Collaborative Research: An Interdisciplinary Approach to Predicting Unequal Treatment

在现代人类社会的几乎每个领域，包括教育、劳动力市场和医疗保健，都可以发现社会群体之间的结果差异。无论是基于性别、种族、年龄还是其他标志，即使社会群体信息无关紧要，甚至当人们明确拒绝社会刻板印象时，人们对待他人的方式也会出现基于群体的差异。尽管在记录这些差异方面取得了进展，但它们的起源仍有很多未知之处。目前的研究集中在个人决策在产生社会层面结果中的作用。具体来说，研究人员的目标是利用行为经济学、社会心理学和认知神经科学的互补优势，揭示个人决策的系统模式，这些模式总体上导致了社会待遇的差异。主要目标是以足够精确的方式描述不平等待遇的起源，以支持对人们如何对待不同社会群体成员的准确、具体情况的预测。对这一合作努力的支持拓宽了有抱负的科学家获得培训机会的途径，为向当地社区进行科学推广提供了机会，并最终有助于对社会差异的科学理解，从而对衡量和解决歧视的努力产生影响。在记录世界上存在的治疗差异方面已经取得了重大进展。另外，在实验室中，在理解人们如何看待不同社会群体方面取得了实质性进展。然而，考虑到人们可以被分类的多种方式，以及影响人们社会行为的因素的复杂性，构建能够将实验室见解与现场观察联系起来的社会思想和行为模型一直是一项挑战。目前的研究旨在将这些努力联系起来，以准确预测特定群体的成员何时以及如何处于（不利）地位。具体而言，基于认知神经科学的证据，评估和社会认知涉及可分离但相互作用的系统，该研究使用计算建模将人们如何看待他人（社会感知）的心理框架与人们如何评价他人结果（社会评估）的行为经济学账户正式整合起来。然后，它使用这些模型来预测人们在实验室和现场环境中如何对待不同社会群体的成员。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

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.