The Algorithmic Assignment of Incentive Schemes
研究如何利用机器学习根据工人特征（如人口统计、人格特质、偏好）分配不同激励方案，通过两个大规模实验证明算法分配能显著提升绩效，尤其对高重复互动或回答一致的工人更有效。
The assignment of individuals with different observable characteristics to different treatments is a central question in designing optimal policies. We study this question in the context of increasing workers’ performance via targeted incentives using machine learning algorithms with worker demographics, personality traits, and preferences as input. Running two large-scale experiments, we show that (i) performance can be predicted by accurately measured worker characteristics, (ii) a machine learning algorithm can detect heterogeneity in responses to different schemes, (iii) a targeted assignment of schemes to individuals increases performance significantly above the level of the single best scheme, and (iv) algorithmic assignment is more effective for workers who have a high likelihood to repeatedly interact with the employer or who provide more consistent survey answers. This paper was accepted by Yan Chen, behavioral economics and decision analysis. Funding: Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) under Germany’s Excellence Strategy [Grant EXC 2126/1-390838866]. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2022.03362 .