{"title":"Establishing Convergence of Infinite-Server Queues with Batch Arrivals to Shot-Noise Processes","authors":"Andrew Daw, Brian Fralix, Jamol Pender","doi":"10.1287/opre.2023.0353","DOIUrl":"https://doi.org/10.1287/opre.2023.0353","url":null,"abstract":"Operations Research, Ahead of Print. <br/>","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Optimal Regularized Online Allocation by Adaptive Re-Solving","authors":"Wanteng Ma, Ying Cao, Danny H. K. Tsang, Dong Xia","doi":"10.1287/opre.2022.0486","DOIUrl":"https://doi.org/10.1287/opre.2022.0486","url":null,"abstract":"Operations Research, Ahead of Print. <br/>","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Learning to Persuade on the Fly: Robustness Against Ignorance","authors":"You Zu, Krishnamurthy Iyer, Haifeng Xu","doi":"10.1287/opre.2021.0529","DOIUrl":"https://doi.org/10.1287/opre.2021.0529","url":null,"abstract":"Operations Research, Ahead of Print. <br/>","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141503002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Mete Şeref Ahunbay, Martin Bichler, Johannes Knörr
Operations Research, Ahead of Print.
运筹学》，印刷版前。
{"title":"Pricing Optimal Outcomes in Coupled and Non-convex Markets: Theory and Applications to Electricity Markets","authors":"Mete Şeref Ahunbay, Martin Bichler, Johannes Knörr","doi":"10.1287/opre.2023.0401","DOIUrl":"https://doi.org/10.1287/opre.2023.0401","url":null,"abstract":"Operations Research, Ahead of Print. <br/>","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141502950","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Boundary Effects in the Diffusion of New Products on Cartesian Networks","authors":"Gadi Fibich, Tomer Levin, Kenneth T. Gillingham","doi":"10.1287/opre.2022.0004","DOIUrl":"https://doi.org/10.1287/opre.2022.0004","url":null,"abstract":"Operations Research, Ahead of Print. <br/>","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141522579","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Prediction markets are renowned for their accuracy in forecasting. However, it is not fully clear how the predication market aggregates the traders’ beliefs. In “Price Interpretability of Prediction Markets: A Convergence Analysis,” Gao, Wang, Wu, and Yu introduce a novel multivariate utility (MU)-based mechanism that consolidates various existing automated market-making schemes. This mechanism establishes convergence results for markets consisting of risk-averse traders with diverse beliefs who interact repeatedly with the market maker. Furthermore, the study delivers analytical and numerical insights into the limiting price across different market models. Building on these results, the authors offer an efficient approximation scheme for the limiting price, shedding light on how traders’ beliefs shape market prices. These discoveries provide valuable guidance to market designers, enabling them to refine and optimize market-making mechanisms for more efficient opinion elicitation.
{"title":"Price Interpretability of Prediction Markets: A Convergence Analysis","authors":"Jianjun Gao, Zizhuo Wang, Weiping Wu, Dian Yu","doi":"10.1287/opre.2022.0417","DOIUrl":"https://doi.org/10.1287/opre.2022.0417","url":null,"abstract":"Prediction markets are renowned for their accuracy in forecasting. However, it is not fully clear how the predication market aggregates the traders’ beliefs. In “Price Interpretability of Prediction Markets: A Convergence Analysis,” Gao, Wang, Wu, and Yu introduce a novel multivariate utility (MU)-based mechanism that consolidates various existing automated market-making schemes. This mechanism establishes convergence results for markets consisting of risk-averse traders with diverse beliefs who interact repeatedly with the market maker. Furthermore, the study delivers analytical and numerical insights into the limiting price across different market models. Building on these results, the authors offer an efficient approximation scheme for the limiting price, shedding light on how traders’ beliefs shape market prices. These discoveries provide valuable guidance to market designers, enabling them to refine and optimize market-making mechanisms for more efficient opinion elicitation.","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141364214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In “Robust Financial Networks,” F. Hu, D. Mitchell, and S. Tompaidis study networks of financial institutions where only aggregate information on liabilities is available. The authors introduce the robust liability network, that is, the network consistent with the available information that exhibits the worst expected losses. They provide an algorithm to identify the robust liability network and, using aggregate data provided by bank holding companies to the Federal Reserve in form FR Y-9C, determine robust liability networks for U.S. banks under various network configurations. They show that the robust liability network is sparse, with links between institutions that hold highly correlated portfolios. They illustrate the methodology in two applications. (1) They look at how robust liability networks changed around the onset of the COVID-19 pandemic. (2) They evaluate the impact of a potential regulation that limits risk-taking based on each institution’s conditional value-at-risk. Their results can be used by regulators to monitor systemic risk in financial networks.
{"title":"Robust Financial Networks","authors":"Feihong Hu, Daniel Mitchell, S. Tompaidis","doi":"10.1287/opre.2022.0272","DOIUrl":"https://doi.org/10.1287/opre.2022.0272","url":null,"abstract":"In “Robust Financial Networks,” F. Hu, D. Mitchell, and S. Tompaidis study networks of financial institutions where only aggregate information on liabilities is available. The authors introduce the robust liability network, that is, the network consistent with the available information that exhibits the worst expected losses. They provide an algorithm to identify the robust liability network and, using aggregate data provided by bank holding companies to the Federal Reserve in form FR Y-9C, determine robust liability networks for U.S. banks under various network configurations. They show that the robust liability network is sparse, with links between institutions that hold highly correlated portfolios. They illustrate the methodology in two applications. (1) They look at how robust liability networks changed around the onset of the COVID-19 pandemic. (2) They evaluate the impact of a potential regulation that limits risk-taking based on each institution’s conditional value-at-risk. Their results can be used by regulators to monitor systemic risk in financial networks.","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":null,"pages":null},"PeriodicalIF":2.7,"publicationDate":"2024-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141371637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：