Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies

Yevgeniy Vorobeychik
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:583-590, 2009.

Abstract

We perform a simulation-based analysis of keyword auctions modeled as one-shot games of incomplete information to study a series of mechanism design questions. Our first question addresses the degree to which incentive compatibility fails in generalized second-price (GSP) auctions. Our results suggest that sincere bidding in GSP auctions is a strikingly poor strategy and a poor predictor of equilibrium outcomes. We next show that the rank-by-revenue mechanism is welfare optimal, corroborating past results. Finally, we analyze profit as a function of auction mechanism under a series of alternative settings. Our conclusions coincide with those of Lahaie and Pennock [2007] when values and quality scores are strongly positively correlated: in such a case, rank-by-bid rules are clearly superior. We diverge, however, in showing that auctions that put little weight on quality scores almost universally dominate the pure rank-by-revenue scheme.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-vorobeychik09a, title = {Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies}, author = {Vorobeychik, Yevgeniy}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {583--590}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/vorobeychik09a/vorobeychik09a.pdf}, url = {https://proceedings.mlr.press/r7/vorobeychik09a.html}, abstract = {We perform a simulation-based analysis of keyword auctions modeled as one-shot games of incomplete information to study a series of mechanism design questions. Our first question addresses the degree to which incentive compatibility fails in generalized second-price (GSP) auctions. Our results suggest that sincere bidding in GSP auctions is a strikingly poor strategy and a poor predictor of equilibrium outcomes. We next show that the rank-by-revenue mechanism is welfare optimal, corroborating past results. Finally, we analyze profit as a function of auction mechanism under a series of alternative settings. Our conclusions coincide with those of Lahaie and Pennock [2007] when values and quality scores are strongly positively correlated: in such a case, rank-by-bid rules are clearly superior. We diverge, however, in showing that auctions that put little weight on quality scores almost universally dominate the pure rank-by-revenue scheme.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies %A Yevgeniy Vorobeychik %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-vorobeychik09a %I PMLR %P 583--590 %U https://proceedings.mlr.press/r7/vorobeychik09a.html %V R7 %X We perform a simulation-based analysis of keyword auctions modeled as one-shot games of incomplete information to study a series of mechanism design questions. Our first question addresses the degree to which incentive compatibility fails in generalized second-price (GSP) auctions. Our results suggest that sincere bidding in GSP auctions is a strikingly poor strategy and a poor predictor of equilibrium outcomes. We next show that the rank-by-revenue mechanism is welfare optimal, corroborating past results. Finally, we analyze profit as a function of auction mechanism under a series of alternative settings. Our conclusions coincide with those of Lahaie and Pennock [2007] when values and quality scores are strongly positively correlated: in such a case, rank-by-bid rules are clearly superior. We diverge, however, in showing that auctions that put little weight on quality scores almost universally dominate the pure rank-by-revenue scheme. %Z Reissued by PMLR on 04 October 2026.
APA
Vorobeychik, Y.. (2009). Simulation-Based Game Theoretic Analysis of Keyword Auctions with Low-Dimensional Bidding Strategies. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:583-590 Available from https://proceedings.mlr.press/r7/vorobeychik09a.html. Reissued by PMLR on 04 October 2026.

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