讲座题目：Augmenting the Operations Manager with a Prediction Machine预测机器环境下运营经理决策行为的机制研究

主讲人：芦涛  康涅狄格大学

讲座时间：2026年01月08日09:00

讲座地点：学院319

讲座摘要：

Firms increasingly use Artificial Intelligence (AI) enabled forecasting engines (“prediction machines”) to augment their managers' own forecasting capabilities and thus improve sales-and-operations planning outcomes. Deployment of a prediction machine may cause an unintended reduction in a manager's own forecasting effort which in turn diminishes the value of machine adoption. We model a firm facing uncertain demand that delegates a procurement quantity decision to a human manager who can exert effort to generate a demand prediction. The firm deploys a machine that provides the manager with a demand-prediction signal. We establish the conditions under which managerial effort reduction occurs and thus reduces the machine's potential value. Adopting a Bayesian persuasion approach, we show that partially disclosing the machine's prediction, either downplaying high predictions or exaggerating low predictions, can be optimal, depending on the product's cost-to-revenue ratio. A strategy of minimal obfuscation (to achieve effort) is optimal if the machine is more accurate than the human; however, maximal obfuscation (while maintaining effort) can be optimal if the human is more accurate. Our results imply that the firm may be better off tuning a machine to be less informative than its maximum capability.

人工智能驱动的预测系统（“预测机器”）正被广泛应用于企业运营管理实践，以辅助运营经理进行需求预测与采购决策。然而，预测机器的引入可能导致运营经理预测努力的内生性下降，从而削弱预测机器采用的价值。研究构建一个需求不确定环境下的企业决策模型：企业将采购数量决策委托给运营经理，该经理可以通过投入努力生成需求预测信息；同时，企业引入预测机器，并向经理提供由机器生成的需求预测信号。研究刻画了经理预测努力下降发生的条件，并分析了该努力下降如何降低预测机器的潜在价值。研究采用贝叶斯说服方法，预测机器预测信息的最优披露机制。结果表明，企业并非总是应当完全披露机器的预测结果。具体而言，最优的信息披露方式取决于产品的成本—收入比：通过弱化较高预测值或夸大较低预测值的部分信息披露可能是最优的。进一步分析发现，当预测机器的预测精度高于人类经理时，为激励管理者投入预测努力，最小程度的信息模糊化策略是最优的；而当人类经理的预测精度更高时，在维持其努力投入的前提下，最大程度的信息模糊化反而可能成为最优选择。综上表明，预测机器信息精度的提高并不必然带来更高的企业决策绩效。通过有策略地限制预测机器所传递的信息含量，使其低于其最大可实现水平，企业反而可能更有效地协调人机之间的预测努力，从而获得更优的整体运营绩效。

主讲人简介：

芦涛，康涅狄格大学商学院副教授，曾在荷兰伊拉斯姆斯大学鹿特丹管理学院任教。研究兴趣广泛涉及供应链管理、可持续运营和零工经济平台。研究成果发表在Management Science, Manufacturing & Service Operations Management, Operations Research, Information Systems Research, Production and Operations Management, and Transportation Science等顶级期刊上。目前担任Management Science、Service Science期刊副主编。