报告题目：Sniping and Limited-attention in Online Auctions

报告人：Zhigang Cao（曹志刚），Beijing Jiaotong University（北京交通大学）

报告时间：2022年3月23日(周三)16:00-17:30

报告地点：中国科学院大学中关村校区教学楼S406；腾讯会议 ID：762 481 215

内容摘要：Sniping (bidding as late as possible) is prevalent in online auctions and harms the efficiency of trading. A common solution adopted by many platforms is changing the ending rule from hard-close to soft-close (i.e., the auction automatically extends for some period of time whenever a bid is submitted within the last few minutes of ending). Some recent empirical studies show that sniping may still be prevalent in soft-close online auctions. Does soft-close ending rule really solve the problem caused by sniping? We analyze a limited-attention model and argue that the answer is in some degree still yes. We prove that, as in hard-close auctions, sniping is the uniquely optimal best-reply to a large class of naive bidders in soft-close auctions. This explains why sniping may still be prevalent in soft-close online auctions. However, the winning probability and the expected profit by using the sniping strategy are both lower in soft-close auctions than in hard-close ones, and the expected revenue of the seller is also higher, demonstrating that soft-close ending rule works. We also provide additional empirical evidences from a large-scale data of Alibaba Judicial Auction. (joint work with Yunlong Wang, Xiaoguang Yang and Lin Zhao)

主讲人简介：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Operations Research、Mathematics of Operations Research和Games and Economic Behavior等期刊发表多篇论文。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等职务。