Collaborative Research: An Interdisciplinary Approach to Predicting Unequal Treatment

社会群体之间的结果差异几乎存在于现代人类社会的每个领域，包括教育，劳动力市场和医疗保健。无论是基于性别、种族、年龄还是其他标记，人们对待他人的方式都会出现基于群体的差异，即使社会群体信息无关紧要，即使人们明确拒绝社会刻板印象。尽管在记录这些差异方面取得了进展，但对其起源仍知之甚少。目前的研究重点是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，这些模式总体上有助于社会治疗差异。主要目标是以足够的精确度来描述不平等待遇的起源，以支持人们如何对待不同社会群体成员的准确、特定背景的预测。对这一合作努力的支持扩大了有抱负的科学家获得培训的机会，为向当地社区进行科学宣传提供了机会，并最终促进了对社会差距的科学理解，对衡量和解决歧视问题的努力产生了影响，在记录世界上存在的待遇差距方面取得了重大进展。另外，在理解人们如何在实验室中思考不同社会群体方面取得了实质性进展。然而，鉴于人们可以被分类的方式众多，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实地观察联系起来的社会思想和行为模型一直具有挑战性。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将在何时以及如何被（分离）。具体来说，基于认知神经科学的证据，即价值和社会认知参与可分离但相互作用的系统，该研究使用计算建模来正式整合人们如何看待他人（社会感知）的心理框架与人们如何评价他人结果（社会价值）的行为经济学账户。该奖项反映了NSF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.