I earned a Ph.D. in Sociology from UCLA in 2006, and an M.S. in statistics from UCLA in 2008. As a student affiliate of the California Center for Population Research, I received a comprehensive education in social demography and social stratification, and rigorous training in research methodology and statistical techniques for analyzing large-scale survey data. During this time, I also developed a technique for linking empirical estimates of individuals' preferences for neighborhoods with agent-based models to explore the segregation dynamics implied by individuals' residential mobility decisions. While this work is at the vanguard of social research, it relies on a primitive and highly unrealistic conception of individuals' residential choice behavior. This is not unique to my research. The statistical models used in quantitative social science and public health research are rarely (if ever) a plausible model of the underlying behavior or decision-making process that gave rise to the social phenomenon under investigation. Over the past year, I have learned that researchers in departments of Marketing at business schools have highly sophisticated statistical models of how people navigate their environment and make decisions, which draw on insights from cognitive science and decision theory. But these methods have never been applied in population health, for example, to how people choose among neighborhoods, schools, jobs, or entrees in a cafeteria. Based on my initial forays into this area, I have found marketing choice models are orders of magnitude more difficult to master than techniques I have taught myself in the past. There is no standard statistical software, and the programs are usually written from scratch; there is no single model or methodological approach, but rather a loose toolkit of techniques or strategies that are customized to a specific application. In addition, the models often require Bayesian estimation techniques (which require significant expertise outside of standardized software packages). The K01 Mentored Research Scientist Award would provide protected time for me to: (1) master the statistical skills involved in estimating these models, an gain a formal understanding of the underlying theories of decision-making; (2) adapt the marketing statistical models to new substantive applications-the study of residential mobility and mate choice (as observed on an online dating site); (3) develop a methodological framework for linking these cognitively plausible models of individual decision-making with agent-based models to understand the implications of decision strategies for aggregate population dynamics; and (4) explore how this statistical framework may be applied to a broader range of decision-making applications relevant to health research. I have developed a course plan to provide more formal training in the statistical techniques and theoretical frameworks that underpin the statistical models of decision-making used in marketing research. In addition, I intend to get a comprehensive overview of the decision literature in psychology to supplement what has up to now been self-study. I will supplement the coursework in statistical modeling with frequent interactions with my primary mentor, Fred Feinberg. The bulk of my training will take place at the University of Michigan, where Feinberg and Diez Roux are also in residence. I will have access to the data management, computing, and administrative resources of the Population Studies Center, the Department of Sociology, and the Center for the Study of Complex Systems. Both Sociology and Population Studies have provided me with office space for the duration of the grant period. My short-term goals are to master the statistical skills involved in estimating these models and gain a formal understanding of the underlying theories of decision-making; and also to adapt the marketing statistical models to new substantive applications-the study of residential mobility and mate choice (as observed on an online dating site). In the longer term, my goals are to develop a methodological framework for linking these cognitively plausible models of individual decision-making with agent-based models to understand the implications of decision strategies for aggregate patterns of social integration or separation; and to explore how this statistical framework may be applied to a broader range of decision-making applications relevant to health research. My proposed research projects apply the choice-modeling framework in two areas of research: mate choice (as observed on an online dating website) and neighborhood choice. These are two specific instances of a general class of choice problems where people choose-with varying degrees of habit or deliberation- from a moderate to large number of potential options. When confronted with this sort of choice problem, decision theorists have repeatedly shown that people tend to invoke screening rules to simplify the choice problem. I will use data from the Los Angeles Family and Neighborhood Survey to estimate cognitively plausible choice models to capture these screening rules at multiple stages. My mate choice project makes use of a rich dataset I recently acquired from an online dating. I will first estimate a multi-stage choice model aimed at identifying the rules used at each stage, and explore how strategies for mate search and mate choice differ across demographic areas. I will later extend our models to allow for learning and adaptive responses. In both the neighborhood and mate choice case, the cognitively plausible choice models will be coupled with realistic agent-based models to explore the co-evolution of individual behavior and the social environment.