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American Economic Review Vol. 106 No. 10 2016

Adverse Selection and Auction Design for Internet Display Advertising

Nick Arnosti1; Marissa Beck2; Paul Milgrom3

1 Department of Management Science and Engineering, Stanford University, 475 Via Ortega, Stanford, CA 94305 (email: ) · 2 Charles River Associates, 5335 College Ave., Suite 26, Oakland, CA 94618 (email: ) · 3 Stanford University, 579 Serra Mall, Stanford, CA 94305 (email: )

open access

Abstract

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.

DOI
10.1257/aer.20141198
Volume
106
Issue
10
Pages
2852-2866
Language
en
Sources
bibtex:phds-export.bib crossref openalex

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