Research

Problem definition: Forecasts for key variables in a variety of operational decisions may be generated by human experts or by artificial intelligence (AI) technologies. A decision maker can benefit from the distinct advantages that each source may offer by providing AI assistance to the experts, allowing them to augment the information contained in the AI forecast by incorporating their own knowledge about the uncertain target variable. When multiple experts are available, accuracy can be further improved by utilizing the wisdom of crowds, forming a consensus by averaging each of their AI-assisted forecasts. However, the potential accuracy of a crowd of AI-assisted demand forecasters may be limited by two structural characteristics. First, because the AI assistance is valuable to each expert at an individual level, the opinion of the AI can end up being overrepresented in the crowd’s consensus. Second, the experts may fail to appropriately utilize the AI assistance when forming their forecasts by underemphasizing the information it provides.

Methodology/results: Using a stylized Bayesian model of information aggregation and a two-step forecast elicitation procedure, we develop a prescriptive method that can recover the most accurate consensus forecast given all information collectively observed by the AI and every expert in the crowd. This procedure works by pivoting the crowd's average AI-assisted forecast either toward or away from the crowd’s average initial forecast, elicited before the forecasters have observed the AI advice. We test the predictive performance of a data-driven pivoting method in three laboratory experiments. Across 3,188 participants and three AI accuracy treatments, we find that the pivoted forecasts match, and in many cases outperform, the AI-assisted crowd, the AI advice itself, and the unassisted crowd of forecasters.

Managerial implications: Firms can use the proposed forecasting method to improve human-AI collaborative forecasting accuracy by identifying and adjusting for the net bias in the crowd's AI-assisted forecast.

Making accurate order quantity decisions for new products with uncertain demand is an important problem faced by inventory managers. When products are heterogeneous, algorithmic solutions to the newsvendor problem developed using feature information and historical demand data can provide useful recommendations to the manager. When such recommendations are interpretable, existing research shows that the quality of algorithm-assisted operational decisions improves significantly. We study a class of interpretable, decision tree-based prescriptive algorithms that organize products into transparent and operationally meaningful categories, and assign a unique order quantity to each group. Using the method of "honest" recursive partitioning, we propose an algorithm which minimizes the empirical newsvendor risk associated with the tree-based policy while accounting for adaptive overfitting to the observed data. We analyze the total regret associated to the honest tree algorithm, and characterize its complex and equivocal relationship with model complexity and sample size. We summarize this relationship by expressing the optimal model complexity as a function of training sample size, and show that the optimal degree of segmentation of the product space increases sublinearly with training data. These results highlight that hard-coding interpretability as an attribute of a prescriptive newsvendor model can enable human managers to make better judgmental adjustments to the algorithm's recommendation, with limited cost to the prescriptive performance of the algorithm. Finally, we demonstrate the application and performance of our honest tree algorithm using real-world sales data from two different product categories.