Decision Making With Quantized Priors Leads to Discrimination
Decision Making With Quantized Priors Leads to Discrimination
复制标题
使用量化先验进行决策会导致歧视
DOI：
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发表时间：
2017
影响因子：
20.6
通讯作者：
Kush R. Varshney
中科院分区：
文献类型：
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作者：
L. Varshney;Kush R. Varshney
警察逮捕等决策场景中的种族歧视似乎违反了预期效用理论。借鉴信息科学的结果，我们讨论了一种基于信息的群体信号检测模型，该模型产生这样的行为，作为决策者基于品味的歧视或种族群体之间差异的替代解释。该模型使用最大化预期效用的决策规则（似然比检验），但将阈值的精度限制为一个小的离散集。精度约束来自人类记忆中的有限理性和用于估计先验的有限训练数据。当与人类决策的社会方面和预防性成本设置相结合时，该模型预测了在多项计量经济学研究中观察到的种族偏见。
Racial discrimination in decision-making scenarios such as police arrests appears to be a violation of expected utility theory. Drawing on results from the science of information, we discuss an information-based model of signal detection over a population that generates such behavior as an alternative explanation to taste-based discrimination by the decision maker or differences among the racial populations. This model uses the decision rule that maximizes expected utility—the likelihood ratio test—but constrains the precision of the threshold to a small discrete set. The precision constraint follows from both bounded rationality in human recollection and finite training data for estimating priors. When combined with social aspects of human decision making and precautionary cost settings, the model predicts the own-race bias that has been observed in several econometric studies.