作者：Evelyn Yao 清华大学本科在读

在“Management Science近期论文精选”中，我们有主题、有针对性地选择了Management Science中一些有趣的文章，不仅对文章的内容进行了概括与点评，而且也对文章的结构进行了梳理，旨在激发广大读者的阅读兴趣与探索热情。在本期“论文精选”中，我们以“信息系统”为主题，分别探究了“在线平台用户反馈”、“健康信息的交流对医疗质量和效率的影响”、“线上市场的雇佣选择”、“机器学习的算法透明化”和“专业房东在P2P市场上的作用”五方面主题，涉及线性优化、贝叶斯-纳什均衡理论、隐马尔可夫模型、条件逻辑模型、信号博弈等多项内容。

推荐文章1

发表时间：2022.7.01

● 题目：The Square Root Agreement Rule for Incentivizing Truthful Feedback on Online Platforms

用平方根协议规则来激励在线平台的真实反馈

● 期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2022.4375

● 作者：Vijay Kamble, Nihar Shah, David Marn, Abhay Parekh, Kannan Ramchandran

● 关键词：information systems: IT policy and management （信息系统：IT政策管理）• electronic commerce（电子商务） • information systems: systems analysis and design （信息系统：系统分析与设计）• systems design and implementation（系统设计与执行） • economics: game theory and bargaining theory（经济学：博弈论与协商理论）

● 摘要：

A major challenge in obtaining evaluations of products or services on e-commerce platforms is eliciting informative responses in the absence of verifiability. This paper proposes the square root agreement rule (SRA): a simple reward mechanism that incentivizes truthful responses to objective evaluations on such platforms. In this mechanism, an agent gets a reward for an evaluation only if the answer matches that of a peer agent, where this reward is inversely proportional to a popularity index of the answer. This index is defined to be the square root of the empirical frequency at which any two agents performing the same evaluation agree on the particular answer across evaluations of similar entities operating on the platform. Rarely agreed-on answers thus earn a higher reward than answers for which agreements are relatively more common. We show that in the many tasks regime, the truthful equilibrium under SRA is strictly payoff-dominant across large classes of natural equilibria that could arise in these settings, thus increasing the likelihood of its adoption. Although there exist other mechanisms achieving such guarantees, they either impose additional assumptions on the response distribution that are not generally satisfied for objective evaluations or they incentivize truthful behavior only if each agent performs a prohibitively large number of evaluations and commits to using the same strategy for each evaluation. SRA is the first known incentive mechanism satisfying such guarantees without imposing any such requirements. Moreover, our empirical findings demonstrate the robustness of the incentive properties of SRA in the presence of mild subjectivity or observational biases in the responses. These properties make SRA uniquely attractive for administering reward-based incentive schemes (e.g., rebates, discounts, reputation scores, etc.) on online platforms.

在电子商务平台上获得产品或服务评价的一个主要挑战是我们一直在对不可验证的信息做出反应。本文提出了平方根协议规则(SRA)：一种简单的奖励机制，以激励人们在这类平台上客观评价、真实反应。在这一机制中，只有当答案与同行代理的答案一致时，代理才会获得评价的奖励，这种奖励与答案的流行指数成反比。这个指数被定义为一种经验频率的平方根，这种平方根是两个代理不仅在此产品的评价一致、而且在类似实体的评价中对特定答案达成一致的一种经验频率的平方根。因此，达成一致意见较少的答案比达成一致意见相对较多的答案获得更多的奖励。我们表明，在许多任务制度中，SRA方法下的真正均衡是严格的报酬主导型，这就增加了采用此规则的可能性。尽管存在其他实现这种保证的机制，但它们要么对响应目标的概率分布施加了额外的假设，而这些假设并不能满足我们客观评价的需要，要么它们只在每个代理人进行大量评价并承诺对每个评价使用相同的策略时才会激励真实行为。SRA是第一种已知的激励机制，在不施加任何此类要求的情况下满足此类保证。此外，我们的经验发现证明了在反应中存在轻微的主观性或观察偏差的情况下，SRA法激励特性的鲁棒性。这些特性使得SRA法对于在网络平台上管理基于奖励的激励计划（例如，回扣、折扣、声誉成绩等）是极具吸引力的。

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● 点评：

在平台的成长过程中，平台运营方迫切地需要用户所提出的真实有效的建议来助力平台改进与发展；不仅如此，用户根据他们的个人经验为产品或者服务提供反馈，也是在线市场的重要组成部分。而针对现在大量评价偏见强、系统难以核实的情况，SRA法为我们提供了一种强真实性、高鲁棒性的检测激励信息反馈方法，将此作为一种信息过滤机制，并将其应用到机器学习上，将对产品运营和平台发展的方向产生很大的影响。作者用线性优化的方法来计算用户的最佳响应，又用贝叶斯-纳什均衡理论探究奖励机制中的博弈论问题。但正如作者自己也强调，这一机制只是在大部分情况下可以实现，也不能把它定义为任何情况下完全的解决方案。在平台设计者做出设计决定时，SRA也可以为他们提供概念性的指导。

 

推荐文章2

发表时间：2022.7.29

● 题目：The Effects of Health Information Exchange Access on Healthcare Quality and Efficiency: An Empirical Investigation

健康信息的交流对医疗质量和效率的影响：一个实证调查

● 期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2022.4378

● 作者：Ramkumar Janakiraman, Eunho Park,b Emre M.Demirezen, Subodha Kumard

● 关键词：healthcare management（医疗管理） • health information exchange (HIE) （健康资讯交流）• healthcare and IT （医疗与计算机技术）• readmission （重新入院）• length of stay （治疗时间）• health information technology（健康资讯科技）

● 摘要：

Health information exchanges (HIEs) are designed to improve the quality and efficiency of healthcare by facilitating improved information sharing between health entities. This study systematically examines the impact of HIE use in emergency departments (EDs) on the quality and efficiency of medical care. We focus on the length of stay (LOS) and the 30-day readmission rate to capture healthcare efficiency and quality, respectively. We also examine whether the breadth of patient health information and physicians’ experience with the HIE moderates these effects. We leverage a unique panel data that tracks actual HIE access by physicians who practice in a set of hospitals that participate in the focal HIE. The patient-level encounter data set—which involves more than 80,000 ED encounters attended by more than 300 physicians over a 19-month period—comprises detailed medical provider information, patient-level medical information, and various other information related to procedures that were performed. After controlling for a battery of patient-specific, physician-specific, disease-specific, and ED visit-specific variables, our results show that HIE access in information-intensive environments (such as EDs) reduces LOS and 30-day readmission rate. We find that breadth of patient health information and physicians’ HIE experience amplify these benefits. We account for endogeneity issues and perform additional falsification tests and robustness checks. We document that benefits of HIE access are amplified for noninjury, chronic condition, and uncommon diagnoses related patient visits. Based on our results, we offer insights to practitioners and academicians alike on how HIEs can yield better patient-level and provider-level outcomes.

健康信息交换（HIEs）旨在通过促进卫生实体之间的信息共享来提高医疗质量和效率。本研究系统地研究了急诊科（EDs）使用HIE对医疗质量和效率的影响。我们关注住院时间（LOS）和30天再入院率，以分别掌握医疗效率和质量。我们还研究了病人健康信息的全面性和医生对HIE的经验是否能调节这些影响。我们利用一个独特的面板数据，跟踪一所医院执业医生的实际HIE使用情况。患者层面的数据集--涉及300多名医生在19个月内的80,000多次急救遭遇--包括详细的医疗提供者信息、患者层面的医疗信息以及与所实施的程序有关的各种其他信息。在控制了一系列针对患者、针对医生、针对疾病和针对ED访问的变量后，我们的结果显示，在信息密集的环境（如ED）中使用HIE可以减少LOS和30天再入院率。我们发现，病人健康信息的全面性和医生的HIE经验放大了这些好处。我们考虑了内生性问题，并进行了额外的证伪测试和鲁棒性检查。我们记录了HIE访问的好处在非伤害、慢性病和不常见的诊断相关的病人访问中被放大。基于我们的研究结果，对于HIE如何在病人层面和供应商层面更好的起作用，我们为从业者和学术界提供了自己的见解。

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● 点评：

在美国，虽然医疗费用在逐年增长，但是医疗质量却没能得到相应的改善。相比于促进组织内病人信息共享的EHRs，HIEs旨在促进组织间病人信息的共享，提高诊疗效率、降低诊疗成本。相比于其他理论性较强的文章，本文更倾向于实践也更易读，文章针对一个计量经济模型展开，基于纽约北部四家医院的病人健康数据信息，证明了HIE在各医院间的使用有助于改善医疗结果、缩短住院时间和降低再入院风险，为HIE投入大规模实践提供了数据支撑。

推荐文章3

发表时间：2022.8.26

● 题目：Learning to Successfully Hire in Online Labor Markets

学会在线上市场上成功招聘在线劳动力

● 期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2022.4426

● 作者：Marios Kokkodis, Sam Ransbotham

● 关键词：employer evolution （雇主的演变）• successful hiring choices（成功的雇佣选择） • online labor markets （线上劳动力市场）• empirical analysis（经验分析法）

● 摘要：

Hiring in online labor markets involves considerable uncertainty: which hiring choices are more likely to yield successful outcomes and how do employers adjust their hiring behaviors to make such choices? We argue that employers will initially explore the value of available information. When employers observe successful outcomes, they will keep reinforcing their hiring strategies; but when the outcomes are unsuccessful, employers will adjust their hiring behaviors. To investigate these dynamics, we propose a two-component framework that links hiring choices with task outcomes. The framework’s first component, a Hidden Markov Model, captures how employers transition from unsuccessful to successful hiring decisions. The framework’s second component, a conditional logit model, estimates employer hiring choices. Analysis of 238,364 hiring decisions from a large online labor market shows that, often, employers initially explore cheaper contractors with a lower reputation. When these options result in unsuccessful outcomes, employers learn and adjust their hiring behaviors to rely more on reputable contractors who are not as cheap. Such hiring tends to be successful, guiding employers to reinforce their hiring processes. As a result, the market observes employers transition from cheaper, lower-reputation options with poorer performance to more expensive reputable options with better performance. We attribute part of this behavior to employer confidence and risk attitude, which can change over time. This work is the first to investigate how employers learn to make successful hiring choices in online labor markets. As a result, it provides platform managers with new knowledge and analytics tools to target employer interventions.

在线劳动力市场的招聘涉及相当大的不确定性：哪些招聘选择更有可能产生成功的结果，以及雇主会怎样调整他们的招聘行为以做出这样的选择？我们认为，雇主最初会探索现有信息的价值。当雇主观察到成功的结果时，他们会不断强化他们的雇佣策略；但当结果不成功时，雇主会调整他们的雇佣行为。为了研究这些动态，我们提出了一个由两部分组成的框架，将招聘选择与任务结果联系起来。该框架的第一部分，即隐马尔可夫模型，反映了雇主如何从不成功的招聘决定过渡到成功的招聘决定的过程。该框架的第二部分，一个条件Logit模型，估计了雇主的雇用选择。对一个大型在线劳动力市场的238,364项雇用决定的分析表明，雇主最初通常会探索声誉较低的便宜承包商。当这些选择导致不成功的结果时，雇主就会学习并调整他们的雇用行为，更多地依靠声誉好但不便宜的承包商。这样的雇用往往是成功的，也可以引导雇主修正他们的雇用过程。因此，市场观察到雇主从价格较低、信誉较差的选择过渡到价格较高、信誉较好的选择。我们将这一行为的一部分归因于雇主的信心和他们面对风险的态度，这可能随着时间的推移而改变。这项工作是第一个研究雇主如何学习在在线劳动力市场上做出成功雇佣选择的工作。因此，它为平台管理者提供了新的知识和分析工具，以便有针对性地对雇主进行干预。

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● 点评：

在线平台将独立的雇主与完成各种任务的承包商联系起来，可是这样的市场不确定性较强，雇主对于承包商的能力与声誉等潜在信息都不甚了解，而错误的雇佣选择会直接导致雇主放弃此在线平台，退出市场。本文为在线劳动力市场提供了HMM框架，便于管理者更好地了解市场的雇主群体，也便于帮助雇主群体更好地做出招聘决策。其对隐马尔可夫模型与条件Logit模型的使用都是我们可以学习的地方。

推荐文章4

发表时间：2022.7.05

● 题目：Algorithmic Transparency with Strategic Users

算法透明化与战略用户

● 期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2022.4475

● 作者：Qiaochu Wang, Yan Huang, Stefanus Jasin, Param Vir Singh

● 关键词：algorithmic transparency（算法透明度） • game theory（博弈论） • machine learning（机器学习） • strategic classification（战略分类） • signaling game（信号博弈）

● 摘要：

Should firms that apply machine learning algorithms in their decision making make their algorithms transparent to the users they affect? Despite the growing calls for algorithmic transparency, most firms keep their algorithms opaque, citing potential gaming by users that may negatively affect the algorithm’s predictive power. In this paper, we develop an analytical model to compare firm and user surplus with and without algorithmic transparency in the presence of strategic users and present novel insights. We identify a broad set of conditions under which making the algorithm transparent actually benefits the firm. We show that, in some cases, even the predictive power of the algorithm can increase if the firm makes the algorithm transparent. By contrast, users may not always be better off under algorithmic transparency. These results hold even when the predictive power of the opaque algorithm comes largely from correlational features and the cost for users to improve them is minimal. We show that these insights are robust under several extensions of the main model. Overall, our results show that firms should not always view manipulation by users as bad. Rather, they should use algorithmic transparency as a lever to motivate users to invest in more desirable features.

在决策中应用机器学习算法的公司是否应该让他们的算法对用户透明？尽管要求算法透明的呼声越来越高，但大多数公司仍将其算法保持在不透明状态，理由是用户的潜在博弈可能对算法的预测能力产生负面影响。在本文中，我们开发了一个分析模型。在存在战略用户的情况下，此模型用以比较有无算法透明的公司和用户盈余，并提出了新的见解。我们确定了一套广泛的条件，在这些条件下，使算法透明化实际上有利于公司。我们表明，在某些情况下，如果公司使算法透明，甚至算法的预测能力也会增加。相比之下，在算法透明的情况下，用户不一定会有更好的收益。甚至在不透明算法的预测能力主要来自于相关特征，并且用户改进这些特征的成本很小的情况下，这些结果也能成立。我们表明我们的结论在主要模型的几个扩展下是稳健的。总的来说，我们的结果表明，公司不应该总是将用户的操纵视为坏事。相反，他们应该把算法的透明度作为一个杠杆来激励用户于在更理想的功能上进行投资。

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公司是否应把他们的机器学习算法向用户保持透明？其实对算法透明化的探究是一个全新的主题，这也与不对称信息的研究密切相关。本文中，作者利用了信号博弈、贝叶斯均衡等多方面的知识，得到了“在算法透明化的条件下，公司发展更好，用户不一定有更好的发展”的稳健性结论，如果你对相关内容感兴趣，欢迎你点开链接进行阅读！

推荐文章5

发表时间：2022.7.18

● 题目：Regulating Professional Players in Peer-to-Peer Markets: Evidence from Airbnb

监管在P2P市场中的专业玩家：爱彼迎的证据

● 期刊：Management Science

● 原文链接：https://doi.org/10.1287/mnsc.2022.4480

● 作者：Wei Chen, Zaiyan Wei, Karen Xie

● 关键词：peer-to-peer markets （P2P市场）• professional players （职业选手）• competition （竞争）• differentiation （差异化）• platform economics （平台经济）• Airbnb（爱彼迎）

● 摘要：

We study professional players and their roles in peer-to-peer (P2P) markets. Most notably, P2P home-sharing platforms (e.g., Airbnb) consist of both professional hosts and nonprofessional individual hosts. What are the roles of the professionals? Should home-sharing platforms regulate their participation? Professional hosts may primarily offer properties that nonprofessional hosts would not supply and attract more guests—the differentiation effect. Or they may mostly supply similar properties and compete with the nonprofessionals—the competition effect. Using a unique data set of Airbnb listings, we first find that professional hosts’ properties are more expensive and have superior characteristics than nonprofessionals’. Second, we capitalize on a quasi-experiment in which Airbnb capped the number of properties a host can manage in several cities in the United States to determine the roles of professional hosts. With different predictions (about the policy impacts) under the differentiation versus competition effects, we find evidence suggesting the dominance of the latter. In particular, the policy increased the supply from nonprofessional hosts, and the price level of nonprofessional properties as a group went up after the policy. However, our findings of heterogeneity in policy impacts suggest that the dominance of competition is less prominent in certain markets. Finally, we find that the platform was not worse off in attracting reservations or securing revenue after the policy. Our findings contribute to both theory and practice as they reveal the roles of professional players and how P2P platforms can manage their participation.

我们研究职业玩家和他们在点对点（P2P）市场中的作用。最值得注意的是，P2P房屋共享平台（如Airbnb）由专业房东和非专业个人房东组成。专业人士的角色是什么？房屋共享平台是否应该规范他们的参与？专业房东可能主要提供非专业房东不会提供的房产，吸引更多的客人--差异化效应。或者他们可能主要提供类似的房产，与非专业人士竞争。利用Airbnb房源的独特数据集，我们首先发现专业房东的房产比非专业房东的更贵，而且有更好的质量。其次，我们利用一个准实验，即Airbnb对美国几个城市的房东可以管理的房产数量设置上限，以确定专业房东的作用。在关于差异化效应和竞争效应下的不同预测下（关于政策的影响），我们发现证据表明后者占主导地位。尤其是政策影响使得来自非专业房东的供应得以增加，而非专业房东销售房产的价格水平在政策出台后普遍上升了。然而，我们对政策影响的异质性研究表明，竞争的主导地位在某些市场并不突出。最后，我们发现，政策出台后，平台在吸引房产预订或确保收入方面并没有变差。我们的研究结果在理论上和在实践上都有贡献，因为它们揭示了职业房东的作用以及P2P平台对他们的管理方法。

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专业卖家在P2P市场中的系列操作对非专业卖家造成了收益上的影响，而爱彼迎平台出台的“一房一主人”政策并没有起到预期的“保障非专业卖家”的效果，反而可能会影响自己平台的发展。作为第一篇研究专业人员在P2P市场中作用的文章，本文针对专业房东和散户的差异化市场和竞争这两方面进行展开，使用倾向得分匹配等统计手段探究了这两方面的重要性问题，用科学的角度颠覆了我们正常的认知，这对于P2P平台的政策改进十分关键，也为学者们探究其他P2P市场中的专业人士提供了很好的参考。