Managerial Insight and Optimal Algorithms

• 成果类型: Article; Early Access
• 署名作者: Flicker, Blair
• 署名单位: University of South Carolina System; University of South Carolina Columbia
• 刊物名称: MANAGEMENT SCIENCE
• ISSN/ISSBN: 0025-1909
• DOI: 10.1287/mnsc.2022.03919
• 发表日期: 2025
• 关键词: Behavioral Operations; forecasting; Inventory management; human-algorithm interaction

摘要

Work is increasingly being completed by humans and algorithms in collaboration. A relative strength of humans in this partnership is their insight: private information that is relevant to the task but not available to computerized systems. I introduce a flexible model of managerial insight that accepts any distribution of demand, an advantage over alternative models, and apply it to the newsvendor setting. The optimal policy in this setting is theoretically straightforward but difficult for managers to implement directly. I propose a novel method called FIND that leverages historical forecasts to convert a point estimate of demand into a conditional probability distribution. In eight experiments, FIND outperforms all other ordering regimes considered over a broad range of conditions. To model subtle, unstructured demand signals, the last four experiments convey managerial insight nonquantitatively using images, colors, and tones. FIND performs equally well with these perceptual signals as it does with more traditional numerical signals.