Robust auctions for revenue via enhanced competition

Tim Roughgarden, Inbal Talgam-Cohen, Qiqi Yan

Research output: Contribution to journalArticlepeer-review

Abstract

Most results in revenue-maximizing mechanism design hinge on “getting the price right”-selling goods to bidders at prices low enough to encourage a sale but high enough to garner nontrivial revenue. This approach is difficult to implement when the seller has little or no a priori information about bidder valuations or when the setting is sufficiently complex, such as matching markets with heterogeneous goods. In this paper, we apply a robust approach to designing auctions for revenue. Instead of relying on prior knowledge regarding bidder valuations, we “let the market do the work” and let prices emerge from competition for scarce goods. We analyze the revenue guarantees of one of the simplest imaginable implementations of this idea: first, we enhance competition in the market by increasing demand (or alternatively, by limiting supply), and second, we run a standard second price (Vickrey) auction. In their renowned work from 1996, Bulow and Klemperer [Bulow J, Klemperer P (1996) Auctions vs. negotiations. Amer. Econom. Rev. 86(1):180-194.] apply this method to markets with single goods. As our main result, we give the first application beyond single-parameter settings, proving that, simultaneously for many valuation distributions, this method achieves expected revenue at least as good as the optimal revenue in the original market. Our robust and simple approach provides a handle on the elusive optimal revenue in multiitem matching markets and shows when the use of welfare-maximizing Vickrey auctions is justified, even if revenue is a priority. By establishing quantitative tradeoffs, our work provides guidelines for a seller in choosing among two different revenue-extracting strategies: Sophisticated pricing based on market research or advertising to draw additional bidders.

Original languageEnglish
Pages (from-to)1074-1094
Number of pages21
JournalOperations Research
Volume68
Issue number4
DOIs
StatePublished - Jul 2020

Keywords

  • Bidding and auctions
  • Matchings
  • Pricing

All Science Journal Classification (ASJC) codes

  • Computer Science Applications
  • Management Science and Operations Research

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