The Best Decisions Are Not the Best Advice: Making Adherence-Aware Recommendations

• 成果类型: Article; Early Access
• 署名作者: Grand-Clement, Julien; Pauphilet, Jean
• 署名单位: University of London; London Business School
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2023.01851
• 发表日期: 2024
• 关键词: prescriptive analytics; recommender systems; DISCRETION; Markov decision processes

摘要

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.