Collaborative Research: An Interdisciplinary Approach to Predicting Unequal Treatment

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Disparities in outcomes across social groups are found in nearly every domain of modern human society, including education, the labor market, and healthcare. Whether on the basis of gender, ethnicity, age or other markers, group-based differences in how people treat others are known to arise even when social group information is irrelevant and even when people explicitly reject social stereotypes. Despite progress in documenting these disparities, much remains unknown about their origins. The current research focuses on the role of individual human decision-making in producing societal-level outcomes. Specifically, the investigators aim to leverage complementary strengths of behavioral economics, social psychology, and cognitive neuroscience to uncover systematic patterns of individual human decision-making that, in aggregate, contribute to societal treatment disparities. The primary goal is to characterize the origins of unequal treatment with sufficient precision to support accurate, context-specific predictions of how people will treat members of different social groups. Support for this collaborative effort broadens access to training opportunities for aspiring scientists, provides opportunities for scientific outreach to local communities, and ultimately contributes scientific understanding of societal disparities, with implications for efforts to measure and address discrimination.Substantial progress has been made in documenting the existence of treatment disparities in the world. Separately, substantial progress has been made in in understanding how people think about different social groups in the laboratory. However, given the multitude of ways in which people can be categorized, and the complexity of factors influencing people's social behavior, it has been challenging to construct models of social thought and behavior that are capable of linking laboratory insights to field observations. The current research aims to connect these efforts to produce accurate predictions about when and how members of particular groups will be (dis)advantaged. Specifically, building upon evidence from cognitive neuroscience that valuation and social cognition engage separable but interacting systems, the research uses computational modeling to formally integrate psychological frameworks of how people see others (social perception) with behavioral economic accounts of how people value others' outcomes (social valuation). It then uses those models to predict how people will treat members of different social groups in laboratory and field settings.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.