报告题目：Does Soft-close Eliminate Sniping in Online Auctions? An Attention-based Explanation

报告所属学科：应用经济学

报告人：曹志刚（北京交通大学）

报告时间：2023年6月7日 9:00-12:00

报告地点：腾讯会议：407120368

报告摘要：

Whether soft-close ending rule eliminates sniping (i.e., bidding as late as possible) and its adverse consequences is a long-lasting concern to the field of online auctions. This paper examines the effectiveness of soft-close in online English auctions when attention poses a true challenge. We find that sniping remains the best reply when naive bidders have limited attentions. Its winning probability and winning price monotonously decrease as the naive bidder becomes more attentive and as the softening horizon increases. Using a unique setting large-scale data from Alibaba Judicial Auction, we find sniping works under soft-close, and evidence consistent with the comparative statics. We preclude several alternative explanations and conclude that inattention is perhaps the most reasonable explanation for why soft-close does not work perfectly in online English auctions. (joint work with Yunlong Wang, Xiaoguang Yang and Lin Zhao)

报告人简介：

曹志刚，北京交通大学经济管理学院教授。2010年毕业于中科院数学与系统科学研究院并留院任助理研究员。2017年9月加盟北京交通大学经济管理学院，任“卓越百人计划”教授。长期从事合作博弈、交通博弈、网络博弈和算法博弈等方面的研究，在包括Operations Research、Mathematics of Operations Research、Games and Economic Behavior、Journal of Mathematical Economics、Social Choice and Welfare、International Journal of Game Theory和《中国科学：数学》在内的期刊上发表多篇论文。相关成果曾获中国信息经济学理论贡献奖、系统科学与系统工程青年科技奖、中国决策科学青年科技奖和关肇直青年研究奖等荣誉。先后主持国家自然科学基金委的青年、面上和优青项目。兼任中国“双法”研究会智能决策与博弈分会副理事长，中国运筹学会博弈论分会副理事长，中国双法学会青年工作委员会副秘书长、网络科学分会副秘书长，中国信息经济学会常务理事和中国运筹学会理事等职务。

报告题目：Behavior-Based Pricing in Two-Sided Platforms: The Role of Network Effects

报告所属学科：管理科学与工程

报告人：虞义敏（香港城市大学）

报告时间：2023年6月8日 14:30-18:30

报告地点：经济与管理学院A0405教室

报告内容摘要：

Behavior-based pricing (BBP) is a pricing strategy used by firms to differentiate between past and new customers based on their purchase history data. Two-sided platforms commonly adopt BBP, but previous research has not considered the network effects. In this study, we use a duopoly two-period model to investigate the role of BBP in two-sided platforms, which connect the customer and developer sides. The developers can be either single-homing or multiple-homing. We consider the cross-side network effects between customers and developers, and our results show that customer-side BBP can increase platform profits when developers are multi-homing. This is in contrast to the traditional understanding of BBP's impact on profitability. The driving force behind this result is the cross-side network effects that reduce competition among customers in the first period. However, when developers are single-homing, BBP does not have any positive effect on platform profits. Our results shed light on the profitability of BBP on two-sided platforms and the role of cross-side network effects.

报告人简介：

虞义敏，博士，香港城市大学管理科学系/市场营销系副教授。本科毕业于中国科技大学，博士毕业于明尼苏达大学双子城分校。主要研究方向：供应链与服务运营管理，包括库存生产系统的优化设计、供应链的激励问题以及定价策略。论文发表于《Manufacturing & Service Operations Management》、《Marketing Science》、《Operations Research》、《Production and Operations Management》等商科顶刊。

报告题目：Remanufacturing and Pricing Strategies under Modular Architecture

报告所属学科：管理科学与工程

报告人：沈厚才（南京大学）

报告时间：2023年6月8日 14:30-18:30

报告地点：经济与管理学院A0405教室

报告内容摘要：

Modular design is an approach that accelerates product development and facilitates product remanufacturing. We examine two remanufacturing strategies under modular architecture: the partial remanufacturing, and the full remanufactured. We show that the manufacturer's choice of remanufacturing strategy depends on the production costs of the partial remanufactured product and the full remanufactured product, as well as the proportion of green consumers. The manufacturer only produces the new product when the production costs of both the mixed and the remanufactured products are high when there is no green consumer. Otherwise, the manufacturer will produce the partial or the full remanufactured product. When there are green consumers, the manufacturer always produces the remanufactured product, and the specific remanufacturing and pricing strategies depend on their production costs. Furthermore, as the proportion of green consumers increases, the manufacturer is more likely to implement the selective pricing strategy. Importantly, regardless of the presence of green consumers, the manufacturer prefers the partial remanufacturing strategy when the production cost of the partial remanufactured product is low. Regarding consumer surplus and social welfare, in the absence of green consumers, the manufacturer's profit is aligned with consumer surplus and social welfare. However, this alignment may not hold when green consumers are present.

报告人简介：

沈厚才，博士，南京大学工程管理学院管理科学与工程教授，博士生导师，兼南京大学数学系运筹学与控制论专业教授，博士生导师。他于1986年在南京大学获得数学专业学士学位，1989年、1995年在东南大学分别获得自动控制理论及应用专业硕士学位与博士学位。主要研究方向：应用优化、统计模型与博弈论等方法对各类复杂系统进行描述、分析与控制，现在重点关注大数据时代复杂系统的数据驱动优化决策理论、方法与应用。目前担任澳门科技大学商学院访问教授、江苏省运筹学会副理事长、江苏省管理科学与工程联盟副理事长。

报告题目：Quality and Private Label Encroachment Strategy

报告所属学科：管理科学与工程

报告人：陈剑（清华大学）

报告时间：2023年6月8日 14:30-18:30

报告地点：经济与管理学院A0405教室

报告内容摘要：

We consider a supply chain consisting of an incumbent national brand manufacturer and a retailer, who wishes to determine the private label encroachment strategy. By investigating the cost–quality trade-off between lower- and higher-quality private labels, we characterize the retailer’s optimal encroachment decisions. Intuitively, the retailer would choose to introduce a lower-quality private label when the quality of the national brand is high. However, we find that a higher-quality private label may better benefit the retailer under certain conditions. Moreover, we establish that introducing a higher-quality private label can improve both channel profit and consumer welfare. Using the results we derive, we explain the market practices and provide useful guidelines for retailers’ private label encroachment decisions.

报告人简介：

陈剑，现任清华大学经济管理学院管理科学与工程系联想讲席教授，清华大学经济管理学院管理科学与工程系系主任, 教育部人文社会科学重点研究基地——清华大学现代管理研究中心主任，国务院学位委员会第七届学科评议组成员，教育部高等学校管理科学与工程类专业教学指导委员会秘书长。主要研究方向：供应链管理、电子商务、商务智能等。主持五十余个国家自然科学基金委、教育部、863等国家部委项目，以及地方政府/企业委托项目；在国内外学术期刊上发表论文二百多篇。应邀在多个国际会议上做大会报告(Keynote/Plenary Lecture)。获得过多项科技奖励及荣誉称号，如国际电气和电子工程师协会会士；教育部长江学者；首届复旦管理学奖；全国优秀博士学位论文指导教师；国家杰出青年科学基金；IBM学院奖等。在多个学术组织中任职，如担任生产和运营管理学会(POMS)副理事长(负责亚太区，2010-2012)，IEEE系统、人与控制论学会服务系统和组织专业委员会主席(2002-)，中国系统工程学会副理事长(2006-2014)，中国优选法统筹法与经济数学研究会副理事长(2006-2014)，中国管理现代化研究会副理事长(2016-)，中国管理学科与工程学会副理事长(2017-)等。也是许多国际会议的主席/共同主席(例如：2004年第一届国际服务系统和服务管理会议主席、2007年INFORMS制造和服务运作管理学会年会共同主席(MSOM07)、2012年INFORMS国际大会主席等)，同时担任多个国际学术刊物的编委。

报告题目：Optimal Risk Sharing for Lambda Value-at-Risk

报告所属学科：管理科学与工程

报告人：胡太忠（中国科学技术大学）

报告时间：2023年6月8日 9:00-10:30

报告地点：经管学院702会议室

报告内容摘要：

A new risk measure, the Lambda Value-at-Risk (VaR), was proposed from a theoretical point of view as a generalization of the ordinary VaR in the literature. Motivated by the recent developments in risk sharing problems for the VaR and other risk measures, we study the optimization of risk sharing for the Lambda VaR. Explicit formulas of the inf-convolution and Pareto-optimal allocations are obtained with respect to the left Lambda VaRs, the right Lambda VaRs, or a mixed collection of the left and right Lambda VaRs. The inf-convolution of Lambda VaRs constrained to comonotonic allocations is investigated. Explicit formulas for worst-case Lambda VaRs under model uncertainty induced by likelihood ratios and by Wasserstein metrics are also given. This is a joint work with Zichao Xia.

报告人简介：

胡太忠，中国科学技术大学管理学院教授，博士生导师。1994年于中国科大获概率论与数理统计专业博士学位，2001年晋升为教授，2004年入选教育部“新世纪优秀人才支持计划”，2014年获安徽省教学名师称号。研究领域包括精算理论、随机比较、统计相依、极值理论和可靠性数学等。现为国际期刊Insurance: Mathematics & Economics, Probability in the Engineering and Informational Sciences, Quality Technology & Quantitative Management 的副主编，Communications in Mathematics and Statistics和Dependence Modeling 的编委，《中国科学技术大学学报》学科主编；担任中国工业与应用数学“金融数学和工程专业委员会”副主任委员，中国现场统计研究会风险管理与精算分会副理事长。

报告题目：EBiCop: Ensemble Bivariate Copulas for Modeling Multivariate Cyber Data Breach Risks with Insurance Applications

报告所属学科：管理科学与工程

报告人：徐茂超（美国伊利诺伊州立大学）

报告时间：2023年6月8日 10:30-12:00

报告地点：经管学院702会议室

报告内容摘要：

Modeling the multivariate dependence among cyber data breach risks presents a significant challenge due to the sparsity and heavy tail properties exhibited by breach events. In this talk, we introduce a novel ensemble learning approach that effectively captures both the temporal and cross-sectional dependence inherent in cyberrisks. Our approach leverages bivariate copulas to generate predictive members, and the resulting predictive distribution is carefully calibrated by minimizing the distribution score. Moreover, we demonstrate the applicability of our proposed model in the domain of insurance pricing. Through extensive simulations and analysis of real-world data, our findings reveal that our approach outperforms existing method ologies reported in the literature. The superior performance of our approach highlights its potential to enhance risk assessment and insurance pricing practices related to cyber data breaches.报告人简介：

徐茂超博士于2010年在美国波特兰州立大学获得统计学博士学位，现为伊利诺伊州立大学数学系终身教授，研究生项目负责人。近年来主要开展基于统计方法的网络安全风险评估和治理研究，相关研究成果发表于Annals of Applied Statistics, Technimetrics，IEEE Transactions on Information Forensics and Security，IISE Transactions 等国际著名期刊。研究曾获伊利诺伊州立大学杰出研究奖，北美精算师协会最佳论文奖，以及应用统计杂志最佳论文推荐奖等。徐教授还担任美国网络风险评估公司Safe Security以及新加坡虚拟货币保险公司InsurAce的学术顾问。