Extracting multistage screening rules from online dating activity data

Significance Online activity data—for example, from dating, housing search, or social networking websites—make it possible to study human behavior with unparalleled richness and granularity. However, researchers typically rely on statistical models that emphasize associations among variables rather than behavior of human actors. Harnessing the full informatory power of activity data requires models that capture decision-making processes and other features of human behavior. Our model aims to describe mate choice as it unfolds online. It allows for exploratory behavior and multiple decision stages, with the possibility of distinct evaluation rules at each stage. This framework is flexible and extendable, and it can be applied in other substantive domains where decision makers identify viable options from a larger set of possibilities. 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.