Ranking and Tradeoffs in Sponsored Search Auctions

Abstract

In a sponsored search auction, decisions about how to rank ads impose tradeoffs between objectives, such as revenue and welfare. In this article, we examine how these tradeoffs should be made. We begin by arguing that the most natural solution concept to evaluate these tradeoffs is the lowest symmetric Nash equilibrium (SNE). As part of this argument, we generalise the well-known connection between the lowest SNE and the VCG outcome. We then propose a new ranking algorithm, loosely based on the revenue-optimal auction, that uses a reserve price to order the ads (not just to filter them) and give conditions under which it raises more revenue than simply applying that reserve price. Finally, we conduct extensive simulations examining the tradeoffs enabled by different ranking algorithms and show that our proposed algorithm enables superior operating points by a variety of metrics.

Nonparametric Estimation of Sponsored Search Auctions and Impacts of Ad Quality on Search Revenue

“…We also consider the sum of revenue and profit as changing ranking rules might also impact the advertiser's decision to continue using the search engine. Thus, as noted in Lahaie and Pennock (2007) and Roberts et al (2016), in the long term, the search engines might want to maximize a weighted average of their revenue and advertisers' profit.…”

“…However, this statement contradicts the results fromSun et al (2014), who showed that the bid in the weighted GSP auction is equal to the weighted value divided by the quality score (β(v × s)/s). Furthermore,Sun et al (2014) did not formally prove that the derived bid is the equilibrium bid in the weighted GSP auction.7 This paper is also loosely related to the literature on looking at various ranking rules for GSP auctions, which include papers such asBörgers et al (2013), Zhang et al (2021,Roberts et al (2016)…”