编者按

在本系列文章中，我们对顶刊《Management Science》于8月份发布文章中进行了精选（共9篇），并总结其基本信息，旨在帮助读者快速洞察行业最新动态。

● 题目：Optimal Mechanism Design with Referral

带推荐的最优机制设计

● 原文链接：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.01540

● 作者：Hao Zhou , Jun Zhang

● 发布时间：2024-08-19

● 摘要：

This paper establishes the optimal selling mechanism when a seller can incentivize an existing buyer to refer his privately known potential buyer to participate. We identify three optimal channels for providing referral incentives. First, if the existing buyer declares that no potential buyer exists, his virtual value is penalized. Second, if the existing buyer refers the potential buyer to the seller, his virtual value is boosted. Third, in some scenarios where this carrots-and-sticks-via-virtual-value approach is insufficient for creating proper referral incentives, the existing buyer is then given a constant referral bonus for referring the potential buyer. We also provide conditions under which the optimal mechanism can be implemented using simple mechanisms. Finally, we demonstrate that the conventional resale mechanism is suboptimal.

本文对于“卖方可以激励现有买家去推荐其私下已知的潜在买家参与”这一情景，探究并建立了此时的最优销售机制。我们确定了提供推荐激励的三种最优途径。首先，如果现有买家声称不存在潜在买家，他的虚拟价值将受到惩罚。其次，如果现有买家将潜在买家推荐给卖方，他的虚拟价值将得到提升。第三，在某些情境下，当通过虚拟价值的奖惩机制不足以创造适当的推荐激励时，现有买家将获得一个固定的推荐奖金以鼓励他推荐潜在买家。我们还提供了可以通过简单机制实现最优机制的条件。最后，我们还证明了传统的转售机制是次优的。

● 题目：Don’t Fake It If You Can’t Make It: Driver Misconduct in Last-Mile Delivery

如果做不到，就别假装：最后一公里物流中的司机不当行为

● 原文链接：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.01829

● 作者：Srishti Arora , Vivek Choudhary , Pavel Kireyev

● 发布时间：2024-08-20

● 摘要：

In the last two decades, last-mile delivery (LMD) firms have seen immense growth fueled by the success of e-commerce, leading to faster and cheaper deliveries. Operating on thin margins, LMD firms strive for successful first-time deliveries to avoid the financial and reputational costs of reattempts. Delivery agents (DAs) are integral to LMD efficiency, influencing customer experience, delivery success, and productivity. However, most LMD performance enhancement research focuses on process, technology, and incentives, which presume workers will conform to procedures and monitoring tools will function flawlessly. Nevertheless, in practice, DAs deviate from expected behaviors, that is, indulge in misconduct, negatively affecting delivery efficiency, often resulting in returned parcels. One of the major forms of misconduct is entering fake remarks about deliveries, wherein DAs intentionally do not deliver the parcels and provide fake reasons for it. For instance, even without reaching a delivery address, a DA remarks “customer unavailable” and records a delivery failure. In this study, we collaborated with a leading Indian LMD firm and, using instrumental variable regression, found that such misconduct leads to a spillover productivity loss. This effect reduces the next day’s successful deliveries by 1.60% and first-time-right deliveries by 1.86%. We discuss misconduct’s correlation with factors such as task complexity and offer novel insights into how opportunistic circumstances can influence worker behavior.

在过去的二十年里，末端配送（LMD）公司因电子商务的成功而迅速增长，推动了更快、更便宜的配送服务。LMD公司在微薄的利润下运作，努力实现首次配送成功，以减少重试的财务和声誉成本。配送员（DA）是LMD效率的关键，影响客户体验、配送成功率和生产力。然而，大多数LMD性能提升的研究集中于流程、技术和激励机制，这些研究假设工人会遵守程序，并且监控工具会完美运行。然而，实际操作中，配送员往往偏离预期行为，进行不当操作，负面影响配送效率，导致包裹被退回。最常见的不当行为之一是录入虚假的配送备注，即配送员故意不配送包裹，并提供虚假的原因。例如，即使没有到达配送地址，配送员也可能备注“客户不在”，并记录为配送失败。在本研究中，我们与一家领先的印度LMD公司合作，利用工具变量回归分析发现，这种不当行为会导致生产力的溢出损失。此效应导致第二天的成功配送率下降1.60%，首次配送成功率下降1.86%。我们讨论了不当行为与任务复杂性等因素的关联，并提供了关于机会主义环境如何影响工人行为的新见解。

● 题目：Global Sensitivity Analysis via Optimal Transport

通过最优传输进行全局敏感性分析

● 原文链接：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.01796

● 作者：Emanuele Borgonovo , Alessio Figalli, Elmar Plischke, Giuseppe Savaré

● 发布时间：2024-08-21

● 摘要：

We examine the construction of variable importance measures for multivariate responses using the theory of optimal transport. We start with theical optimal transport formulation. We show that the resulting sensitivity indices are well-defined under input dependence, are equal to zero under statistical independence, and are maximal under fully functional dependence. Also, they satisfy a continuity property for information refinements. We show that the new indices encompass Wagner’s variance-based sensitivity measures. Moreover, they provide deeper insights into the effect of an input’s uncertainty, quantifying its impact on the output mean, variance, and higher-order moments. We then consider the entropic formulation of the optimal transport problem and show that the resulting global sensitivity measures satisfy the same properties, with the exception that, under statistical independence, they are minimal, but not necessarily equal to zero. We prove the consistency of a given-data estimation strategy and test the feasibility of algorithmic implementations based on alternative optimal transport solvers. Application to the assemble-to-order simulator reveals a significant difference in the key drivers of uncertainty between the case in which the quantity of interest is profit (univariate) or inventory (multivariate). The new importance measures contribute to meeting the increasing demand for methods that make black-box models more transparent to analysts and decision makers.

我们利用最优传输理论构建多元响应变量的重要性度量。首先，从经典的最优传输公式出发，我们展示了在输入变量具有依赖性的情况下，所得的敏感性指数是定义良好的；在统计独立的情况下，这些指数等于零；在完全功能依赖的情况下，敏感性指数达到最大值。此外，它们在信息细化时满足连续性特性。我们表明，这些新指标涵盖了Wagner的基于方差的敏感性度量，且能够更深入地揭示输入不确定性对输出均值、方差及高阶矩的影响。随后，我们考虑了最优传输问题的熵公式，并证明了所得的全局敏感性度量也满足相同的性质，唯一的例外是，在统计独立的情况下，这些度量的值为最小，但不一定等于零。我们还证明了一种基于给定数据的估计策略的一致性，并测试了基于替代最优传输求解器的算法实现的可行性。在组装到订单模拟器中的应用揭示了，当关注的变量是利润（单变量）或库存（多变量）时，不确定性的关键驱动因素存在显著差异。新的重要性度量方法有助于满足日益增长的需求，使得黑箱模型对分析师和决策者更加透明。

● 题目：Identity Disclosure and Anthropomorphism in Voice Chatbot Design: A Field Experiment

语音聊天机器人设计中的身份披露与拟人化：一项实地实验

● 原文链接：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.03833

● 作者：Yuqian Xu , Hongyan Dai , Wanfeng Yan

● 发布时间：2024-08-26

● 摘要：

Fueled by the widespread adoption of algorithms and artificial intelligence, the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use voice chatbots, particularly by leveraging the two design features identity disclosure and anthropomorphism, and evaluate their impact on the firm operational performance. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned 11,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that disclosing the identity of the chatbot at the beginning of the conversation negatively affects operational performance, leading to around 11% reduction in the response probability. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response probability, conversation length, and the probability of order acceptance intention by over 5.6%, 24.9%, and 10.1%, respectively. Moreover, even when the chatbot’s identity is disclosed along with humanizing features, the operational outcomes still improve. This finding suggests that enhancing anthropomorphism may potentially counteract the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation for the performance improvement—the enhanced trust between humans and algorithms—and provide empirical evidence that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are currently being implemented and utilized by our collaborator platform.

随着算法和人工智能的广泛应用，聊天机器人的使用在各类商业场景中变得越来越流行。本文研究了如何有效且适当地使用语音聊天机器人，特别是通过身份披露和拟人化两个设计特征，并评估其对企业运营绩效的影响。我们与一家大型货运共享平台合作，进行了实地实验，随机将11,000名货车司机分配给平台的语音聊天机器人调度员进行外呼联系。实证结果表明，在对话开始时披露聊天机器人的身份会对运营绩效产生负面影响，导致响应概率下降约11%。然而，通过添加拟人化特征（如感叹词和填充词）对语音聊天机器人进行人性化处理，可以显著提高响应概率、对话时长和订单接受意图的概率，分别增长超过5.6%、24.9%和10.1%。此外，即使在披露聊天机器人身份的情况下，加入拟人化特征后，运营结果依然有所改善。这一发现表明，增强拟人化特征可能抵消聊天机器人身份披露的负面影响。最后，我们提出了对绩效提升的一个合理解释——人类与算法之间的信任增强，并提供了实证证据表明，带有拟人化特征的聊天机器人调度员更容易让司机披露信息。我们提出的拟人化改进方案目前已被合作平台实施并使用。

● 题目：Adaptive Preference Measurement with Unstructured Data

利用非结构化数据的自适应偏好测量

● 原文链接 ：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.03775

● 作者：Ryan Dew

● 发布时间：2024-08-28

● 摘要：

Many products are most meaningfully described using unstructured data such as text or images. Unstructured data are also common in e-commerce, in which products are often described by photos and text but not with standardized sets of attributes. Whereas much is known about how to efficiently measure consumer preferences when products can be meaningfully described by structured attributes, there is scant research on doing the same for unstructured data. This paper introduces a real-time, adaptive survey design framework for measuring preferences over unstructured data, leveraging Bayesian optimization. By adaptively choosing items to display based on uncertainty around a nonparametric utility model, the proposed method maximizes information gain per question, enabling quick estimation of individual-level preferences. The approach operates on embeddings of the unstructured data, thereby eliminating the requirement for manual coding of product attributes. We apply the method to measuring preferences over clothing and highlight its potential for both the general task of marketing research and the specific task of designing customer onboarding surveys to mitigate the cold-start recommendation problem. We also develop methods for interpreting the nonparametric utility functions, which allow us to reconstruct consumer valuations of discrete attributes, even for attributes that were not considered or available a priori.

许多产品最有意义的描述是通过非结构化数据，如文本或图像。在电子商务中，非结构化数据也很常见，产品通常通过照片和文本来描述，而不是标准化的属性集。虽然关于如何有效衡量消费者偏好（当产品可以通过结构化属性进行有意义的描述）已有大量研究，但针对非结构化数据的偏好测量研究却很少。本文引入了一种基于贝叶斯优化的实时自适应调查设计框架，用于衡量非结构化数据的偏好。通过根据非参数效用模型的不确定性自适应地选择展示的商品，该方法最大化了每个问题的信息增益，从而能够快速估计个体层面的偏好。该方法基于非结构化数据的嵌入操作，消除了对产品属性进行手动编码的需求。我们将该方法应用于服装偏好的测量，并强调了其在营销研究的一般任务和设计客户入职调查以缓解冷启动推荐问题的具体任务中的潜力。我们还开发了解释非参数效用函数的方法，允许我们重建消费者对离散属性的评估，即使这些属性事先没有被考虑或可用。

● 题目：Coopetition in Platform-Based Retailing: On the Platform’s Entry

基于平台零售的竞合关系：平台的进入问题

● 原文链接 ：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.00260

● 作者：Lian Qi , Haotian Song , Wenqiang Xiao

● 发布时间：2024-08-28

● 摘要：

We study the dynamic incentive interactions between a platform and a third-party seller over two stages, where the seller exerts product-value-enhancement effort in the first stage in anticipation of the platform’s potential entry in the second stage. Upon entry, the platform can exert effort to boost the value of the product sold by the platform, with positive spillovers to the value of the product sold by the platform. We show the existence of both the protective and the receptive regimes, characterizing the necessary and sufficient conditions for each regime. These conditions are of thresholds type with respect to the parameters including the degree of spillover, the referral rate, and the competition intensity. In the protective (receptive) regime, the seller is worse (better) off with the platform’s entry than without, thereby distorting effort downward (upward) from the first best to deter (induce) the platform’s entry to the products of intermediate value. Notably, if the spillover effect is negative, only the protective regime will arise. We also provide the necessary and sufficient conditions under which the platform’s nonentry commitment strictly improves the platform’s profits by restoring the seller’s effort to the first best, achieving the win-win outcome for both the platform and the seller.

我们研究了一个平台与第三方卖家在两个阶段中的动态激励互动。在第一阶段，卖家为提升产品价值进行努力，预期平台可能在第二阶段进入市场。平台进入后，可以通过努力提升其售卖产品的价值，并对卖家产品的价值产生正向溢出效应。我们展示了“保护性”和“接受性”两种机制的存在，并刻画了每种机制的必要和充分条件。这些条件基于阈值类型，涉及溢出效应程度、推荐率和竞争强度等参数。在“保护性”机制中，卖家的情况因为平台进入变得更糟，从而减少努力以阻止平台进入；而在“接受性”机制中，卖家的情况因平台进入而改善，因而增加努力以吸引平台进入，尤其是针对中等价值的产品。值得注意的是，如果溢出效应为负，则只会出现“保护性”机制。我们还提供了平台不进入承诺能够严格改善其利润的必要和充分条件，通过恢复卖家的努力至最佳水平，平台和卖家均能实现双赢。

● 题目：Feature Misspecification in Sequential Learning Problems

顺序学习问题中的特征误设

● 原文链接 ：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.00328

● 作者：Dohyun Ahn , Dongwook Shin , Assaf Zeevi

● 发布时间：2024-08-29

● 摘要：

We consider a of sequential learning problems where a decision maker must learn the unknown statistical characteristics of a finite set of alternatives (or systems) using sequential sampling to ultimately select a subset of “good” alternatives. A salient feature of our problem is that system performance is governed by a set of features. The decision maker postulates the dependence on these features to be linear, but this model may not precisely represent the true underlying system structure. We show that this misspecification, if not managed properly, can lead to suboptimal performance because of a phenomenon identified as sample-selection endogeneity. We propose a prospective sampling principle—a new approach that eliminates the adverse effects of misspecification as the number of samples grows large. The proposed principle applies across a very general of widely used sampling policies, enjoys strong asymptotic performance guarantees, and exhibits effective finite-sample performance in numerical experiments.

我们研究了一类顺序学习问题，其中决策者必须通过顺序采样来学习一组有限备选方案（或系统）的未知统计特征，最终选择出一部分“优良”方案。我们问题的显著特征在于系统性能受一组特征的影响。决策者假设这些特征之间的依赖关系是线性的，但这一模型可能无法准确表示真实的系统结构。我们表明，如果未能正确处理这种误设，可能会因为一个被称为样本选择内生性（sample-selection endogeneity）的现象导致次优表现。为此，我们提出了一种前瞻性采样原则，这是一种新的方法，随着样本数量的增加，它能够消除误设带来的不利影响。该原则适用于一类非常广泛的常用采样策略，具有强大的渐近性能保证，并且在数值实验中表现出有效的有限样本性能。

● 题目：Fast Delivery: B2B Field Experiments Amid COVID-19 Outbreak and New Normal

快速交付：COVID-19爆发和新常态下的B2B实地实验

● 原文链接 ：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2022.03523

● 作者：Meng Li , Shichen Zhang

● 发布时间：2024-08-29

● 摘要：

Procuring essential products during disasters can save lives and livelihoods; nevertheless, limited research has explored this critical issue. In this paper, we collaborate with a buying company to run a unique B2B field experiment during the early COVID-19 outbreak that prompts an urgent need for personal protective equipment. We find that suppliers would deliver products faster by quoting a significantly shorter lead time when the buying company purchases products for donation rather than for resale. Similarly, suppliers quote a shorter lead time when the company provides crisis information—the number of COVID-19 cases in the region with high crisis levels—to highlight the urgent need for products. Interestingly, faster delivery does not come at the expense of the buying company. Buyers receive the products faster but also pay less. We collaborate with the same company to rerun the field experiment in the new normal and find that the buyer’s intent of donation leads to shorter lead time but not to lower wholesale price. Moreover, providing the COVID-19 information of the local community to suppliers has no impact on their decisions. Overall, our paper provides the first empirical evidence on suppliers’ lead time and wholesale price decisions during and after a disaster, thereby helping companies to deliver emergency products in a timely and efficient manner as well as designing their philanthropic and information strategies.

在灾难期间采购必需品能够挽救生命和生计，但对此关键问题的研究却较为有限。本文与一家采购公司合作，在COVID-19疫情早期进行了一项独特的B2B实地实验，旨在满足个人防护设备的紧急需求。我们的研究发现，当采购公司为捐赠而非转售购买产品时，供应商会显著缩短交货时间。同样，当公司提供危机信息（例如高危地区的COVID-19病例数）以强调产品的紧急需求时，供应商也会缩短交货时间。有趣的是，快速交付并不会增加采购公司的成本。买家不仅能更快收到产品，还能支付更低的价格。我们与同一公司合作，在新常态下重新进行实验，发现买家的捐赠意图仍然会缩短交货时间，但不会降低批发价格。此外，向供应商提供当地COVID-19信息对其决策没有影响。总体而言，我们的研究首次提供了灾难期间及之后供应商交货时间和批发价格决策的实证证据，有助于公司及时高效地交付紧急产品，并优化其慈善和信息策略。

● 题目：The Continuous-Time Joint Replenishment Problem: ϵ-Optimal Policies via Pairwise Alignment

连续时间联合补货问题：通过成对对齐的ϵ-最优策略

● 原文链接 ：https://pubsonline.informs.org/doi/abs/10.1287/mnsc.2023.00705

● 作者：Danny Segev

● 发布时间：2024-08-30

● 摘要：

The main contribution of this paper resides in developing a new algorithmic approach for addressing the continuous-time joint replenishment problem, termed ΨΨ-pairwise alignment. The latter mechanism, through which we synchronize multiple economic order quantity models, allows us to devise a purely combinatorial algorithm for efficiently approximating optimal policies within any degree of accuracy. As a result, our work constitutes the first quantitative improvement over power-of-2 policies, which have been state-of-the-art in this context since the mid-1980s. Moreover, in light of recent intractability results, by proposing an efficient polynomial-time approximation scheme for the joint replenishment problem, we resolve the long-standing open question regarding the computational complexity of thisical setting.

本文的主要贡献在于开发了一种新的算法方法来解决连续时间联合补货问题，称为$$\Psi$$-成对对齐。该机制通过同步多个经济订货量模型，使我们能够设计一种纯组合的算法，有效地在任何精度范围内近似最优策略。因此，我们的研究是自20世纪80年代中期以来，首次在这一领域对二次幂策略进行量化改进的工作。此外，鉴于最近的难解性结果，通过提出一种高效的多项式时间近似方案来解决联合补货问题，我们解决了这一经典问题的计算复杂性长期存在的开放问题。