Competitive auctions and digital goods
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
Adaptive limited-supply online auctions
EC '04 Proceedings of the 5th ACM conference on Electronic commerce
Mechanism Design via Machine Learning
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
On the competitive ratio of the random sampling auction
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Limited and online supply and the bayesian foundations of prior-free mechanism design
Proceedings of the 10th ACM conference on Electronic commerce
On the competitive ratio of online sampling auctions
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Proceedings of the 12th ACM conference on Electronic commerce
Mechanism design via consensus estimates, cross checking, and profit extraction
Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Prior-free auctions with ordered bidders
STOC '12 Proceedings of the forty-fourth annual ACM symposium on Theory of computing
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
Mechanism Design via Consensus Estimates, Cross Checking, and Profit Extraction
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
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In the context of auctions for digital goods, an interesting Random Sampling Optimal Price auction (RSOP) has been proposed by Goldberg, Hartline and Wright; this leads to a truthful mechanism. Since random sampling is a popular approach for auctions that aims to maximize the seller's revenue, this method has been analyzed further by Feige, Flaxman, Hartline and Kleinberg, who have shown that it is 15-competitive in the worst case -- which is substantially better than the previously proved bounds but still far from the conjectured competitive ratio of 4. In this paper, we prove that RSOP is indeed 4-competitive for a large class of instances in which the number λ of bidders receiving the item at the optimal uniform price, is at least 6. We also show that it is 4.68 competitive for the small class of remaining instances thus leaving a negligible gap between the lower and upper bound. Furthermore, we develop a robust version of RSOP -- one in which the seller's revenue is, with high probability, not much below its mean -- when the above parameter λ grows large. We employ a mix of probabilistic techniques and dynamic programming to compute these bounds.