Addictive Technology and Its Implications for Antitrust Enforcement

The advent of mobile devices and digital media platforms in the past decade represents the biggest shock to cognition in human history. Robust medical evidence is emerging that digital media platforms are addictive and, when used in excess, harmful to users’ mental health. Other types of addictive products, like tobacco and prescription drugs, are heavily regulated to protect consumers. Currently, there is no regulatory structure protecting digital media users from these harms. Antitrust enforcement and regulation that lowers entry barriers could help consumers of social media by increasing competition. Economic theory tells us that more choice in digital media will increase the likelihood that some firms will vie to offer higher-quality and safer platforms. For this reason, evaluating harm to innovation (especially safety innovation) and product variety may be particularly important in social media merger and conduct cases. Another critical element to antitrust enforcement in this space is a correct accounting of social media’s addictive qualities. Standard antitrust analysis seeks to prohibit conduct that harms consumer welfare. Economists have taught the antitrust bar that the output of a product or service is a reliable proxy for consumer welfare. However, output and welfare do not have this relationship when a product is addictive. Indeed, in social media markets, increased output is often harmful. We argue that antitrust analysis must reject the output proxy and return to a focus on consumer welfare itself in cases involving addictive social media platforms. In particular, courts should reject defenses that rely only on gross output measures without evidence that any alleged increases in output actually benefit consumers

Auditory cues and inhibition of return: the importance of oculomotor activation

We studied the effects of eccentric auditory cues to clarify the conditions that evoke inhibition of return (IOR). We found that auditory cues positioned 12° to the left or right of midline failed to produce IOR whereas visual cues produced IOR under the same experimental conditions. The eccentric auditory cues elicited automatic orienting as evidenced by more rapid detection of cued than uncued visual targets at short stimulus onset asynchrony. Yet these same cues did not produce IOR unless observers were required to saccade to the cue and back to center before generating a manual detection response. Thus, under the conditions examined herein automatic orienting was not sufficient to evoke IOR, but oculomotor activation appeared to be essential. The functional significance of IOR and the question of modality-specific orienting processes are considered.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/46532/1/221_2004_Article_BF00227185.pd

Estimating Peer Effects in Longitudinal Dyadic Data Using Instrumental Variables

The identification of causal peer effects (also known as social contagion or induction) from observational data in social networks is challenged by two distinct sources of bias: latent homophily and unobserved confounding. In this paper, we investigate how causal peer effects of traits and behaviors can be identified using genes (or other structurally isomorphic variables) as instrumental variables (IV) in a large set of data generating models with homophily and confounding. We use directed acyclic graphs to represent these models and employ multiple IV strategies and report three main identification results. First, using a single fixed gene (or allele) as an IV will generally fail to identify peer effects if the gene affects past values of the treatment. Second, multiple fixed genes/alleles, or, more promisingly, time-varying gene expression, can identify peer effects if we instrument exclusion violations as well as the focal treatment. Third, we show that IV identification of peer effects remains possible even under multiple complications often regarded as lethal for IV identification of intra-individual effects, such as pleiotropy on observables and unobservables, homophily on past phenotype, past and ongoing homophily on genotype, inter-phenotype peer effects, population stratification, gene expression that is endogenous to past phenotype and past gene expression, and others. We apply our identification results to estimating peer effects of body mass index (BMI) among friends and spouses in the Framingham Heart Study. Results suggest a positive causal peer effect of BMI between friends

Cohort of Birth Modifies the Association between FTO Genotype and BMI

A substantial body of research has explored the relative roles of genetic and environmental factors on phenotype expression in humans. Recent research has also sought to identify gene-environment (or g-by-e) interactions, with mixed success. One potential reason for these mixed results may relate to the fact that genetic effects might be modified by changes in the environment over time. For example, the noted rise of obesity in the United States in the latter part of the 20th century might reflect an interaction between genetic variation and changing environmental conditions that together affect the penetrance of genetic influences. To evaluate this hypothesis, we use longitudinal data from the Framingham Heart Study collected over 30 y from a geographically relatively localized sample to test whether the well-documented association between the rs993609 variant of the FTO (fat mass and obesity associated) gene and body mass index (BMI) varies across birth cohorts, time period, and the lifecycle. Such cohort and period effects integrate many potential environmental factors, and this gene-by-environment analysis examines interactions with both time-varying contemporaneous and historical environmental influences. Using constrained linear age-period-cohort models that include family controls, we find that there is a robust relationship between birth cohort and the genotype-phenotype correlation between the FTO risk allele and BMI, with an observed inflection point for those born after 1942. These results suggest genetic influences on complex traits like obesity can vary over time, presumably because of global environmental changes that modify allelic penetrance

Understanding adolescent economic behavior: A novel empirical approach using genetic data

This thesis has two main foci. First, it seeks to outline how recent findings from the genetics and neuroscience literature can enrich both the empirical and theoretical understanding of economic behavior. Second, it presents models of health/education investment in teens as well as a special case of the Becker-Murphy model of smoking that treats adolescence separately. To address the hypotheses generated by these models, a unique and rich longitudinal dataset of 1000 high school students that includes detailed information on smoking and genetic markers is analyzed. Two staged least squares models that use genetic markers as instruments are used to infer causality from health to academic achievement. The main findings from this thesis are: (1) Genetic markers possess good statistical properties as instrumental variables that can yield rich insights when included in estimation. (2) The impact of health status on educational outcomes varies greatly by gender. This difference becomes more striking once endogeneity of health outcomes is accounted for. (3) Co-morbid health conditions complicate results and present an additional hurdle for empirical researchers. (4) Among boys with ADHD and girls who are diagnosed as obese, cigarette smoking is correlated with higher GPA, although low sample size may bias those results. (5) Models of addiction do not take into account changes in the adolescent brain that can impact addictive behavior. (6) Though data limitations make it impossible to empirically test the predictions of the smoking model, future data sets should allow for such analysis

Understanding adolescent economic behavior: A novel empirical approach using genetic data

This thesis has two main foci. First, it seeks to outline how recent findings from the genetics and neuroscience literature can enrich both the empirical and theoretical understanding of economic behavior. Second, it presents models of health/education investment in teens as well as a special case of the Becker-Murphy model of smoking that treats adolescence separately. To address the hypotheses generated by these models, a unique and rich longitudinal dataset of 1000 high school students that includes detailed information on smoking and genetic markers is analyzed. Two staged least squares models that use genetic markers as instruments are used to infer causality from health to academic achievement. The main findings from this thesis are: (1) Genetic markers possess good statistical properties as instrumental variables that can yield rich insights when included in estimation. (2) The impact of health status on educational outcomes varies greatly by gender. This difference becomes more striking once endogeneity of health outcomes is accounted for. (3) Co-morbid health conditions complicate results and present an additional hurdle for empirical researchers. (4) Among boys with ADHD and girls who are diagnosed as obese, cigarette smoking is correlated with higher GPA, although low sample size may bias those results. (5) Models of addiction do not take into account changes in the adolescent brain that can impact addictive behavior. (6) Though data limitations make it impossible to empirically test the predictions of the smoking model, future data sets should allow for such analysis