Abstract
Many high-stake decisions follow an expert-in-loop structure in that a human operator receives recommendations from an algorithm but is the ultimate decision maker. Hence, the algorithm’s recommendation may differ from the actual decision implemented in practice. However, most algorithmic recommendations are obtained by solving an optimization problem that assumes recommendations will be perfectly implemented. We propose an adherence-aware optimization framework to capture the dichotomy between the recommended and the implemented policy and analyze the impact of partial adherence on the optimal recommendation. Our framework provides useful tools to analyze the structure and to compute optimal recommendation policies that are naturally immune against such human deviations and are guaranteed to improve upon the baseline policy. This paper was accepted by Nicolas Stier-Moses, special issue on the human-algorithm connection. Funding: J. Grand-Clément was supported by the Agence Nationale de la Recherche [Grant 11-LABX-0047] and Hi! Paris. Supplemental Material: The data files are available at https://doi.org/10.1287/mnsc.2023.01851 .
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