On the competitive ratio of the random sampling auction

Authors:
Uriel Feige;Abraham Flaxman;Jason D. Hartline;Robert Kleinberg
Affiliations:
Microsoft Research, Redmond, WA;Dept. of Computer Science, Carnegie Mellon University, Pittsburgh, PA;Microsoft Research, Mountain View, CA;Dept. of Computer Science, Cornell University, Ithaca, NY
Venue:
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Year:
2005

Citing 7
Cited 15

Competitive auctions and digital goods

SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Competitive generalized auctions

STOC '02 Proceedings of the thiry-fourth annual ACM symposium on Theory of computing
Competitive Auctions for Multiple Digital Goods

ESA '01 Proceedings of the 9th Annual European Symposium on Algorithms
Adaptive limited-supply online auctions

EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Multi-unit auctions with budget-constrained bidders

Proceedings of the 6th ACM conference on Electronic commerce
Mechanism Design via Machine Learning

FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Knapsack auctions

SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm

Multi-unit auctions with unknown supply

EC '06 Proceedings of the 7th ACM conference on Electronic commerce
Matroids, secretary problems, and online mechanisms

SODA '07 Proceedings of the eighteenth annual ACM-SIAM symposium on Discrete algorithms
Optimal mechanism design and money burning

STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Reducing mechanism design to algorithm design via machine learning

Journal of Computer and System Sciences
Limited and online supply and the bayesian foundations of prior-free mechanism design

Proceedings of the 10th ACM conference on Electronic commerce
On random sampling auctions for digital goods

Proceedings of the 10th ACM conference on Electronic commerce
An Online Multi-unit Auction with Improved Competitive Ratio

WINE '09 Proceedings of the 5th International Workshop on Internet and Network Economics
On the competitive ratio of online sampling auctions

WINE'10 Proceedings of the 6th international conference on Internet and network economics
Sum of us: strategyproof selection from the selectors

Proceedings of the 13th Conference on Theoretical Aspects of Rationality and Knowledge
Secretary problems with convex costs

ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part I
Limited supply online auctions for revenue maximization

WINE'12 Proceedings of the 8th international conference on Internet and Network Economics
Selling in Exclusive Markets: Some Observations on Prior-Free Mechanism Design

ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
On the Competitive Ratio of Online Sampling Auctions

ACM Transactions on Economics and Computation - Special Issue on Algorithmic Game Theory
Using lotteries to approximate the optimal revenue

Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Competitive auctions for markets with positive externalities

ICALP'13 Proceedings of the 40th international conference on Automata, Languages, and Programming - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

We give a simple analysis of the competitive ratio of the random sampling auction from [10]. The random sampling auction was first shown to be worst-case competitive in [9] (with a bound of 7600 on its competitive ratio); our analysis improves the bound to 15. In support of the conjecture that random sampling auction is in fact 4-competitive, we show that on the equal revenue input, where any sale price gives the same revenue, random sampling is exactly a factor of four from optimal.