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在本文章中，我们对顶刊《Management Science》于2026年1月份在线发布的文章中进行了精选（共8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。本月MS发文聚焦风险管理策略、平台经济中的空间匹配与信息共享、新兴技术采纳及行为金融与组织动力学，研究网约车服务可及性的密度经济效应、按需平台供需错配的信息披露机制、生成式AI的快速普及特征、税收断点对投资者行为的影响等前沿问题。方法涵盖博弈论建模与反事实模拟、聚束分析法、符号约束向量自回归及大规模微观调查与实证分析等先进技术。

文章1

● 题目：On the Expansion of Risk Pooling

风险共担的拓展研究

● 作者：Michail Anthropelos , Runhuan Feng , Seongyoon Kim

● 发表时间：2026-1-2

● 原文链接:  https://doi.org/10.1287/mnsc.2024.06863

● 摘要：

Risk pooling has become an increasingly critical tool for managing risks among corporations, institutions, states, and nations, with examples including multinational pooling, decentralized insurance schemes, catastrophe risk pooling, and burden sharing for nuclear accidents. Although there has been a rich literature on such practices, little is known from a theoretical viewpoint regarding the operational strategies of risk pools and, in particular, on issues about the effect of a pool’s expansion on each existing member’s welfare. This paper is the first to explore these issues by establishing different notions of consensus for the expansion of a risk pool: strong consensus, where both existing members and new candidates improve their risk measures due to the pool’s expansion, and weak consensus, which refers to the willingness of existing members to remain in the pool. Under optimal risk sharing for each pool, we show that its properties regarding expansion’s effects depend strongly on the underlying pricing rule. For instance, only with risk-adjusted equilibrium pricing are existing members willing to accept highly risky new members, whereas simple linear pricing excludes such members from the pool. Additionally, the impact of exogenous reinsurance on consensus is analyzed under both pricing rules. The established framework offers valuable managerial insights and policy implications, which we illustrate through two indicative case studies based on real data.

风险共担已成为企业、机构、各州及各国开展风险管理的重要工具，典型应用包括跨国风险共担、去中心化保险方案、巨灾风险共担以及核事故损失分担等。现有相关研究成果颇丰，但从理论层面来看，关于风险共担机制的运营策略，尤其是共担机制拓展对现有各参与方福利的影响问题，相关研究仍较为匮乏。本文首次围绕上述问题展开探究，为风险共担的拓展设定了两类共识界定标准：强共识，即共担机制拓展后，现有参与方与新申请加入方的风险度量水平均得到改善；弱共识，即现有参与方愿意继续留在该共担机制中。在各类风险共担机制实现风险最优分担的前提下，研究发现其拓展效应的相关特征与底层定价规则高度相关。例如，唯有采用风险调整均衡定价，现有参与方才愿意接纳高风险新参与方，而简单线性定价则会将此类参与方排除在共担机制之外。此外，本文还分析了在上述两种定价规则下，外部分保对共识形成产生的影响。本文构建的研究框架能够为实践提供具有价值的管理启示与政策含义，并结合两项基于实际数据的典型案例研究对其进行了具体阐释。

文章2

● 题目：Spatial Distribution of Access to Service: Theory and Evidence from Ride-Sharing

服务可及性的空间分布：理论分析与网约车的实证证据

● 作者：Soheil Ghili , Vineet Kumar , Fei Teng

● 发表时间：2026-1-14

● 原文链接: https://doi.org/10.1287/mnsc.2021.02699

● 摘要：

We study access to ride-sharing across geographical regions using both theoretical and empirical analyses. We specifically model and examine the effects of economies of density in ride-sharing. Our model predicts that (i) economies of density skew access to ride-sharing away from less dense regions; (ii) the skew will be more pronounced for smaller platforms (i.e., “thinner markets”); and (iii) ride-sharing platforms do not find this skew efficient and thus, use price and wage levers to mitigate (but not eliminate) it. We show that these insights are robust to whether the source of economies of density is the supply side or the demand side. We then calibrate our model using ride-level Uber data from New York City. We use the model to simulate counterfactual scenarios, offering a quantitative evaluation of our theoretical results and informing platform strategy and policy.

本文采用理论分析与实证分析相结合的方法，研究不同地理区域的网约车服务可及性问题。研究重点对网约车的密度经济效应进行建模与检验。本文构建的模型预测：第一，密度经济会使网约车服务可及性向低密度区域倾斜的程度降低；第二，这一偏差在规模较小的平台（即 “稀薄市场”）中表现得更为显著；第三，网约车平台认为该偏差缺乏效率，因此会通过价格与报酬调控手段对其进行缓解，但无法彻底消除。研究证明，无论密度经济的成因源于供给端还是需求端，上述研究结论均具备稳健性。本文随后利用纽约市优步平台的订单级数据对模型进行校准，并通过模型模拟反事实场景，对理论研究结果开展量化验证，同时为平台运营策略与相关政策制定提供参考依据。

文章3

● 题目：Spatial Information Sharing on On-Demand Service Platforms

按需服务平台上的空间信息共享

● 作者：Swanand Kulkarni , Basak Kalkanci 

● 发表时间：2026-1-14

● 原文链接: https://doi.org/10.1287/mnsc.2021.03426

● 摘要：

We investigate how an on-demand service platform’s mechanism to share demand-supply mismatch information spatially affects drivers’ relocation decisions and the platform’s matching efficiency. We consider three mechanisms motivated by practice; the platform shares demand-supply mismatch information about either zones(s) with excess demand with all drivers (surge information sharing, common practice today), all zones with all drivers (full information sharing), or zone(s) with excess demand only with drivers sufficiently close by (local information sharing). We develop a game-theoretic model with three zones wherein drivers in two non-surge zones decide whether to relocate to the surge zone with excess demand. We incorporate two spatial aspects: drivers’ relocation costs and initial supply across different non-surge zones. Theoretically, full information sharing can hurt the platform’s matching efficiency compared with surge information sharing under low relocation costs because drivers in non-surge zones facing high demand locally do not chase the surge as much. Local information sharing is strictly dominated by other mechanisms in terms of matching efficiency when the supply of drivers near the surge zone is limited and weakly dominated otherwise by surge information sharing. We test these theory predictions in the laboratory with human participants as drivers in an environment where theoretical matching efficiency is highest with surge and lowest with local information sharing. Experimentally, the platform serves fewer customers than predicted with surge information sharing because drivers relocate too often, compromising efficiency in non-surge zones. In contrast, the platform serves more customers than predicted with full and local information sharing, and these mechanisms perform at least as well in matching efficiency as surge. Therefore, sharing demand-supply mismatch information either fully or in a targeted manner (as in local) can help to alleviate coordination problems on a platform. A behavioral equilibrium incorporating loss aversion through mental accounting and decision errors describes drivers’ behavior in our experiments better than the rational equilibrium.

我们研究了按需服务平台在空间上共享供需错配信息的机制如何影响司机的重新定位决策以及平台的匹配效率。我们考查了三种源于实践的机制；平台向所有司机共享超额需求区域的供需错配信息（溢价信息共享，即目前的通行做法），向所有司机共享所有区域的信息（完全信息共享），或仅向距离足够近的司机共享超额需求区域的信息（局部信息共享）。我们构建了一个包含三个区域的博弈论模型，其中位于两个非溢价区域的司机需决定是否重新定位至存在超额需求的溢价区域。我们纳入了两个空间因素：司机的重新定位成本以及不同非溢价区域的初始供给。

理论上，在低重新定位成本下，与溢价信息共享相比，完全信息共享可能会损害平台的匹配效率，因为在本地面临高需求的非溢价区域司机不会那么积极地追逐溢价区域。当溢价区域附近的司机供给有限时，局部信息共享在匹配效率方面严格劣于其他机制；而在其他情况下，则弱劣于溢价信息共享。我们在实验室中以人类参与者作为司机测试了这些理论预测，实验环境设定为理论匹配效率在溢价信息共享下最高，而在局部信息共享下最低。

实验结果显示，在溢价信息共享下，平台服务的客户数量少于预测值，因为司机重新定位过于频繁，损害了非溢价区域的效率。相比之下，在完全信息共享和局部信息共享下，平台服务的客户数量多于预测值，且这些机制在匹配效率方面的表现至少与溢价信息共享相当。因此，完全共享或以有针对性的方式（如局部共享）共享供需错配信息，有助于缓解平台上的协调问题。一个通过心理账户纳入损失厌恶以及决策误差的行为均衡，比理性均衡能更好地描述我们在实验中观察到的司机行为。

文章4

● 题目：The Rapid Adoption of Generative AI

生成式 AI 的快速普及

● 作者：Alexander Bick , Adam Blandin , David J. Deming 

● 发表时间：2026-1-20

● 原文链接: https://doi.org/10.1287/mnsc.2025.02523

● 摘要：

Generative artificial intelligence (genAI) is a potentially important new technology, but its impact on the economy depends on the speed and intensity of adoption. This paper reports results from a series of nationally representative U.S. surveys of genAI use at work and at home. As of late 2024, 45% of the U.S. population age 18–64 uses genAI. Among employed respondents, 27% used genAI for work at least once in the previous week: 10% used it every workday and 17% on some but not all workdays. Relative to each technology’s first mass-market product launch, work adoption of genAI has been faster than the personal computer (PC), and overall adoption has outpaced both PCs and the internet by an even wider margin. Between 1% and 7% of all work hours are currently assisted by genAI, and respondents report time savings equivalent to 1.4% of total work hours. Potential productivity gains vary widely by industry, and firm climate and policies play an important role in adoption patterns.

生成式人工智能 (genAI) 是一项具有潜力的重要新技术，但其对经济的影响取决于采用的速度和强度。本文报告了一系列关于 genAI 在工作和家庭中使用的、具有美国全国代表性的调查结果。截至 2024 年末，45% 的 18 至 64 岁美国人口在使用 genAI。在在职受访者中，27% 的人在过去一周内至少在工作中使用了以此 genAI：10% 的人每个工作日都使用，17% 的人在部分而非所有工作日使用。相较于各项技术的首款大众市场产品发布而言，genAI 在工作中的采用速度快于个人电脑 (PC)，而其总体采用速度则以更大的幅度超过了个人电脑和互联网。目前，约 1% 到 7% 的总工时由 genAI 辅助完成，受访者报告节省的时间相当于总工时的 1.4%。潜在的生产力提升因行业而异，且企业氛围和政策在采用模式中起着重要作用。

文章5

● 题目：How Do Taxes Affect the Trading Behavior of Private Investors? Evidence from Individual Portfolio Data

税收如何影响私人投资者的交易行为？来自个人投资组合数据的证据

● 作者：Florian Buhlmann, Philipp Doerrenberg , Benjamin Loos , Johannes Voget

● 发表时间：2026-1-22

● 原文链接: https://doi.org/10.1287/mnsc.2023.04213

● 摘要：

The removal of an intertemporal tax discontinuity in Germany provides us with a natural experiment to study the causal effect of taxes on individual stock-trading behavior and the disposition effect. Using individual investor transactions data combined with nonparametric regressions and bunching methods, we find that the presence of the tax discontinuity induces investors to adjust their holding periods, which reduces their effective tax rate by 11.3%. We also find that tax effects dominate the disposition effect in the days around the discontinuity and can inhibit it (by about 20%) during the six months preceding the discontinuity. We discuss the consequences of our results for the firms whose stocks are traded and policy implications.

德国消除跨期税收断点为我们提供了一个自然实验，用于研究税收对个人股票交易行为和处置效应的因果影响。利用个人投资者交易数据，结合非参数回归和聚束分析法，我们发现税收断点的存在促使投资者调整其持有期，从而将其有效税率降低了 11.3%。我们还发现，在断点前后的日子里，税收效应主导了处置效应，并且在断点前的六个月内可以抑制处置效应（约 20%）。最后，我们讨论了研究结果对股票被交易的公司所产生的后果以及政策启示。

文章6

● 题目：Do Financial Advisors Charge Sustainable Investors a Premium?

财务顾问是否会向可持续投资者收取溢价？

● 作者：Marten Laudi , Paul Smeets , Utz Weitzel 

● 发表时间：2026-1-23

● 原文链接: https://doi.org/10.1287/mnsc.2025.00232

● 摘要：

Despite growing regulatory concerns about potential overcharging of sustainable investors, empirical evidence is lacking. In two controlled laboratory-in-the-field experiments with 415 professional financial advisors from Europe and the United States and an incentivized survey, we identify two distinct but interacting effects. First, advisors charge sustainable investors a premium. This premium persists even when accounting for differences in skill, effort, and costs. Second, advisors impose higher fees on clients with low financial literacy. These factors interact. Sustainable investors with low financial literacy are charged the highest fee, whereas those with high financial literacy do not pay a sustainability premium. Our findings suggest that advisors extract additional fees for sustainable investment mandates but avoid overcharging sustainable investors with high financial literacy.

尽管监管层日益关注可能针对可持续投资者的过度收费问题，但目前仍缺乏实证证据。基于对来自欧洲和美国的 415 名专业财务顾问进行的两次受控实地实验室实验以及一项激励性调查，我们识别出了两种截然不同但相互影响的效应。首先，顾问会向可持续投资者收取溢价。即便在控制了技能、付出精力及成本差异后，这种溢价依然存在。其次，顾问会对金融素养较低的客户收取更高的费用。这两个因素存在交互作用。金融素养较低的可持续投资者被收取的费用最高，而具备高金融素养的可持续投资者则无需支付可持续性溢价。我们的研究结果表明，顾问会针对可持续投资委托收取额外费用，但会避免对具备高金融素养的可持续投资者过度收费。

文章7

● 题目：Identifying Demand Curves in Index Option Markets

识别指数期权市场中的需求曲线

● 作者：Kris Jacobs, Anh Thu Mai, Paola Pederzoli

● 发表时间：2026-1-27

● 原文链接: https://doi.org/10.1287/mnsc.2023.04263

● 摘要：

We identify latent demands in index option markets using a sign-restricted vector autoregression (VAR) and highlight the bias from treating (equilibrium) net demand as exogenous. Market-maker and end-user demand curves are far from infinitely elastic as assumed by some models, but elasticities exceed existing estimates for equities. Characterizing demand curves provides insights into the structure of index option markets. Deteriorating market conditions are associated with right shifts of the latent demand curves. The at-the-money (ATM) (out-of-the-money (OTM)) markets for calls and puts are mainly driven by end-user (market-maker) demand, and end-user (market-maker) ATM (OTM) call option demand predicts S&P500 returns.

我们利用符号约束向量自回归 (VAR) 识别了指数期权市场中的潜在需求，并强调了将 (均衡) 净需求视为外生变量所产生的偏差。做市商和最终用户的需求曲线远非如某些模型所假设的那样具有无限弹性，但其弹性仍超过了现有的针对股票的估计值。刻画需求曲线为理解指数期权市场的结构提供了见解。市场环境的恶化与潜在需求曲线的右移相关。看涨和看跌期权的平值 (ATM) (虚值 (OTM)) 市场主要由最终用户 (做市商) 的需求驱动，且最终用户 (做市商) 的平值 (虚值) 看涨期权需求可预测 S&P500 的回报。

文章8

● 题目：The Role of Success and Failure in Fluid Teams: Evidence from the Motion Picture Industry

成功与失败在流动团队中的作用：来自电影行业的证据

● 作者：Suresh Muthulingam , Kumar Rajaram 

● 发表时间：2026-1-27

● 原文链接: https://doi.org/10.1287/mnsc.2023.03756

● 摘要：

Many business settings involve fluid teams, where team members come together to work on a project, after which the team is disbanded. It is well-known that coordination can be challenging and affect the performance outcomes of fluid teams. The literature has studied how several facets of experience can facilitate learning and improve outcomes for fluid teams. However, the role of experience with success and failure and its effect on improving outcomes for fluid teams has remained unexplored. In this study, we use data from the motion picture industry to examine how the experience with success and failure resident within key members of a movie production team affects profitability. Our analysis of the data for 2,091 movies released in the United States between 1999 and 2018 reveals that a movie’s profitability depends on the production team’s history with success and failure. Additionally, we find that teams with a history of success result in movies with higher profits, whereas teams with a history of failure result in movies with lower profits. We also find that increased relative dispersion in the team’s experience does not affect the movie’s profitability. Further analysis of the composition of movie teams indicates that financial performance can be significantly impacted when movie teams are predominantly composed of members with a history of success or failure. We contribute by illustrating a new measure of team experience relevant for fluid teams and by providing insights on how to compose teams based on members’ experience with success and failure.

许多商业环境涉及流动团队，即团队成员为开展某项目而聚集，并在项目结束后解散。众所周知，协调工作可能具有挑战性，并会影响流动团队的绩效产出。现有文献已经研究了经验的多个方面如何促进流动团队的学习并改善其成果。然而，关于成功与失败的经验的作用及其对改善流动团队成果的影响，尚未得到探索。在本研究中，我们利用电影行业的数据，考察了电影制作团队核心成员所具备的成功与失败经验如何影响盈利能力。我们对 1999 年至 2018 年间在美国上映的 2,091 部电影的数据进行了分析，结果显示，一部电影的盈利能力取决于制作团队过往的成败经历。此外，我们发现，拥有成功历史的团队会带来更高利润的电影，而拥有失败历史的团队则会导致电影利润较低。我们还发现，团队经验中相对离散度的增加并不会影响电影的盈利能力。对电影团队构成的进一步分析表明，当电影团队主要由具有成功或失败历史的成员组成时，其财务绩效会受到显著影响。本文通过阐明一种适用于流动团队的新的团队经验度量指标，并就如何根据成员的成败经验来组建团队提供见解，从而做出了贡献。

文章9

● 题目：Dynamic Unraveling

动态瓦解

● 作者：Joel P. Flynn 

● 发表时间：2026-1-30

● 原文链接: https://doi.org/10.1287/mnsc.2022.02412

● 摘要：

This paper studies price and liquidity dynamics in the presence of costly short-selling when uninformed traders have limited willingness-to-pay to trade securities. In this setting, unraveling and Bayesian social learning interact to produce a novel mechanism, dynamic unraveling: Unraveling that generates signals that lead to future unraveling. Applying the theory, I show how dynamic unraveling explains low-volume crashes: falls in the prices of securities on low or declining trading volume. In this context, short-selling restrictions can make low-volume crashes more likely by intensifying dynamic unraveling, but liquidity injections have the opposite effect.

本文研究了在卖空有成本且非知情交易者对交易证券的支付意愿有限的情况下，价格和流动性的动态变化。在此设定下，瓦解与贝叶斯社会学习相互作用，产生了一种新机制，即动态瓦解：这是一种通过产生信号进而导致未来瓦解的瓦解过程。应用该理论，我展示了动态瓦解如何解释缩量崩盘：即在交易量低或下降的情况下证券价格的下跌。在此背景下，卖空限制会因加剧动态瓦解而增加缩量崩盘的可能性，而流动性注入则产生相反的效果。