Ranking and Tradeoffs in Sponsored Search Auctions

TL;DR

This paper explores how to balance revenue and welfare in sponsored search auctions, proposing a new ranking algorithm based on reserve prices that improves revenue outcomes through simulations.

Contribution

It introduces a novel ranking algorithm using reserve prices, generalizes the connection between the lowest SNE and VCG, and evaluates tradeoffs via extensive simulations.

Findings

The new algorithm can outperform traditional methods in revenue.

The lowest SNE is a natural solution concept for tradeoff evaluation.

Simulations show improved tradeoff management with the proposed method.

Abstract

In a sponsored search auction, decisions about how to rank ads impose tradeoffs between objectives such as revenue and welfare. In this paper, 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.