Combinatorial Auctions
Computing optimal bundles for sponsored search
WINE'07 Proceedings of the 3rd international conference on Internet and network economics
To match or not to match: economics of cookie matching in online advertising
Proceedings of the 13th ACM Conference on Electronic Commerce
SAGT'12 Proceedings of the 5th international conference on Algorithmic Game Theory
Constrained signaling for welfare and revenue maximization
ACM SIGecom Exchanges
Revenue maximization via hiding item attributes
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Signaling Competition and Social Welfare
ACM Transactions on Economics and Computation
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Emek et al presented a model of probabilistic single-item second price auctions where an auctioneer who is informed about the type of an item for sale, broadcasts a signal about this type to uninformed bidders. They proved that finding the optimal (for the purpose of generating revenue) pure signaling scheme is strongly NP-hard. In contrast, we prove that finding the optimal mixed signaling scheme can be done in polynomial time using linear programming. For the proof, we show that the problem is strongly related to a problem of optimally bundling divisible goods for auctioning. We also prove that a mixed signaling scheme can in some cases generate twice as much revenue as the best pure signaling scheme and we prove a generally applicable lower bound on the revenue generated by the best mixed signaling scheme.