 Simple rules to guide expert classificationsJongbin Jung, Connor Concannon, Ravi Shroff, Sharad Goel and Daniel Goldstein Journal of the Royal Statistical Society Series A, 2020, vol. 183, issue 3, 771-800 Abstract: 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. Date: 2020 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:jorssa:v:183:y:2020:i:3:p:771-800 Ordering information: This journal article can be ordered from Access Statistics for this article Journal of the Royal Statistical Society Series A is currently edited by A. Chevalier and L. Sharples More articles in Journal of the Royal Statistical Society Series A from Royal Statistical Society Contact information at EDIRC. |
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