The best decisions are not the best advice: making adherence-aware recommendations

Grand-Clement, J and Pauphilet, J (2024) The best decisions are not the best advice: making adherence-aware recommendations. Management Science. ISSN 0025-1909 (In Press) OPEN ACCESS


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.

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Item Type: Article
Subject Areas: Management Science and Operations
Funder Name: Agence Nationale de la Recherche
Date Deposited: 11 Jun 2024 10:36
Date of first compliant deposit: 04 Dec 2023
Subjects: Decision-making
Markov processes
Expert systems
Last Modified: 11 Jun 2024 10:49

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