the uncontrolled: Are there incidental experimenter effects on physiologic responding?

The degree to which experimenters shape participant behavior has long been of interest in experimental social science research. Here, we extend this question to the domain of peripheral psychophysiology, where experimenters often have direct, physical contact with participants, yet researchers do not consistently test for their influence. We describe analytic tools for examining experimenter effects in peripheral physiology. Using these tools, we investigate nine data sets totaling 1,341 participants and 160 experimenters across different roles (e.g., lead research assistants, evaluators, confederates) to demonstrate how researchers can test for experimenter effects in participant autonomic nervous system activity during baseline recordings and reactivity to study tasks. Our results showed (a) little to no significant variance in participants' physiological reactivity due to their experimenters, and (b) little to no evidence that three characteristics of experimenters that are well known to shape interpersonal interactions-status (using five studies with 682 total participants), gender (using two studies with 359 total participants), and race (in two studies with 554 total participants)-influenced participants' physiology. We highlight several reasons that experimenter effects in physiological data are still cause for concern, including the fact that experimenters in these studies were already restricted on a number of characteristics (e.g., age, education). We present recommendations for examining and reducing experimenter effects in physiological data and discuss implications for replication

Solution of the 2-star model of a network

The p-star model or exponential random graph is among the oldest and best-known of network models. Here we give an analytic solution for the particular case of the 2-star model, which is one of the most fundamental of exponential random graphs. We derive expressions for a number of quantities of interest in the model and show that the degenerate region of the parameter space observed in computer simulations is a spontaneously symmetry broken phase separated from the normal phase of the model by a conventional continuous phase transition.Comment: 5 pages, 3 figure

Contrast and conflict in Dutch vowels

The nature of phonological representations has been extensively studied in phonology and psycholinguistics. While full specification is still the norm in psycholinguistic research, underspecified representations may better account for perceptual asymmetries. In this paper, we report on a mismatch negativity (MMN) study with Dutch listeners who took part in a passive oddball paradigm to investigate when the brain notices the difference between expected and observed vowels. In particular, we tested neural discrimination (indicating perceptual discrimination) of the tense mid vowel pairs /o/-/ø/ (place contrast), /e/-/ø/ (labiality or rounding contrast), and /e/-/o/ (place and labiality contrast). Our results show (a) a perceptual asymmetry for place in the /o/-/ø/ contrast, supporting underspecification of [CORONAL] and replicating earlier results for German, and (b) a perceptual asymmetry for labiality for the /e/-/ø/ contrast, which was not reported in the German study. A labial deviant [ø] (standard /e/) yielded a larger MMN than a deviant [e] (standard /ø/). No asymmetry was found for the two-feature contrast. This study partly replicates a similar MMN study on German vowels, and partly presents new findings indicating cross-linguistic differences. Although the vowel inventory of Dutch and German is to a large extent comparable, their (morpho)phonological systems are different, which is reflected in processing

The statistical mechanics of networks

We study the family of network models derived by requiring the expected properties of a graph ensemble to match a given set of measurements of a real-world network, while maximizing the entropy of the ensemble. Models of this type play the same role in the study of networks as is played by the Boltzmann distribution in classical statistical mechanics; they offer the best prediction of network properties subject to the constraints imposed by a given set of observations. We give exact solutions of models within this class that incorporate arbitrary degree distributions and arbitrary but independent edge probabilities. We also discuss some more complex examples with correlated edges that can be solved approximately or exactly by adapting various familiar methods, including mean-field theory, perturbation theory, and saddle-point expansions.Comment: 15 pages, 4 figure

Extended polarized semiclassical model for quantum-dot cavity QED and its application to single-photon sources

We present a simple extension of the semi-classical model for a two-level system in a cavity, in order to incorporate multiple polarized transitions, such as those appearing in neutral and charged quantum dots (QDs), and two nondegenerate linearly polarized cavity modes. We verify the model by exact quantum master equation calculations, and experimentally using a neutral QD in a polarization non-degenerate micro-cavity, in both cases we observe excellent agreement. Finally, the usefulness of this approach is demonstrated by optimizing a single-photon source based on polarization postselection, where we find an increase in the brightness for optimal polarization conditions as predicted by the model.Comment: 8 pages, for simple code see https://doi.org/10.5281/zenodo.347666

Sharing Social Network Data: Differentially Private Estimation of Exponential-Family Random Graph Models

Motivated by a real-life problem of sharing social network data that contain sensitive personal information, we propose a novel approach to release and analyze synthetic graphs in order to protect privacy of individual relationships captured by the social network while maintaining the validity of statistical results. A case study using a version of the Enron e-mail corpus dataset demonstrates the application and usefulness of the proposed techniques in solving the challenging problem of maintaining privacy \emph{and} supporting open access to network data to ensure reproducibility of existing studies and discovering new scientific insights that can be obtained by analyzing such data. We use a simple yet effective randomized response mechanism to generate synthetic networks under $\epsilon$-edge differential privacy, and then use likelihood based inference for missing data and Markov chain Monte Carlo techniques to fit exponential-family random graph models to the generated synthetic networks.Comment: Updated, 39 page

Multiple-membership multiple-classification models for social network and group dependences

The social network literature on network dependences has largely ignored other sources of dependence, such as the school that a student attends, or the area in which an individual lives. The multilevel modelling literature on school and area dependences has, in turn, largely ignored social networks. To bridge this divide, a multiple-membership multiple-classification modelling approach for jointly investigating social network and group dependences is presented. This allows social network and group dependences on individual responses to be investigated and compared. The approach is used to analyse a subsample of the Adolescent Health Study data set from the USA, where the response variable of interest is individual level educational attainment, and the three individual level covariates are sex, ethnic group and age. Individual, network, school and area dependences are accounted for in the analysis. The network dependences can be accounted for by including the network as a classification in the model, using various network configurations, such as ego-nets and cliques. The results suggest that ignoring the network affects the estimates of variation for the classifications that are included in the random part of the model (school, area and individual), as well as having some influence on the point estimates and standard errors of the estimates of regression coefficients for covariates in the fixed part of the model. From a substantive perspective, this approach provides a flexible and practical way of investigating variation in an individual level response due to social network dependences, and estimating the share of variation of an individual response for network, school and area classifications