Extracting multistage screening rules from online dating activity data

Abstract

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 ...

Decision-Making Processes in Social Contexts

“…Daws and Brown (2002 Daws and Brown (2004) find that, when choosing a college, UK students’ awareness and choice sets differ systematically by socioeconomic status. Finally, in a recent study of online mate choice, Bruch and colleagues (2016) build on insights from marketing and decision research to develop a statistical model that allows for multistage decision processes with different (potentially noncompensatory) decision rules at each stage. They find that conjunctive screeners are common at the initial stage of online mate pursuit, and precise cutoffs differ by gender and other factors.…”

“…Although nonlinearity can be captured using polynomial functions of covariates, these impose smoothness of response; by contrast, conjunctive and disjunctive processes impose cutoffs beyond which evaluation is either wholly positive or insurmountably negative (Bruch et al , 2016). We view the development of this area as critical to the widespread acceptance of formal choice models to social scientists and sociologists in particular, who typically wish to know which neighborhoods we would never live in, jobs we would never take, etc.…”

“…Equally important, these data often contain information on which options people considered , but ultimately decided against. Such activity data provide a rich source of information on sociologically relevant decision processes (Bruch et al 2016). …”

Individuals’ Decisions in the Presence of Multiple Goals

“…So-called big data would not only enable but also practically invite the latter, where unsupervised learning could pick out salient patterns across data types while sidestepping attrition, missing information, and other distracting arcana of real data sets. Indeed, such a revolution has begun, with the automated extraction of marketing Bmeaning^from large corpora [57] and the use of high-dimensional nonparametric models to account for complex nonlinear and non-monotonic data relationships [8]. The use of such methods is presently constrained by storage, manipulation, and processing constraints, although recent advances in both classical and Bayesian estimation [23], as well as the inexorable upward trajectory in computational speed, will increasingly enable researchers to fashion flexible models of goal attainment on available, large-scale data sources.…”

Was geschieht beim Online-Dating?

“…Was dann geschah, wurde zwar auch bereits wissenschaftlich zu erfassen versucht, die Methode der unauffälligen teilnehmenden Beobachtung hatte jedoch ihre immanenten Grenzen. 1 Dies hat sich mit dem Aufkommen von Computern, dem Internet und von Software, die das Begegnen zwischen den Geschlechtern online ermöglicht (Online Dating Platforms) geändert. Dort hinterlässt man Spuren, weil man meist beim Akzeptieren der Geschäftsbedingungen mit unterschreibt, dass die Daten für wissenschaftliche Zwecke weiterverwendet werden dürfen.…”