Simple Algorithmic Rules for Complex Human Decisions

摘要: Decision makers must often choose a course of action under limited time with limited knowledge. In this work, we formalize longstanding observations about the efficacy of improper linear models to construct accurate yet easily applied rules that can help resource-constrained practitioners make better informed decisions that are consistent with their stated objectives. We find that simple rules can help substantially improve the performance of human experts, while rivaling the accuracy of complex prediction models that base decisions on considerably more information. Policy makers, however, may be reluctant to adopt such analytical tools due to the difficulty in anticipating, prior to deployment, the impact of resulting policies. In particular, one generally cannot use historical data to directly observe what would have happened had the recommended actions been taken. To address this issue, we present two strategies …