{"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":"21 1","pages":""},"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":"80 1","pages":""},"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":"205 1","pages":""},"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}
Anna Winnicki, Joseph Lubars, Michael Livesay, R. Srikant
Operations Research, Ahead of Print.
运筹学》，印刷版前。
{"title":"The Role of Lookahead and Approximate Policy Evaluation in Reinforcement Learning with Linear Value Function Approximation","authors":"Anna Winnicki, Joseph Lubars, Michael Livesay, R. Srikant","doi":"10.1287/opre.2022.0357","DOIUrl":"https://doi.org/10.1287/opre.2022.0357","url":null,"abstract":"Operations Research, Ahead of Print. <br/>","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":"20 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141192335","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}
Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes
Classical Bayesian mechanism design relies on the common prior assumption, but the common prior is often not available in practice. We study the design of prior-independent mechanisms that relax this assumption: The seller is selling an indivisible item to n buyers such that the buyers’ valuations are drawn from a joint distribution that is unknown to both the buyers and the seller, buyers do not need to form beliefs about competitors, and the seller assumes the distribution is adversarially chosen from a specified class. We measure performance through the worst-case regret, or the difference between the expected revenue achievable with perfect knowledge of buyers’ valuations and the actual mechanism revenue. We study a broad set of classes of valuation distributions that capture a wide spectrum of possible dependencies: independent and identically distributed (i.i.d.) distributions, mixtures of i.i.d. distributions, affiliated and exchangeable distributions, exchangeable distributions, and all joint distributions. We derive in quasi closed form the minimax values and the associated optimal mechanism. In particular, we show that the first three classes admit the same minimax regret value, which is decreasing with the number of competitors, whereas the last two have the same minimax regret equal to that of the case n = 1. Furthermore, we show that the minimax optimal mechanisms have a simple form across all settings: a second-price auction with random reserve prices, which shows its robustness in prior-independent mechanism design. En route to our results, we also develop a principled methodology to determine the form of the optimal mechanism and worst-case distribution via first-order conditions that should be of independent interest in other minimax problems.
Supplemental Material: The online appendices are available at https://doi.org/10.1287/opre.2022.0428.
经典的贝叶斯机制设计依赖于共同先验假设，但在实践中往往无法获得共同先验。我们研究了放宽这一假设的与先验无关的机制设计：卖方要向 n 个买方出售一件不可分割的物品，而买方的估价来自买方和卖方都未知的联合分布，买方不需要形成关于竞争对手的信念，卖方假定该分布是从一个指定类别中逆向选择的。我们通过最坏情况下的遗憾（即在完全了解买方估值的情况下可实现的预期收益与实际机制收益之间的差额）来衡量绩效。我们研究了一系列广泛的估值分布类别，它们捕捉了各种可能的依赖关系：独立且同分布（i.i.d.）分布、i.i.d.分布的混合物、附属分布和可交换分布、可交换分布以及所有联合分布。我们以准封闭形式推导出最小值和相关的最优机制。我们特别指出，前三类的最小遗憾值相同，且随竞争者数量的增加而减小，而后两类的最小遗憾值与 n = 1 的情况相同。此外，我们还证明了最小最优机制在所有情况下都有一个简单的形式：带有随机底价的第二价格拍卖，这显示了它在与先验无关的机制设计中的稳健性。在得出结果的过程中，我们还开发了一种原则性方法，通过一阶条件确定最优机制的形式和最坏情况分布，这在其他 minimax 问题中也会引起兴趣：在线附录见 https://doi.org/10.1287/opre.2022.0428。
{"title":"On the Robustness of Second-Price Auctions in Prior-Independent Mechanism Design","authors":"Jerry Anunrojwong, Santiago R. Balseiro, Omar Besbes","doi":"10.1287/opre.2022.0428","DOIUrl":"https://doi.org/10.1287/opre.2022.0428","url":null,"abstract":"<p>Classical Bayesian mechanism design relies on the common prior assumption, but the common prior is often not available in practice. We study the design of prior-independent mechanisms that relax this assumption: The seller is selling an indivisible item to <i>n</i> buyers such that the buyers’ valuations are drawn from a joint distribution that is unknown to both the buyers and the seller, buyers do not need to form beliefs about competitors, and the seller assumes the distribution is adversarially chosen from a specified class. We measure performance through the worst-case <i>regret</i>, or the difference between the expected revenue achievable with perfect knowledge of buyers’ valuations and the actual mechanism revenue. We study a broad set of classes of valuation distributions that capture a wide spectrum of possible dependencies: independent and identically distributed (i.i.d.) distributions, mixtures of i.i.d. distributions, affiliated and exchangeable distributions, exchangeable distributions, and all joint distributions. We derive in quasi closed form the minimax values and the associated optimal mechanism. In particular, we show that the first three classes admit the same minimax regret value, which is decreasing with the number of competitors, whereas the last two have the same minimax regret equal to that of the case <i>n</i> = 1. Furthermore, we show that the minimax optimal mechanisms have a simple form across all settings: a <i>second-price auction with random reserve prices</i>, which shows its robustness in prior-independent mechanism design. En route to our results, we also develop a principled methodology to determine the form of the optimal mechanism and worst-case distribution via first-order conditions that should be of independent interest in other minimax problems.</p><p><b>Supplemental Material:</b> The online appendices are available at https://doi.org/10.1287/opre.2022.0428.</p>","PeriodicalId":54680,"journal":{"name":"Operations Research","volume":"39 1","pages":""},"PeriodicalIF":2.7,"publicationDate":"2024-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141063531","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}
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