An optimal deterministic algorithm for online b-matching
Theoretical Computer Science
The spending constraint model for market equilibrium: algorithmic, existence and uniqueness results
STOC '04 Proceedings of the thirty-sixth annual ACM symposium on Theory of computing
AdWords and Generalized On-line Matching
FOCS '05 Proceedings of the 46th Annual IEEE Symposium on Foundations of Computer Science
Online stochastic packing applied to display ad allocation
ESA'10 Proceedings of the 18th annual European conference on Algorithms: Part I
Frequency capping in online advertising
WADS'11 Proceedings of the 12th international conference on Algorithms and data structures
Advertisement allocation for generalized second-pricing schemes
Operations Research Letters
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The choice of a bidding language is crucial in auction design in order to correctly capture bidder utilities. We propose a new bidding model for the Adwords auctions of search engine advertisement - decreasing valuation bids. This provides a richer language than the current model for advertisers to convey their preferences. Besides providing more expressivity, our bidding model has two additional advantages: It is an add-on to the standard model, and retains its simplicity of expression. Furthermore, it allows efficient algorithms - we show that the greedy (highest bid) algorithm retains its factor of 1/2 from the standard bidding model, and also provide an optimal allocation algorithm with a factor of 1-1/e (as is case in the standard bidding model). We also show how these bidding languages achieve a good trade-off between expressivity and complexity - we demonstrate a slight generalization of these models for which the greedy allocation algorithm has an arbitrarily bad competitive ratio.