Investigating Drivers of Repeated Behaviors in Field Data

This dissertation investigates the influences on frequently repeated human behaviors (e.g. eating, exercising, washing hands) using empirical tests on field data. While some of the phenomena discussed have been studied in lab settings (e.g., self-regulation failures, insensitivity to reward devaluation), these studies present some of the first tests of these behavioral phenomena in the field. This dissertation also assembles a number of methodologies which can be used to study individual-level field data, informed by an interdisciplinary perspective on social and decision science research. The first chapter uses field data to study spillovers across behavioral domains, namely exercise and food choice. This work joins a small group of papers which document field evidence related to domain spillovers and failures of self-regulation. Most of the existing research on self-regulation has been conducted in controlled laboratory settings, where participants are either asked to imagine making hypothetical restrained choices or exert effort on a laboratory task as a proxy for making a restrained choice. As is the critique of many lab studies without direct field equivalents however, it is debatable whether the self-regulation behaviors observed in survey and laboratory settings necessarily generalize to the field. We fill this gap by looking at how natural (rather than incentivized) changes in exercise systematically affect food choice, thus empirically identifying spillovers across two behavioral domains in field data. We find that, even after controlling for individual fixed effects, there is a robust effect of morning exercise on the healthiness of a lunch choice. We complement the analysis of field data with surveys to better understand the mechanism driving this result. The second chapter presents a novel methodology for identifying behaviors that are highly and predictably context-sensitive, and thus candidates for being habitual. While there is a large body of laboratory research documenting the mechanisms underlying well-developed habits in animals and humans, there is much less field research on how human habits naturally develop over time. Using two large datasets on gym attendance and handwashing behavior, we use machine learning to statistically classify when choices are predicted by an identifiable set of context variables. This technique generates a person-specific measure of behavioral predictability, which can then be used to study individual differences in predictability and speed of habit formation. This allows us to establish two important discoveries. First, the sets of context cues that are predictive of individual-level behavior are different for different people. Specifically, while historical behavior is an important universal predictor, other context variables such as day of the week or month of the year have more heterogeneous effects. Second, contrary to common wisdom, there is no "magic number" for how long it takes to form a habit. Instead, the speed of habit formation appears to vary significantly, both between behavioral domains and between individuals within domains. The third chapter uses a novel methodology to run a field experiment testing the effect of a price promotion on consumer behavior. The goal of this "pilot study" is to credibly dissociate predictions made by brand loyalty/habit formation from reference-dependence theories. A customizable vending machine serves as a "mini-retailer," allowing for full control of price promotion details in an ecologically valid setting. The vending machine allows controlling for stockpiling behavior, an important concern for empirical work analyzing price promotions in the marketing literature. Analysis of the data collected from this pilot study suggests that price promotions increase the sales of both discounted and non-discounted items, as well as the total number of unique customers making purchases. Furthermore, in line with the loss leader hypothesis, more items are purchased during the sale period overall.</p

The golden age of social science

Social science is entering a golden age, marked by the confluence of explosive growth in new data and analytic methods, interdisciplinary approaches, and a recognition that these ingredients are necessary to solve the more challenging problems facing our world. We discuss how developing a “lingua franca” can encourage more interdisciplinary research, providing two case studies (social networks and behavioral economics) to illustrate this theme. Several exemplar studies from the past 12 y are also provided. We conclude by addressing the challenges that accompany these positive trends, such as career incentives and the search for unifying frameworks, and associated best practices that can be employed in response

Subjective Fear Dilutes as Group Size Increases

According to Hamilton’s Selfish Herd Theory, a crucial survival benefit of group living is that it provides a ‘risk dilution’ function against predation. Despite a large literature on group living benefits in animals, few studies have been conducted on how group size alters subjective fear or threat perception in humans, and on what factors drive preferences for being in groups when facing threats. We conducted seven experiments (N=3,838) to test (A) if the presence of others decreases perception of threat under a variety of conditions. In studies 1 to 3, we experimentally manipulated group size in hypothetical and real-world situations, to show that fear responses decreased as group size increased. In studies 4 to 7 we again used a combination of hypothetical, virtual and real-world decisions to test (B) how internal states (e.g. anxiety) and external factors (e.g. threat level, availability of help) affected participants’ preference for groups. Participants consistently chose larger groups when threat and anxiety were high. Overall, our findings show that group size provides a salient signal of protection and safety

What can machine learning teach us about habit formation? Evidence from exercise and hygiene

We apply a machine learning technique to characterize habit formation in two large panel data sets with objective measures of 1) gym attendance (over 12 million observations) and 2) hospital handwashing (over 40 million observations). Our Predicting Context Sensitivity (PCS) approach identifies context variables that best predict behavior for each individual. This approach also creates a time series of overall predictability for each individual. These time series predictability values are used to trace a habit formation curve for each individual, operationalizing the time of habit formation as the asymptotic limit of when behavior becomes highly predictable. Contrary to the popular belief in a “magic number” of days to develop a habit, we find that it typically takes months to form the habit of going to the gym but weeks to develop the habit of handwashing in the hospital. Furthermore, we find that gymgoers who are more predictable are less responsive to an intervention designed to promote more gym attendance, consistent with past experiments showing that habit formation generates insensitivity to reward devaluation