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. V ast amounts of activity data streaming from the web, smartphones, and other connected devices make it possible to study human behavior with an unparalleled richness of detail. These "big data" are interesting, in large part because they are behavioral data: strings of choices made by individuals. Taking full advantage of the scope and granularity of such data requires a suite of quantitative methods that capture decision-making processes and other features of human activity (i.e., exploratory behavior, systematic search, and learning). Historically, social scientists have not modeled individuals' behavior or choice processes directly, instead relating variation in some outcome of interest into portions attributable to different "explanatory" covariates. Discrete choice models, by contrast, can provide an explicit statistical representation of choice processes. However, these models, as applied, often retain their roots in rational choice theory, presuming a fully informed, computationally efficient, utility-maximizing individual (1).Over the past several decades, psychologists and decision theorists have shown that decision makers have limited time for learning about choice alternatives, limited working memory, and limited computational capabilities. As a result, a great deal of behavior is habitual, automatic, or governed by simple rules or heuristics. For example, when faced with more than a small handful of options, people engage in a multistage choice process, in which the first stage involves enacting one or more screeners to arrive at a manageable subset amenable to detailed processing and comparison (2-4). These screeners eliminate large swaths of options based on a relatively narrow set of criteria.Researchers in the fields of quantitative marketing and transportation research have built on these insights to develop sophisticated models of individual-level behavior for which a choice history is available, such as ...