Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design

69 Pages Posted: 21 Sep 2012 Last revised: 22 Jun 2014

Santiago Balseiro

Duke University - Decision Sciences

Omar Besbes

Columbia Business School - Decision Risk and Operations

Gabriel Y. Weintraub

Stanford Graduate School of Business, Stanford University; Columbia University - Columbia Business School - Decision Risk and Operations

Date Written: March 11, 2014

Abstract

Ad Exchanges are emerging Internet markets where advertisers may purchase display ad placements, in real-time and based on specific viewer information, directly from publishers via a simple auction mechanism. Advertisers join these markets with a pre-specified budget and participate in multiple second-price auctions over the length of a campaign. This paper studies the competitive landscape that arises in Ad Exchanges and the implications for publishers' decisions. The presence of budgets introduces dynamic interactions among advertisers that need to be taken into account when attempting to characterize the bidding landscape or the impact of changes in the auction design. To this end, we introduce the notion of a Fluid Mean Field Equilibrium (FMFE) that is behaviorally appealing, computationally tractable, and in some important cases yields a closed-form characterization. We establish that a FMFE approximates well the rational behavior of advertisers in these markets. We then show how this framework may be used to provide sharp prescriptions for key auction design decisions that publishers face in these markets. In particular, we show that ignoring budgets, a common practice in this literature, can result in significant profit losses for the publisher when setting the reserve price.

Keywords: auction design, revenue management, ad exchange, display advertising, internet, budget constraints, dynamic games, mean field, fluid approximation

Suggested Citation

Balseiro, Santiago and Besbes, Omar and Weintraub, Gabriel Y., Repeated Auctions with Budgets in Ad Exchanges: Approximations and Design (March 11, 2014). NET Institute Working Paper No. 12-11; Columbia Business School Research Paper No. 12/55. Available at SSRN: https://ssrn.com/abstract=2149319 or http://dx.doi.org/10.2139/ssrn.2149319

Santiago Balseiro

Duke University - Decision Sciences ( email )

100 Fuqua Drive
Durham, NC 27708-0120
United States

Omar Besbes (Contact Author)

Columbia Business School - Decision Risk and Operations ( email )

New York, NY
United States

Gabriel Y. Weintraub

Stanford Graduate School of Business, Stanford University ( email )

Stanford, CA 94305
United States

Columbia University - Columbia Business School - Decision Risk and Operations ( email )

New York, NY
United States

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