推文作者：Evelyn Yao?清华大学本科在读
编者按
在本系列文章中，我们梳理了运筹学顶刊Management Science11月份发布的47篇文章的基本信息，旨在帮助读者快速洞察行业最新动态。本文为第二部分。
●?题目：Nonregular Employment and Payout Policy: Evidence from the Massachusetts Independent Contractor Law
非正常雇佣与支付政策：来自马萨诸塞州独立承包商法的证据
●?原文链接：https://doi.org/10.1287/mnsc.2022.00103
●?作者：JiHoon Hwang, Kathleen M. Kahle
●?发布时间：2023.11.8
●?摘要：
Compared with regular employees, independent contractors (ICs) offer labor flexibility and cost savings to their employers. Using a difference-in-differences design around the 2004 Massachusetts law that discourages IC usage, we find that this exogenous decrease in IC usage makes treated firms’ earnings more sensitive to changes in sales, increases labor-related expenses, and reduces profitability. Firms subsequently reduce share repurchases. The decrease is more pronounced for firms with high operating leverage and financial constraints. Our results are robust to entropy balancing. We conclude that IC usage affects firms’ operating leverage and profitability, which in turn, influence payout policy.
与正式员工相比，独立承包商（IC）为雇主提供了劳动灵活性并节约了成本。我们围绕 2004 年马萨诸塞州不鼓励使用独立承包商的法律，采用了差分法设计，发现独立承包商使用率的外生减少使受影响企业的盈利对销售额的变化更加敏感，增加了与劳动力相关的支出，降低了盈利能力。企业随之减少了股票回购。对于经营杠杆和财务约束较高的企业来说，这种下降更为明显。我们的结果对熵平衡是稳健的。我们的结论是，集成电路的使用会影响企业的经营杠杆和盈利能力，进而影响支付政策。
●?题目：CEO Activism and Firm Value
首席执行官激进主义与公司价值
●?原文链接：https://doi.org/10.1287/mnsc.2023.4971
●?作者：Anahit Mkrtchyan, Jason Sandvik, Vivi Z. Zhu
●?发布时间：2023.11.9
●?摘要：
We investigate the increasingly common practice of chief executive officers (CEOs) taking public stances on social and political issues (CEO activism). We find that CEO activism stems from a CEO’s personal ideology and its alignment with investor, employee, and customer ideologies. We show that CEO activism results in positive market reactions. Furthermore, firms with CEO activism realize increased shareholdings from investors with a greater liberal leaning, who rebalance their portfolios toward these firms. Our results suggest that investors’ socio-political preferences are an important channel through which CEO activism affects equity demand and stock prices. Notably, CEOs are less likely to be fired when their activist stances generate positive market responses.
我们对首席执行官（CEO）就社会和政治问题公开表态（CEO激进主义）这一日益普遍的做法进行了调查。我们发现，首席执行官激进主义源于首席执行官的个人意识形态及其与投资者、员工和客户意识形态的一致性。我们的研究表明，CEO激进主义会带来积极的市场反应。此外，首席执行官激进主义的公司会从更倾向于自由主义的投资者那里获得更多的股份，这些投资者会重新平衡他们对这些公司的投资组合。我们的研究结果表明，投资者的社会政治偏好是首席执行官激进主义影响股票需求和股票价格的一个重要渠道。值得注意的是，当首席执行官的激进主义立场引发积极的市场反应时，他们被解雇的可能性较小。
●?题目：Calibrating Sales Forecasts in a Pandemic Using Competitive Online Nonparametric Regression
利用竞争性在线非参数回归校准大流行病中的销售预测
●?原文链接：https://doi.org/10.1287/mnsc.2023.4969
●?作者：David Simchi-Levi, Rui Sun, Michelle Xiao Wu, Ruihao Zhu??
●?发布时间：2023.11.9
●?摘要：
Motivated by our collaboration with Anheuser-Busch InBev (AB InBev), a consumer packaged goods (CPG) company, we consider the problem of forecasting sales under the coronavirus disease 2019 (COVID-19) pandemic. Our approach combines nonparametric regression, game theory, and pandemic modeling to develop a data-driven competitive online nonparametric regression method. Specifically, the method takes the future COVID-19 case estimates, which can be simulated via the susceptible-infectious-removed (SIR) epidemic model as an input, and outputs the level of calibration for the baseline sales forecast generated by AB InBev. In generating the calibration level, we focus on an online learning setting where our algorithm sequentially predicts the label (i.e., the level of calibration) of a random covariate (i.e., the current number of active cases) given past observations and the generative process (i.e., the SIR epidemic model) of future covariates. To provide robust performance guarantee, we derive our algorithm by minimizing regret, which is the difference between the squared ?2-norm associated with labels generated by the algorithm and labels generated by an adversary and the squared ?2-norm associated with labels generated by the best isotonic (nondecreasing) function in hindsight and the adversarial labels. We develop a computationally efficient algorithm that attains the minimax-optimal regret over all possible choices of the labels (possibly non-i.i.d. and even adversarial). We demonstrate the performances of our algorithm on both synthetic and AB InBev’s data sets of three different markets (each corresponds to a country) from March 2020 to March 2021. The AB InBev’s numerical experiments show that our method is capable of reducing the forecast error in terms of weighted mean absolute percentage error (WMAPE) and mean squared error (MSE) by more than 37% for the company.
受与消费包装品（CPG）公司AB InBev合作的启发，我们考虑了在冠状病毒（COVID-19）大流行的情况下预测销售额的问题。我们的方法结合了非参数回归、博弈论和流行病建模，开发出一种数据驱动的竞争性在线非参数回归方法。具体来说，该方法将未来的 COVID-19 病例估计值（可通过易感-感染-清除（SIR）流行病模型模拟）作为输入，并输出AB InBev生成的基线销售预测的校准水平。在生成校准级别时，我们将重点放在在线学习设置上，即我们的算法根据过去的观察结果和未来协变量的生成过程（即 SIR 流行病模型），依次预测随机协变量（即当前活动病例数）的标签（即校准级别）。为了提供稳健的性能保证，我们通过最小化regret来推导算法，regret是算法生成的标签与对手生成的标签相关的平方 ?2-norm之差，也是后视最佳同调（非递减）函数生成的标签与对手标签相关的平方 ?2-norm之差。我们开发了一种计算效率高的算法，它能在所有可能的标签选择（可能是非 i.i.d.标签，甚至是对抗标签）中获得最小最优regret。我们在 2020 年 3 月至 2021 年 3 月三个不同市场（每个市场对应一个国家）的合成数据集和AB InBev数据集上演示了我们算法的性能。AB InBev的数值实验表明，我们的方法能够将该公司的加权平均绝对百分比误差 (WMAPE) 和平均平方误差 (MSE) 预测误差降低 37% 以上。
●?题目：Supply Chain Transparency and Blockchain Design
供应链透明度和区块链设计
●?原文链接：https://doi.org/10.1287/mnsc.2023.4851
●?作者：Yao Cui, Vishal Gaur, Jingchen Liu
●?发布时间：2023.11.9
●?摘要：
Companies that are investing in blockchain technology to enhance supply chain transparency face challenges in fostering collaborations with others and deciding what information to share. Transparency over the actions of supply chain partners can improve operational decisions, but sharing own data on the blockchain can put firms at a competitive disadvantage. In this paper, we investigate the resulting questions of when blockchain should be adopted in a supply chain and how it should be designed by analyzing two ways that it can enhance supply chain transparency: making the manufacturer’s sourcing cost transparent to the buyers (i.e., vertical cost transparency) and making the ordering status of buyers transparent to each other (i.e., horizontal order transparency). Given such transparency, firms can design a smart contract that automates transactions contingent on the revealed information and enables them to realize better equilibrium outcomes. We find that blockchain increases supply chain profit only when the manufacturer’s capacity is large and decreases supply chain profit otherwise. If the capacity is sufficiently large to eliminate the buyers’ competition, blockchain leads to a win–win–win and the incentives of all participants are naturally aligned. If the capacity is only moderately large, the manufacturer needs to compensate the buyers to facilitate a blockchain implementation. However, if the capacity is small, horizontal order transparency enabled by the blockchain mitigates the buyers’ overorder incentive to compete for the manufacturer’s capacity and increases double marginalization. For such cases, we show that a blockchain that only enables vertical cost transparency should (and can) still be adopted in a range of small capacity cases, and we propose an access control layer for the logistics data to implement such a blockchain.
投资区块链技术以提高供应链透明度的公司在促进与其他公司的合作以及决定共享哪些信息方面面临挑战。供应链合作伙伴行动的透明度可以改善运营决策，但在区块链上共享自己的数据可能会使企业在竞争中处于不利地位。在本文中，我们通过分析区块链提高供应链透明度的两种方式：使制造商的采购成本对买方透明（即纵向成本透明）和使买方的订购状态对彼此透明（即横向订单透明），来探讨供应链中何时应该采用区块链以及如何设计区块链等问题。鉴于这种透明度，企业可以设计一种智能合约，根据所披露的信息自动进行交易，从而实现更好的均衡结果。我们发现，只有当制造商的产能很大时，区块链才会增加供应链利润，反之则会减少供应链利润。如果产能大到足以消除买方竞争，区块链就会带来三赢，所有参与者的激励也会自然一致。如果产能只是中等规模，制造商需要对买家进行补偿，以促进区块链的实施。但是，如果产能较小，区块链带来的横向订单透明度会减轻买方争夺制造商产能的超额订单动机，并增加双重边缘化。对于这种情况，我们表明，在一系列小产能情况下，只实现纵向成本透明的区块链仍然应该（并且可以）被采用，我们还提出了一个物流数据访问控制层，以实现这种区块链。
●?题目：Let’s Chat… When Communication Promotes Efficiency in Experimental Asset Markets
让我们聊聊......当交流促进实验资产市场的效率时
●?原文链接：https://doi.org/10.1287/mnsc.2023.4967
●?作者：Brice Corgnet, Mark DeSantis, David Porter?
●?发布时间：2023.11.10
●?摘要：
The growing prevalence of stock market chat rooms and social media suggests that communication between traders may affect market outcomes. Using data from a series of laboratory experiments, we study the causal effect of trader communication on market efficiency. We show that communication allows markets to convey private information more effectively. This effect is robust to a wide range of information settings. The presence of insiders limits the impact, whereas posted reputation scores in the communication platform magnify it. These findings illustrate the need to consider social interactions when designing market institutions to leverage the social motives that foster information aggregation.
股市聊天室和社交媒体的日益盛行表明，交易者之间的交流可能会影响市场结果。利用一系列实验室实验的数据，我们研究了交易者交流对市场效率的因果效应。我们发现，沟通能让市场更有效地传递私人信息。这种效应在各种信息环境下都是稳健的。内部人的存在限制了这种影响，而在交流平台上发布的声誉分数则放大了这种影响。这些发现说明，在设计市场机制时需要考虑社会互动，以利用促进信息聚合的社会动机。
●?题目：Organized Crime and Firms: Evidence from Antimafia Enforcement Actions
有组织犯罪与公司：来自反黑手党执法行动的证据
●?原文链接：https://doi.org/10.1287/mnsc.2021.00859
●?作者：Pablo Slutzky, Stefan Zeume
●?发布时间：2023.11.10
●?摘要：
We exploit staggered municipality-level antimafia enforcement actions in Italy over the 1995–2015 period to study how the presence of organized crime affects firms. Following enforcement actions, we find increases in competition (i) among firms and (ii) for public procurement contracts. Firms that do not exit after a weakening of organized crime shrink in size, more so when operating in the nontradable sector. Our findings are consistent with organized crime acting as a barrier to entry and affecting local economic activity.
我们利用 1995-2015 年间意大利市级交错开展的反黑手党执法行动，研究有组织犯罪的存在如何影响企业。在执法行动之后，我们发现（i）企业之间以及（ii）公共采购合同的竞争加剧。在有组织犯罪减弱后没有退出的企业规模会缩小，在非贸易部门经营的企业规模会更大。我们的研究结果表明，有组织犯罪阻碍了企业进入市场，并影响了当地的经济活动。
●?题目：Physical Frictions and Digital Banking Adoption
物理摩擦与数字银行的采用
●?原文链接：https://doi.org/10.1287/mnsc.2023.4972
●?作者：Hyun-Soo Choi, Roger K. Loh
●?发布时间：2023.11.14
●?摘要：
The behavioral literature suggests that minor frictions can elicit desirable behavior without obvious coercion. Using closures of ATMs in a densely populated city as an instrument for small frictions to physical banking access, we find that customers affected by ATM closures increase their usage of the bank’s digital platform. Other spillover effects of this adoption of financial technology include increases in point-of-sale transactions, electronic funds transfers, automatic bill payments and savings, and a reduction in cash usage. Our results show that minor frictions can help overcome the status quo bias and facilitate significant behavior change.
行为学文献表明，微小的摩擦可以在没有明显胁迫的情况下引发理想的行为。在一个人口稠密的城市，我们用关闭自动取款机作为实体银行服务小摩擦的工具，发现受自动取款机关闭影响的客户增加了对银行数字平台的使用。采用金融技术的其他溢出效应包括销售点交易、电子转账、自动账单支付和储蓄的增加，以及现金使用量的减少。我们的研究结果表明，微小的摩擦有助于克服现状偏差，促进显著的行为改变。
●?题目：Building Socially Intelligent AI Systems: Evidence from the Trust Game Using Artificial Agents with Deep Learning
构建社会智能人工智能系统：使用深度学习人工智能代理的信任游戏证据
●?原文链接：https://doi.org/10.1287/mnsc.2023.4782
●?作者：Jason Xianghua Wu, Yan (Diana) Wu, Kay-Yut Chen, Lei Hua
●?发布时间：2023.11.14
●?摘要：
The trust game, a simple two-player economic exchange, is extensively used as an experimental measure for trust and trustworthiness of individuals. We construct deep neural network–based artificial intelligence (AI) agents to participate a series of experiments based upon the trust game. These artificial agents are trained by playing with one another repeatedly without any prior knowledge, assumption, or data regarding human behaviors. We find that, under certain conditions, AI agents produce actions that are qualitatively similar to decisions of human subjects reported in the trust game literature. Factors that influence the emergence and levels of cooperation by artificial agents in the game are further explored. This study offers evidence that AI agents can develop trusting and cooperative behaviors purely from an interactive trial-and-error learning process. It constitutes a first step to build multiagent-based decision support systems in which interacting artificial agents are capable of leveraging social intelligence to achieve better outcomes collectively.
信任博弈是一种简单的双人经济交换游戏，被广泛用作衡量个人信任和可信度的实验指标。我们构建了基于深度神经网络的人工智能（AI）代理，以参与一系列基于信任博弈的实验。这些人工智能代理是在没有任何关于人类行为的先验知识、假设或数据的情况下，通过反复相互博弈训练出来的。我们发现，在某些条件下，人工智能代理做出的行为与信任博弈文献中报道的人类主体的决定在性质上非常相似。我们还进一步探讨了影响游戏中人工代理合作的出现和水平的因素。这项研究证明，人工智能代理可以纯粹通过互动试错学习过程来发展信任与合作行为。它为建立基于多代理的决策支持系统迈出了第一步，在该系统中，交互式人工代理能够利用社会智能，共同取得更好的结果。
●?题目：Reciprocal Human-Machine Learning: A Theory and an Instantiation for the Case of Message Classification
人机互惠学习：信息分类理论与实例
●?原文链接：https://doi.org/10.1287/mnsc.2022.03518
●?作者：Dov Te’eni, Inbal Yahav, Alexely Zagalsky, David Schwartz, Gahl Silverman, Daniel Cohen, Yossi Mann, Dafna Lewinsky
●?发布时间：2023.11.14
●?摘要：
There is growing agreement among researchers and developers that in certain machine-learning (ML) tasks, it may be advantageous to keep a “human in the loop” rather than rely on fully autonomous systems. Continual human involvement can mitigate machine bias and performance deterioration while enabling humans to continue learning from insights derived by ML. Yet a microlevel theory that effectively facilitates joint and continual learning in both humans and machines is still lacking. To address this need, we adopt a design science approach and build on theories of human reciprocal learning to develop an abstract configuration for reciprocal human-ML (RHML) in the context of text message classification. This configuration supports learning cycles between humans and machines who repeatedly exchange feedback regarding a classification task and adjust their knowledge representations accordingly. Our configuration is instantiated in Fusion, a novel technology artifact. Fusion is developed iteratively in two case studies of cybersecurity forums (drug trafficking and hacker attacks), in which domain experts and ML models jointly learn to classify textual messages. In the final stage, we conducted two experiments of the RHML configuration to gauge both human and machine learning processes over eight learning cycles. Generalizing our insights, we provide formal design principles for the development of systems to support RHML.
越来越多的研究人员和开发人员一致认为，在某些机器学习（ML）任务中，让 "人参与其中 "而不是依赖完全自主的系统可能更有优势。人类的持续参与可以减轻机器的偏差和性能下降，同时使人类能够继续从 ML 得出的见解中学习。然而，目前仍缺乏有效促进人类和机器共同持续学习的微观理论。为了满足这一需求，我们采用了一种设计科学方法，并以人类互惠学习理论为基础，在文本信息分类的背景下开发了一种抽象的人类-ML（RHML）互惠配置。该配置支持人类和机器之间的学习循环，人类和机器就分类任务反复交换反馈，并相应地调整他们的知识表征。我们的配置在 Fusion 中实例化，Fusion 是一种新颖的技术工具。Fusion 是在两个网络安全论坛（贩毒和黑客攻击）的案例研究中反复开发的，在这两个案例研究中，领域专家和 ML 模型共同学习对文本信息进行分类。在最后阶段，我们对 RHML 配置进行了两次实验，以评估八个学习周期中人类和机器的学习过程。根据我们的见解，我们为开发支持 RHML 的系统提供了正式的设计原则。
●?题目：An Experiment on Gender Representation in Majoritarian Bargaining
多数派谈判中的性别代表性实验
●?原文链接：https://doi.org/10.1287/mnsc.2022.01800
●?作者：Andrzej Baranski, Diogo Geraldes, Ada Kovaliukaite, James Tremewan
●?发布时间：2023.11.14
●?摘要：
Women are underrepresented in business, academic, and political decision-making bodies across the world. To investigate the causal effect of gender representation on multilateral negotiations, we experimentally manipulate the composition of triads in a majoritarian, divide-the-dollar game. We document a robust gender gap in earnings driven largely by the exclusion of women from alliances rather than differential shares within alliances. Experiments with different subject pools show that distinct bargaining dynamics can underlie the same inequitable outcomes; gender-biased outcomes can be caused by outright discrimination, but they can also be driven by more complex dynamics related to differences in bargaining strategies. Although replacing the male with a female majority all but eliminates the gap in one pool, it has minimal effect in the other. These findings show that there is no “one-size-fits-all” solution to the gender gap we uncovered and highlight the importance of studying bargaining dynamics in detail.
在世界各地的商业、学术和政治决策机构中，女性的代表性都不足。为了研究性别代表性对多边谈判的因果影响，我们在一个多数决、分美元的游戏中实验性地操纵了三方的组成。我们记录了收入方面的巨大性别差距，其主要原因是女性被排除在联盟之外，而不是联盟内部的不同份额。使用不同主体库进行的实验表明，不同的讨价还价动态可以导致相同的不公平结果；性别偏见结果可能是由直接歧视造成的，但也可能是由与讨价还价策略差异相关的更复杂动态驱动的。虽然以女性多数取代男性多数几乎消除了一个群体中的差距，但对另一个群体的影响却微乎其微。这些发现表明，对于我们发现的性别差距，没有 "放之四海而皆准 "的解决方案，并强调了详细研究讨价还价动态的重要性。
●?题目：Analyst Coverage Networks and Corporate Financial Policies
分析师覆盖网络和公司财务政策
●?原文链接：https://doi.org/10.1287/mnsc.2023.4891
●?作者：Armando Gomes, Radhakrishnan Gopalan, Mark T. Leary, Francisco Marcet
●?发布时间：2023.11.15
●?摘要：
We use the setting of analyst coverage networks to shed light on the nature of peer effects in financial policies. First, we use the “friends-of-friends” approach and exploit the fact that analyst coverage networks partially overlap to identify endogenous peer effects, in which firms respond directly to the capital structure choices of their peers, separately from contextual effects, in which they respond to their peers’ characteristics. We further show evidence that analysts facilitate these peer effects through their role as informational intermediaries. Analyst network peer effects are distinct from industry peer effects and are more pronounced among peers connected by analysts that are more experienced and from more influential brokerage houses. Finally, the analyst peer effects become weaker after exogenous reductions in common coverage as a consequence of brokerage closures.
我们利用分析师覆盖网络来揭示金融政策中同行效应的本质。首先，我们使用 "朋友的朋友 "方法，并利用分析师覆盖网络部分重叠这一事实来识别内生的同行效应，即公司直接对同行的资本结构选择做出反应，而非背景效应，即公司对同行的特征做出反应。我们进一步证明，分析师通过其信息中介的角色促进了这些同行效应。分析师网络同行效应有别于行业同行效应，而且在由经验更丰富、来自更有影响力的经纪公司的分析师连接的同行中更为明显。最后，由于券商倒闭导致共同覆盖面外生性减少，分析师同行效应会变得更弱。
●?题目：Exposure to the Views of Opposing Others with Latent Cognitive Differences Results in Social Influence—But Only When Those Differences Remain Obscured
接触具有潜在认知差异的对立者的观点会产生社会影响--但只有当这些差异被掩盖时才会如此
●?原文链接：https://doi.org/10.1287/mnsc.2022.00895
●?作者：Douglas Guilbeault, Austin van Loon, Katharina Lix, Amir Goldberg, Sameer B. Srivastava?
●?发布时间：2023.11.15
●?摘要：
Cognitive differences can catalyze social learning through the process of one-to-one social influence. Yet the learning benefits of exposure to the ideas of cognitively dissimilar others often fail to materialize. Why do cognitive differences produce learning from interpersonal influence in some contexts but not in others? To answer this question, we distinguish between cognition that is expressed—one’s public stance on an issue and the way in which supporting arguments are framed—and cognition that is latent—the semantic associations that underpin these expressions. We theorize that, when latent cognition is obscured, one is more likely to be influenced to change one’s mind on an issue when exposed to the opposing ideas of cognitively dissimilar, rather than similar, others. When latent cognition is instead observable, a subtle similarity-attraction response tends to counteract the potency of cognitive differences—even when social identity cues and other categorical distinctions are inaccessible. To evaluate these ideas, we introduce a novel experimental paradigm in which participants (a) respond to a polarizing scenario; (b) view an opposing argument by another whose latent cognition is either similar to or different from their own and is either observable or obscured; and (c) have an opportunity to respond again to the scenario. A preregistered study (n = 1,000) finds support for our theory. A supplemental study (n = 200) suggests that the social influence of latent cognitive differences operates through the mechanism of argument novelty. We discuss implications of these findings for research on social influence, collective intelligence, and cognitive diversity in groups.
认知差异可以通过一对一的社会影响过程促进社会学习。然而，接触认知不同的他人的想法所带来的学习益处往往无法实现。为什么认知差异会在某些情况下产生人际影响学习，而在另一些情况下却不会呢？为了回答这个问题，我们区分了两种认知--一种是表达出来的认知--即对某一问题的公开立场和支持论点的框架方式；另一种是潜在的认知--即这些表达方式背后的语义关联。我们的理论是，当潜在认知被掩盖时，当一个人接触到认知上不同而非相似的人的相反观点时，他更有可能受到影响而改变对某一问题的看法。而当潜在认知可以被观察到时，一种微妙的相似性吸引反应往往会抵消认知差异的影响力--即使社会身份线索和其他分类区别是不可获取的。为了评估这些观点，我们引入了一个新颖的实验范式，在这个范式中，参与者（a）对一个两极分化的情景做出反应；（b）观看另一个人的对立论点，而这个人的潜在认知要么与自己的相似，要么与自己的不同，要么是可观察到的，要么是被掩盖的；（c）有机会再次对情景做出反应。一项预先登记的研究（n = 1,000）支持我们的理论。一项补充研究（n = 200）表明，潜在认知差异的社会影响是通过论据的新颖性机制产生的。我们将讨论这些发现对社会影响、集体智慧和群体认知多样性研究的意义。
●?题目：A Heuristic for Combining Correlated Experts When There Are Few Data
数据较少时组合相关专家的启发式方法
●?原文链接：https://doi.org/10.1287/mnsc.2021.02009
●?作者：David Soule, Yael Grushka-Cockayne, Jason Merrick
●?发布时间：2023.11.15
●?摘要：
It is intuitive and theoretically sound to combine experts’ forecasts based on their proven skills, while accounting for correlation among their forecast submissions. Simpler combination methods, however, which assume independence of forecasts or equal skill, have been found to be empirically robust, in particular, in settings in which there are few historical data available for assessing experts’ skill. One explanation for the robust performance by simple methods is that empirical estimation of skill and of correlations introduces error, leading to worse aggregated forecasts than simpler alternatives. We offer a heuristic that accounts for skill and reduces estimation error by utilizing a common correlation factor. Our theoretical results present an optimal form for this common correlation, and we offer Bayesian estimators that can be used in practice. The common correlation heuristic is shown to outperform alternative combination methods on macroeconomic and experimental forecasting where there are limited historical data.
根据专家已证实的技能将他们的预测结合起来，同时考虑到他们提交的预测之间的相关性，这种方法既直观又有理论依据。然而，假定预测独立或技能相等的简单组合方法在经验上是稳健的，特别是在可用于评估专家技能的历史数据很少的情况下。简单方法表现稳健的一个原因是，对技能和相关性的经验估计会带来误差，导致综合预测比简单方法更差。我们提供了一种启发式方法，通过利用共同相关因子来考虑技能并减少估计误差。我们的理论结果提出了这种共同相关性的最优形式，并提供了可用于实践的贝叶斯估计方法。在历史数据有限的宏观经济和实验预测中，共同相关启发式的效果优于其他组合方法。
●?题目：Behavioral Microfoundations of New Practice Adoption: The Effects of Rewards, Training and Population Dynamics
采用新做法的行为微观基础：奖励、训练和人口动态的影响
●?原文链接：https://doi.org/10.1287/mnsc.2022.00305
●?作者：Antoine Feylessoufi, Stylianos Kavadias, Daniel Ralph?
●?发布时间：2023.11.16
●?摘要：
Organizations face challenges when trying to effectively introduce new operational practices that substitute for existing ones. We study how the dynamics due to social comparisons between employees give rise to individual strategic considerations and eventually shape the organizational adoption outcome. We develop an evolutionary game theory model that accounts for these microlevel individual adoption decisions and their impact on macrolevel population adoption equilibria. Social comparisons invoke dynamics that expand the possible outcomes beyond the traditional nonadoption versus full-adoption dichotomy. Specifically, ahead-seeking social comparisons drive the long-term coexistence of practices because employees seek to differentiate their choices from those of others. Meanwhile, behind-averse comparisons create a bandwagon effect that determines adoption depending on the initial fraction of adopters—that is, employees who are trained upfront. These dynamics are robust to various settings: different conceptualizations of social comparisons, each employee responding to more than one kind of social comparison, and nonhomogeneous social comparisons across employees. Moreover, they are material to organizations that seek to maximize their profit when introducing a new practice, by setting the levels of upfront training and adoption rewards. Our results call for senior managers to account for such behavioral traits when managing the introduction of new practices. Profitable adoption critically relies upon matching rewards and training to the type of social comparison.
组织在试图有效引入新的运营实践来替代现有实践时面临着挑战。我们研究了员工之间的社会比较如何导致个人战略考虑的动态变化，并最终影响组织的采用结果。我们建立了一个进化博弈论模型，以解释这些微观层面的个人采纳决策及其对宏观层面的群体采纳均衡的影响。社会比较所引发的动态变化使可能的结果超出了传统的不采用与完全采用的二分法。具体来说，追求领先的社会比较会推动各种实践的长期共存，因为员工会寻求将自己的选择与他人的选择区分开来。与此同时，后退型社会比较则会产生一种 "浪潮效应"（bandwagon effect），这种效应会根据初始采用者（即接受过前期培训的员工）的比例来决定采用与否。这些动态变化在不同的环境下都是稳健的：不同的社会比较概念、每个员工对不止一种社会比较的反应以及不同员工之间非同质的社会比较。此外，对于那些通过设定前期培训和采用奖励的水平来寻求在引入新实践时实现利润最大化的组织来说，它们也是有价值的。我们的研究结果要求高级管理人员在管理新实践的引入时考虑到这些行为特征。采用新方法能否带来利润，关键在于奖励和培训是否与社会比较类型相匹配。
●?题目：When Beliefs Influence the Perceived Signal Precision: The Impact of News on Reinforcement-Oriented Agents
当信念影响感知信号精度时：新闻对强化导向型代理的影响
●?原文链接：https://doi.org/10.1287/mnsc.2023.4941
●?作者：Stefanie Schraeder
●?发布时间：2023.11.21
●?摘要：
In a world of increasingly extensive information, rational investors can make better decisions. However, reinforcement-oriented investors are also more likely to observe preferred signals close to their own perception. A focus on these signals distorts the perceived aggregate signal in the direction of the prior estimate. This reduces belief adaptation. Hence, the empirically well-documented selective exposure/reinforcement theory reduces the positive impact of greater information availability on price efficiency. Additional information can sometimes even decrease perception correctness. In a market with biased investors, managers have an incentive to announce more diffuse (fewer precise) signals in case of negative (positive) information. This results in postearnings-announcement drift and dispersion anomaly. Also, the distribution shape matters for information processing. For unimodal, symmetric distributions, agents’ perceptions converge to the fundamental—even though at a reduced speed. For multimodal signal distributions, the estimate can diverge from the fundamental.
在信息日益广泛的世界里，理性的投资者可以做出更好的决策。然而，以强化为导向的投资者也更有可能观察到与其自身感知相近的首选信号。对这些信号的关注会扭曲所感知的总体信号，使其向先验估计的方向发展。这就降低了信念适应性。因此，经验充分证明的选择性暴露/强化理论降低了更多信息可用性对价格效率的积极影响。额外信息有时甚至会降低认知的正确性。在一个投资者有偏见的市场中，管理者有动力在出现负面（正面）信息时公布更多分散（更少精确）的信号。这就导致了收益公布后的漂移和分散异常。此外，分布形状对信息处理也很重要。对于单模态的对称分布，代理人的感知会向基本面靠拢——尽管速度较慢。对于多模态信号分布，估计值可能会偏离基本面。