Adverse Selection and Auction Design for Internet Display Advertising
成果类型：
Article
署名作者：
Arnosti, Nick; Beck, Marissa; Milgrom, Paul
署名单位：
Stanford University; Charles River Associates; Stanford University
刊物名称：
AMERICAN ECONOMIC REVIEW
ISSN/ISSBN：
0002-8282
DOI：
10.1257/aer.20141198
发表日期：
2016
页码：
2852-2866
关键词：
摘要：
We model an online display advertising environment in which performance advertisers can measure the value of individual impressions, whereas brand advertisers cannot. If advertiser values for ad opportunities are positively correlated, second-price auctions for impressions can be inefficient and expose brand advertisers to adverse selection. Bayesian-optimal auctions have other drawbacks: they are complex, introduce incentives for false-name bidding, and do not resolve adverse selection. We introduce modified second bid auctions as the unique auctions that overcome these disadvantages. When advertiser match values are drawn independently from heavy-tailed distributions, a modified second bid auction captures at least 94.8 percent of the first-best expected value. In that setting and similar ones, the benefits of switching from an ordinary second-price auction to the modified second bid auction may be large, and the cost of defending against shill bidding and adverse selection may be low.