英文标题：
《Machine Predictions and Human Decisions with Variation in Payoffs and
  Skill》
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作者：
Michael Allan Ribers and Hannes Ullrich
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最新提交年份：
2020
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分类信息：

一级分类：Economics        经济学
二级分类：General Economics        一般经济学
分类描述：General methodological, applied, and empirical contributions to economics.
对经济学的一般方法、应用和经验贡献。
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一级分类：Quantitative Finance        数量金融学
二级分类：Economics        经济学
分类描述：q-fin.EC is an alias for econ.GN. Economics, including micro and macro economics, international economics, theory of the firm, labor economics, and other economic topics outside finance
q-fin.ec是econ.gn的别名。经济学，包括微观和宏观经济学、国际经济学、企业理论、劳动经济学和其他金融以外的经济专题
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英文摘要：
  Human decision-making differs due to variation in both incentives and available information. This constitutes a substantial challenge for the evaluation of whether and how machine learning predictions can improve decision outcomes. We propose a framework that incorporates machine learning on large-scale data into a choice model featuring heterogeneity in decision maker payoff functions and predictive skill. We apply this framework to the major health policy problem of improving the efficiency in antibiotic prescribing in primary care, one of the leading causes of antibiotic resistance. Our analysis reveals large variation in physicians\' skill to diagnose bacterial infections and in how physicians trade off the externality inherent in antibiotic use against its curative benefit. Counterfactual policy simulations show that the combination of machine learning predictions with physician diagnostic skill results in a 25.4 percent reduction in prescribing and achieves the largest welfare gains compared to alternative policies for both estimated physician as well as conservative social planner preference weights on the antibiotic resistance externality.
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