We introduce optimal regimes for algorithm-assisted human decision-making. Such regimes are decision functions of measured pre-treatment variables and enjoy a "superoptimality" property whereby they are guaranteed to outperform conventional optimal regimes currently considered in the literature. A key feature of these superoptimal regimes is the use of natural treatment values as input to the decision function. Importantly, identification of the superoptimal regime and its value require exactly the same assumptions as identification of conventional optimal regimes in several common settings, including instrumental variable settings. As an illustration, we study superoptimal regimes in an example that has been presented in the optimal regimes literature.

翻译：本文提出了算法辅助人类决策的最优策略机制。此类策略机制是预处理变量的决策函数，并具备“超优性”特征——即保证优于当前文献中常规最优策略机制的性能表现。这些超优策略的核心特征在于将自然处理值作为决策函数的输入变量。值得注意的是，在包括工具变量设置在内的多种常见场景下，识别超优策略机制及其价值所需的假设条件，与识别常规最优策略机制完全一致。作为示例，我们通过最优策略文献中的经典案例对超优策略机制进行了研究。