Raphael Epperson, Johannes Diederich, Timo Goeschl
Management Science, Ahead of Print.
管理科学》，印刷版前。
{"title":"How to Design the Ask? Funding Units vs. Giving Money","authors":"Raphael Epperson, Johannes Diederich, Timo Goeschl","doi":"10.1287/mnsc.2021.00157","DOIUrl":"https://doi.org/10.1287/mnsc.2021.00157","url":null,"abstract":"Management Science, Ahead of Print. <br/>","PeriodicalId":49890,"journal":{"name":"Management Science","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Inferring the Unofficial Incomes of Officials from Home Ownership","authors":"Yongheng Deng, Shang-Jin Wei","doi":"10.1287/mnsc.2021.01725","DOIUrl":"https://doi.org/10.1287/mnsc.2021.01725","url":null,"abstract":"Management Science, Ahead of Print. <br/>","PeriodicalId":49890,"journal":{"name":"Management Science","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528258","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Viral V. Acharya, Timothy Johnson, Suresh Sundaresan, Steven Zheng
Management Science, Ahead of Print.
管理科学》，印刷版前。
{"title":"Vaccine Progress, Stock Prices, and the Value of Ending the Pandemic","authors":"Viral V. Acharya, Timothy Johnson, Suresh Sundaresan, Steven Zheng","doi":"10.1287/mnsc.2023.00863","DOIUrl":"https://doi.org/10.1287/mnsc.2023.00863","url":null,"abstract":"Management Science, Ahead of Print. <br/>","PeriodicalId":49890,"journal":{"name":"Management Science","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528260","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Human-Robot Interactions in Investment Decisions","authors":"Milo Bianchi, Marie Brière","doi":"10.1287/mnsc.2022.03886","DOIUrl":"https://doi.org/10.1287/mnsc.2022.03886","url":null,"abstract":"Management Science, Ahead of Print. <br/>","PeriodicalId":49890,"journal":{"name":"Management Science","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141528261","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Legal scholars highlight the tensions that exist between different classes of shareholders in startups. We model a startup owned by undiversified investors with heterogeneous capital contributions and risk preferences. A social planner runs the firm on behalf of all investors. We compare investors’ expected utility with a hypothetical first-best decentralized benchmark. The startup’s optimal investment policy is procyclical and a time-varying weighted average of shareholders’ optimal investment policies. The optimal contracts issued to investors are tailor-made, interdependent, and include equity claims resembling preferred stock with heterogeneous payout caps, leading to a complex capitalization table as more investors join the startup. This paper was accepted by Will Cong, finance. Funding: This work was supported by the Cambridge Endowment for Research in Finance and Keynes Fellowship. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01724 .
法律学者强调了初创企业中不同类别股东之间存在的紧张关系。我们建立了一个初创企业的模型，该企业由具有不同出资额和风险偏好的非分散投资者所拥有。一名社会规划者代表所有投资者经营这家公司。我们将投资者的预期效用与假设的第一最优分散基准进行比较。初创公司的最优投资政策是顺周期的，是股东最优投资政策的时变加权平均值。发放给投资者的最优合约是量身定制的、相互依存的，其中包括类似优先股的股权债权，并有不同的赔付上限，随着越来越多的投资者加入初创企业，会产生复杂的资本化表。本文已被金融学专业的 Will Cong 接受。资助：本文由剑桥金融研究基金和凯恩斯奖学金资助。补充材料：在线附录和数据文件可在 https://doi.org/10.1287/mnsc.2022.01724 上获取。
{"title":"Resolving Tensions Between Heterogeneous Investors in a Startup","authors":"Shiqi Chen, Bart M. Lambrecht","doi":"10.1287/mnsc.2022.01724","DOIUrl":"https://doi.org/10.1287/mnsc.2022.01724","url":null,"abstract":"Legal scholars highlight the tensions that exist between different classes of shareholders in startups. We model a startup owned by undiversified investors with heterogeneous capital contributions and risk preferences. A social planner runs the firm on behalf of all investors. We compare investors’ expected utility with a hypothetical first-best decentralized benchmark. The startup’s optimal investment policy is procyclical and a time-varying weighted average of shareholders’ optimal investment policies. The optimal contracts issued to investors are tailor-made, interdependent, and include equity claims resembling preferred stock with heterogeneous payout caps, leading to a complex capitalization table as more investors join the startup. This paper was accepted by Will Cong, finance. Funding: This work was supported by the Cambridge Endowment for Research in Finance and Keynes Fellowship. Supplemental Material: The online appendices and data files are available at https://doi.org/10.1287/mnsc.2022.01724 .","PeriodicalId":49890,"journal":{"name":"Management Science","volume":null,"pages":null},"PeriodicalIF":5.4,"publicationDate":"2024-06-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141347494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Louis L. Chen, Chee Chin Lim, Divya Padmanabhan, Karthik Natarajan
In this paper, we pursue a correlation-robust study of the influence maximization problem. Departing from the classic independent cascade model, we study a diffusion process adversarially adapted to the choice of seed set. More precisely, rather than the independent coupling of known individual edge probabilities, we now evaluate a seed set’s expected influence under all possible correlations, specifically, the one that presents the worst case. We find that the worst case expected influence can be efficiently computed, its NP-hard optimization done approximately [Formula: see text] with greedy construction, and we provide complete, efficient characterizations of the adversarial coupling, the random graph, and the random number of influenced nodes. But, most importantly, upon mixing the independent cascade with the worst case, we attain a tunable and more comprehensive model better suited for real-world diffusion phenomena than the independent cascade alone and without increased computational complexity. Extensions to the correlation-robust study of risk follow along with numerical experiments on network data sets with demonstration of how our models can be tuned. This paper was accepted by George Shanthikumar, data science. Funding: This work was supported by the Air Force Office of Scientific Research (Mathematical Optimization Program) under the grant: “Optimal Decision Making under Tight Performance Requirements in Adversarial and Uncertain Environments: Insight from Rockafellian Functions”. Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mnsc.2021.03445 .
在本文中，我们对影响最大化问题进行了相关性稳健研究。与经典的独立级联模型不同，我们研究的是与种子集选择相适应的对抗性扩散过程。更确切地说，我们现在评估的不是已知单个边缘概率的独立耦合，而是种子集在所有可能相关性下的预期影响力，特别是最坏情况下的预期影响力。我们发现，最坏情况下的预期影响可以高效计算，其 NP-hard（近似）优化可以通过贪婪构造完成[公式：见正文]，我们还提供了对抗耦合、随机图和受影响节点的随机数量的完整、高效表征。但最重要的是，在将独立级联与最坏情况混合后，我们获得了一个可调整的、更全面的模型，比单独的独立级联更适合现实世界中的扩散现象，而且不增加计算复杂度。接下来，我们将对风险相关性研究进行扩展，并在网络数据集上进行数值实验，展示如何调整我们的模型。本文由数据科学部的 George Shanthikumar 接收。资助：这项工作得到了空军科学研究办公室（数学优化计划）的资助："对抗性和不确定性环境下严格性能要求下的最优决策：Rockafellian 函数的启示"。补充材料：在线附录和数据文件可在 https://doi.org/10.1287/mnsc.2021.03445 上获取。
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