Understanding information use in multiattribute decision making

An inference task in one in which some known set of information is used to produce an estimate about an unknown quantity. Existing theories of how humans make inferences include specialized heuristics that allow people to make these inferences in familiar environments quickly and without unnecessarily complex computation. Specialized heuristic processing may be unnecessary, however; other research suggests that the same patterns in judgment can be explained by existing patterns in encoding and retrieving memories. This dissertation compares and attempts to reconcile three alternate explanations of human inference. After justifying three hierarchical Bayesian version of existing inference models, the three models are com- pared on simulated, observed, and experimental data. The results suggest that the three models capture different patterns in human behavior but, based on posterior prediction using laboratory data, potentially ignore important determinants of the decision process.

Physiological Effects of Alcohol on Crayfish Escape Circuitry

Alcohol is one of the oldest and most widely used drugs on the planet, but the cellular mechanisms by which it affects neural function are still poorly understood. Unlike other drugs of abuse, alcohol has no specific receptor in the nervous system, but is believed to operate through GABAergic and serotonergic neurotransmitter systems. Invertebrate models offer circuits of reduced numerical complexity and involve the same cell types and neurotransmitter systems as vertebrate circuits. The well-understood neural circuits controlling crayfish escape behavior offer neurons that are modulated by GABAergic inhibition, thus making tail-flip circuitry an effective circuit model to study the cellular mechanisms of acute alcohol exposure. Crayfish are capable of two stereotyped, reflexive escape behaviors known as tail-flips that are controlled by two different pairs of giant interneurons, the lateral giants (LG) and the medial giants (MG). The LG circuit has been an established model in the neuroscience field for more than 60 years and is almost completely mapped out. In contrast, the MG is still poorly understood, but has important behavioral implications in social behavior and value-based decision making. In this dissertation, I show that both crayfish tail-flip circuitry are physiologically sensitive to relevant alcohol concentrations and that this sensitivity is observable on the single cell level. I also show that this ethyl alcohol (EtOH) sensitivity in the LG can be changed by altering the crayfish’s recent social experience and by removing descending inputs to the LG. While the MG exhibits similar physiological sensitivity, its inhibitory properties have never been studied before this research. Through the use of electrophysiological and pharmacological techniques, I show that the MG exhibits many similar inhibitory properties as the LG that appear to be the result of GABA-mediated chloride currents. Finally, I present evidence that the EtOH-induced changes in the MG are blocked through pre-treatment of the potent GABAA receptor agonist, muscimol, which underlines the role of GABA in EtOH’s effects on crayfish tail-flip circuitry. The work presented here opens the way for crayfish tail-flip circuitry to be used as an effective model for EtOH’s acute effects on aggression and value-based decision making.

VOX POPULI: THREE ESSAYS ON THE USE OF SOCIAL MEDIA FOR VALUE CREATION IN SERVICES

Prior research shows that electronic word of mouth (eWOM) wields considerable influence over consumer behavior. However, as the volume and variety of eWOM grows, firms are faced with challenges in analyzing and responding to this information. In this dissertation, I argue that to meet the new challenges and opportunities posed by the expansion of eWOM and to more accurately measure its impacts on firms and consumers, we need to revisit our methodologies for extracting insights from eWOM. This dissertation consists of three essays that further our understanding of the value of social media analytics, especially with respect to eWOM. In the first essay, I use machine learning techniques to extract semantic structure from online reviews. These semantic dimensions describe the experiences of consumers in the service industry more accurately than traditional numerical variables. To demonstrate the value of these dimensions, I show that they can be used to substantially improve the accuracy of econometric models of firm survival. In the second essay, I explore the effects on eWOM of online deals, such as those offered by Groupon, the value of which to both consumers and merchants is controversial. Through a combination of Bayesian econometric models and controlled lab experiments, I examine the conditions under which online deals affect online reviews and provide strategies to mitigate the potential negative eWOM effects resulting from online deals. In the third essay, I focus on how eWOM can be incorporated into efforts to reduce foodborne illness, a major public health concern. I demonstrate how machine learning techniques can be used to monitor hygiene in restaurants through crowd-sourced online reviews. I am able to identify instances of moral hazard within the hygiene inspection scheme used in New York City by leveraging a dictionary specifically crafted for this purpose. To the extent that online reviews provide some visibility into the hygiene practices of restaurants, I show how losses from information asymmetry may be partially mitigated in this context. Taken together, this dissertation contributes by revisiting and refining the use of eWOM in the service sector through a combination of machine learning and econometric methodologies.

Studying the Causes and Consequences of Internal Labor Migration Using Survey and Administrative Data Sources

This dissertation comprises three chapters. The first chapter motivates the use of a novel data set combining survey and administrative sources for the study of internal labor migration. By following a sample of individuals from the American Community Survey (ACS) across their employment outcomes over time according to the Longitudinal Employer-Household Dynamics (LEHD) database, I construct a measure of geographic labor mobility that allows me to exploit information about individuals prior to their move. This enables me to explore aspects of the migration decision, such as homeownership and employment status, in ways that have not previously been possible. In the second chapter, I use this data set to test the theory that falling home prices affect a worker’s propensity to take a job in a different metropolitan area from where he is currently located. Employing a within-CBSA and time estimation that compares homeowners to renters in their propensities to relocate for jobs, I find that homeowners who have experienced declines in the nominal value of their homes are approximately 12% less likely than average to take a new job in a location outside of the metropolitan area where they currently reside. This evidence is consistent with the hypothesis that housing lock-in has contributed to the decline in labor mobility of homeowners during the recent housing bust. The third chapter focuses on a sample of unemployed workers in the same data set, in order to compare the unemployment durations of those who find subsequent employment by relocating to a new metropolitan area, versus those who find employment in their original location. Using an instrumental variables strategy to address the endogeneity of the migration decision, I find that out-migrating for a new job significantly reduces the time to re-employment. These results stand in contrast to OLS estimates, which suggest that those who move have longer unemployment durations. This implies that those who migrate for jobs in the data may be particularly disadvantaged in their ability to find employment, and thus have strong short-term incentives to relocate.