Judges, doctors and managers are among those decision makers who must often choose a course of action under limited time, with limited knowledge and without the aid of a computer. Because data‐driven methods typically outperform unaided judgements, resource‐constrained practitioners can benefit from simple, statistically derived rules that can be applied mentally. In this work, we formalize long‐standing observations about the efficacy of improper linear models to construct accurate yet easily applied rules. To test the performance of this approach, we conduct a large‐scale evaluation in 22 domains and focus in detail on one: judicial decisions to release or detain defendants while they await trial. In these domains, we find that simple rules rival the accuracy of complex prediction models that base decisions on considerably more information. Further, comparing with unaided judicial decisions, we find that simple rules substantially outperform the human experts. To conclude, we present an analytical framework that sheds light on why simple rules perform as well as they do.

中文翻译：

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指导专家分类的简单规则

决策者包括法官，医生和管理人员，他们通常必须在有限的时间内，以有限的知识而无需计算机的帮助下选择行动方案。由于数据驱动的方法通常胜过独立的判断，因此资源受限的从业人员可以从简单的，统计上得出的规则中受益，这些规则可以在精神上应用。在这项工作中，我们对关于不正确线性模型构造准确而易于应用的规则的有效性的长期观察进行了形式化。为了测试这种方法的效果，我们在22个领域中进行了大规模评估，并重点关注一个领域：在被告等待审判时释放或拘留被告的司法决定。在这些领域中，我们发现简单的规则可以与基于大量信息进行决策的复杂预测模型的准确性相媲美。此外，与无助的司法裁决相比，我们发现简单的规则大大优于人类专家。总而言之，我们提出了一个分析框架，阐明了为什么简单规则执行得如此出色。