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Students and administrators can benefit from new analytics.

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We study the structure of heterosexual dating markets in the United States through an analysis of the interactions of several million users of a large online dating website, applying recently developed network analysis methods to the pattern of messages exchanged among users. Our analysis shows that the strongest driver of romantic interaction at the national level is simple geographic proximity, but at the local level, other demographic factors come into play. We find that dating markets in each city are partitioned into submarkets along lines of age and ethnicity. Sex ratio varies widely between submarkets, with younger submarkets having more men and fewer women than older ones. There is also a noticeable tendency for minorities, especially women, to be younger than the average in older submarkets, and our analysis reveals how this kind of racial stratification arises through the messaging decisions of both men and women. Our study illustrates how network techniques applied to online interactions can reveal the aggregate effects of individual behavior on social structure.

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We develop and estimate a statistical model of neighborhood choice that draws on insights from cognitive science and decision theory as well as qualitative studies of housing search. The model allows for a sequential decision process and the possibility that people consider a small and selective subset of all potential destinations. When combined with data from the Los Angeles Family and Neighborhood Survey, our model reveals that affordability constraints and households' tendency toward short-distance moves lead blacks and Hispanics to have racially stratified choice sets in which their own group is disproportionately represented. We use an agent-based model to assess how racially stratified choice sets contribute to segregation outcomes. Our results show that cognitive decision strategies can amplify patterns of segregation and inequality.

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Romantic courtship is often described as taking place in a dating market where men and women compete for mates, but the detailed structure and dynamics of dating markets have historically been difficult to quantify for lack of suitable data. In recent years, however, the advent and vigorous growth of the online dating industry has provided a rich new source of information on mate pursuit. We present an empirical analysis of heterosexual dating markets in four large U.S. cities using data from a popular, free online dating service. We show that competition for mates creates a pronounced hierarchy of desirability that correlates strongly with user demographics and is remarkably consistent across cities. We find that both men and women pursue partners who are on average about 25% more desirable than themselves by our measures and that they use different messaging strategies with partners of different desirability. We also find that the probability of receiving a response to an advance drops markedly with increasing difference in desirability between the pursuer and the pursued. Strategic behaviors can improve one's chances of attracting a more desirable mate, although the effects are modest.

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Over the past half-century, scholars in the interdisciplinary field of Judgment and Decision Making have amassed a trove of findings, theories, and prescriptions regarding the processes ordinary people enact when making choices. But this body of knowledge has had little influence on sociology. Sociological research on choice emphasizes how features of the social environment shape individual behavior, not people's underlying decision processes. Our aim in this article is to provide an overview of selected ideas, models, and data sources from decision research that can fuel new lines of inquiry on how socially situated actors navigate both everyday and major life choices. We also highlight opportunities and challenges for cross-fertilization between sociology and decision research that can allow the methods, findings, and contexts of each field to expand their joint range of inquiry.

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This paper presents a statistical framework for harnessing online activity data to better understand how people make decisions. Building on insights from cognitive science and decision theory, we develop a discrete choice model that allows for exploratory behavior and multiple stages of decision making, with different rules enacted at each stage. Critically, the approach can identify if and when people invoke noncompensatory screeners that eliminate large swaths of alternatives from detailed consideration. The model is estimated using deidentified activity data on 1.1 million browsing and writing decisions observed on an online dating site. We find that mate seekers enact screeners ("deal breakers") that encode acceptability cutoffs. A nonparametric account of heterogeneity reveals that, even after controlling for a host of observable attributes, mate evaluation differs across decision stages as well as across identified groupings of men and women. Our statistical framework can be widely applied in analyzing large-scale data on multistage choices, which typify searches for "big ticket" items.

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Agent-based modeling has become increasingly popular in recent years, but there is still no codified set of recommendations or practices for how to use these models within a program of empirical research. This article provides ideas and practical guidelines drawn from sociology, biology, computer science, epidemiology, and statistics. We first discuss the motivations for using agent-based models in both basic science and policy-oriented social research. Next, we provide an overview of methods and strategies for incorporating data on behavior and populations into agent-based models, and review techniques for validating and testing the sensitivity of agent-based models. We close with suggested directions for future research.

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This study provides a framework for understanding how population composition conditions the relationship between individuals' choices about group affiliation and aggregate patterns of social separation or integration. The substantive focus is the role of income inequality in racial residential segregation. The author identifies three population parameters--between-group inequality, within-group inequality, and relative group size--that determine how income inequality between race groups affects racial segregation. She uses data from the Panel Study of Income Dynamics to estimate models of individual-level residential mobility and incorporates these estimates into agent-based models. She then simulates segregation dynamics under alternative assumptions about (1) the relative size of minority groups and (2) the degree of correlation between race and income among individuals. The author finds that income inequality can have offsetting effects at the high and low ends of the income distribution. She demonstrates the empirical relevance of the simulation results using fixed-effects, metro-level regressions applied to 1980-2000 U.S. census data.

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This paper reviews methods for analyzing both individual preferences and choices about where to live, and the implications of these choices for residential patterns. Although these methods are discussed in the context of residential choice, they can be applied more broadly to individual choices in a range of social contexts where behavior is interdependent. We review a variety of types of data on residential preferences and mobility and discuss appropriate statistical models for these data. We discuss the analysis of ranked and other types of clustered data; functional form issues; problems of unobserved heterogeneity in individuals and in neighborhoods; and strengths and weaknesses of stated preference data versus observations of actual mobility behavior. We also discuss specific problems with residential mobility data, including the treatment of one's current location as a potential choice, how to specify the choice set of potential movers, the aggregation of units (such as dwelling units into neighborhoods) and the need to take account of variations in neighborhood size, the problem of very large choice sets and possible sampling solutions; and the link between residential mobility and patterns of neighborhood change.