Doctoral Dissertation Research in DRMS: Integrating Managerial Insight and Optimal Algorithms

大量的实验工作表明，人类决策者在面对实验室中的不确定性时，通常会做出次优选择。由于这些偏离理性行为的行为会削减公司的利润，人们可能会得出结论，那些经常遇到这种选择的员工应该被替换为自动执行最佳策略的软件。另一方面，如果人类管理者有？管理洞察力？如果它们能够解决未来需求的一些不确定性，那么它们应该被视为有价值的资产而不是负债。人类对世界有着广泛的上下文意识，但他们的计算能力有限且容易出错。计算机相对来说是无意识的，但它们不会出错，并且具有几乎无限的处理能力。这个博士论文研究改进补助金，合作PI开发了一个模型，以研究企业如何最好地利用人类管理者？洞察力，同时用技术支撑他们的决策。 该项目将探讨是否更好地让人类管理人员调整自动化系统提出的建议，或者是否应该将管理见解转换为机器可用的格式，以便由软件进行自动处理，并拥有最终决策权。这些发现的影响，如供应链管理的问题将被探讨。该项目涉及一个实验方法，参与者在一个？报童问题？环境被赋予了管理洞察力，其形式是关于即将到来的需求的嘈杂信号。有了这条额外的信息，计算最佳洞察通知订购策略的经理应该优于没有此信息的最佳策略。 然而，在实验室中，人类受试者在做出这些决策时无法提高盈利能力，因为他们的上级需求信息被他们的低级计算能力削弱了。co-Pi将探索通过将预测任务（管理洞察力可以改善运营）与订单数量选择（一旦知道需求信息，就可以机械计算）分解，是否可以导致真正的最佳响应。

An extensive body of experimental work shows that human decision-makers routinely make sub-optimal choices when faced with uncertainty in the laboratory. Since these deviations from rational behavior cut into firm profits, one might conclude that employees who routinely encounter such choices should be replaced with software that is programmed to automatically implement optimal strategy. On the other hand, if human managers have ?managerial insight? that can resolve some of the uncertainty about future demand, then they should be considered valuable assets rather than liabilities. Humans have a broad contextual awareness of the world, but their computational abilities are limited and error-prone. Computers are relatively unaware, but they are unerring and have nearly unlimited processing capability. This Doctoral Dissertation Research Improvement Grant, the co-PI develops a model to examine how a business might best take advantage of human managers? insights while scaffolding their decision-making with technology. The project will explore whether it is better to have human managers adjust recommendations made by an automated system or whether managerial insight should be translated into a machine-usable format for automated processing with the software holding ultimate decision rights. Implications of these findings to issues like supply chain management will be explored.The project involves an experimental methodology where participants in a ?newsvendor problem? setting are endowed with managerial insight in the form of a noisy signal about upcoming demand. With this additional piece of information, a manager computing the optimal insight-informed ordering policy should outperform the optimal policy absent this information. However, in the lab, human subjects fail to improve profitability when making these decisions because their superior demand information is undercut by their inferior computational ability. The co-Pi will explore whether, by disaggregating the forecasting task (where managerial insight can improve operations) from the order quantity selection (which can be mechanically computed once demand information is known) can lead to truly optimal responses.