講座題目：Augmenting the Operations Manager with a Prediction Machine預(yù)測機器環(huán)境下運營經(jīng)理決策行為的機制研究
主講人：蘆濤 康涅狄格大學(xué)
講座時間：2026年01月08日09:00
講座地點：學(xué)院319
講座摘要：
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.
人工智能驅(qū)動的預(yù)測系統(tǒng)（“預(yù)測機器”）正被廣泛應(yīng)用于企業(yè)運營管理實踐，以輔助運營經(jīng)理進行需求預(yù)測與采購決策。然而，預(yù)測機器的引入可能導(dǎo)致運營經(jīng)理預(yù)測努力的內(nèi)生性下降，從而削弱預(yù)測機器采用的價值。研究構(gòu)建一個需求不確定環(huán)境下的企業(yè)決策模型：企業(yè)將采購數(shù)量決策委托給運營經(jīng)理，該經(jīng)理可以通過投入努力生成需求預(yù)測信息；同時，企業(yè)引入預(yù)測機器，并向經(jīng)理提供由機器生成的需求預(yù)測信號。研究刻畫了經(jīng)理預(yù)測努力下降發(fā)生的條件，并分析了該努力下降如何降低預(yù)測機器的潛在價值。研究采用貝葉斯說服方法，預(yù)測機器預(yù)測信息的最優(yōu)披露機制。結(jié)果表明，企業(yè)并非總是應(yīng)當(dāng)完全披露機器的預(yù)測結(jié)果。具體而言，最優(yōu)的信息披露方式取決于產(chǎn)品的成本—收入比：通過弱化較高預(yù)測值或夸大較低預(yù)測值的部分信息披露可能是最優(yōu)的。進一步分析發(fā)現(xiàn)，當(dāng)預(yù)測機器的預(yù)測精度高于人類經(jīng)理時，為激勵管理者投入預(yù)測努力，最小程度的信息模糊化策略是最優(yōu)的；而當(dāng)人類經(jīng)理的預(yù)測精度更高時，在維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為最優(yōu)選擇。綜上表明，預(yù)測機器信息精度的提高并不必然帶來更高的企業(yè)決策績效。通過有策略地限制預(yù)測機器所傳遞的信息含量，使其低于其最大可實現(xiàn)水平，企業(yè)反而可能更有效地協(xié)調(diào)人機之間的預(yù)測努力，從而獲得更優(yōu)的整體運營績效。
主講人簡介：
蘆濤，康涅狄格大學(xué)商學(xué)院副教授，曾在荷蘭伊拉斯姆斯大學(xué)鹿特丹管理學(xué)院任教。研究興趣廣泛涉及供應(yīng)鏈管理、可持續(xù)運營和零工經(jīng)濟平臺。研究成果發(fā)表在Management Science, Manufacturing & Service Operations Management, Operations Research, Information Systems Research, Production and Operations Management, and Transportation Science等頂級期刊上。目前擔(dān)任Management Science、Service Science期刊副主編。
