Randomized algorithms
Envy-free auctions for digital goods
Proceedings of the 4th ACM conference on Electronic commerce
SODA '05 Proceedings of the sixteenth annual ACM-SIAM symposium on Discrete algorithms
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
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
Simple versus optimal mechanisms
Proceedings of the 10th ACM conference on Electronic commerce
Revenue maximization with a single sample
Proceedings of the 11th ACM conference on Electronic commerce
Proceedings of the 12th ACM conference on Electronic commerce
Mechanism Design with Set-Theoretic Beliefs
FOCS '11 Proceedings of the 2011 IEEE 52nd Annual Symposium on Foundations of Computer Science
Crowdsourced Bayesian auctions
Proceedings of the 3rd Innovations in Theoretical Computer Science Conference
Prior-independent multi-parameter mechanism design
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Prior-free auctions for budgeted agents
Proceedings of the fourteenth ACM conference on Electronic commerce
Near-optimal multi-unit auctions with ordered bidders
Proceedings of the fourteenth ACM conference on Electronic commerce
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Prior-free auctions are robust auctions that assume no distribution over bidders' valuations and provide worst-case (input-by-input) approximation guarantees. In contrast to previous work on this topic, we pursue good prior-free auctions with non-identical bidders. Prior-free auctions can approximate meaningful benchmarks for non-identical bidders only when "sufficient qualitative information" about the bidder asymmetry is publicly known. We consider digital goods auctions where there is a total ordering of the bidders that is known to the seller, where earlier bidders are in some sense thought to have higher valuations. We use the framework of Hartline and Roughgarden (STOC '08) to define an appropriate revenue benchmark: the maximum revenue that can be obtained from a bid vector using prices that are nonincreasing in the bidder ordering and bounded above by the second-highest bid. This monotone-price benchmark is always as large as the well-known fixed-price benchmark F(2), so designing prior-free auctions with good approximation guarantees is only harder. By design, an auction that approximates the monotone-price benchmark satisfies a very strong guarantee: it is, in particular, simultaneously near-optimal for essentially every Bayesian environment in which bidders' valuation distributions have nonincreasing monopoly prices, or in which the distribution of each bidder stochastically dominates that of the next. Of course, even if there is no distribution over bidders' valuations, such an auction still provides a quantifiable input-by-input performance guarantee. In this paper, we design a simple prior-free auction for digital goods with ordered bidders, the Random Price Restriction (RPR) auction. We prove that its expected revenue on every bid profile b is Ω(M(b)/log*n), where M denotes the monotone-price benchmark and log*n denotes the number of times that the log2 operator can be applied to n before the result drops below a fixed constant.