More on the power of demand queries in combinatorial auctions: learning atomic languages and handling incentives

Authors:
Sébastien Lahaie;Florin Constantin;David C. Parkes
Affiliations:
Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA;Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA;Division of Engineering and Applied Sciences, Harvard University, Cambridge, MA
Venue:
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
Year:
2005

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Abstract

Query learning models from computational learning theory (CLT) can be adopted to perform elicitation in combinatorial auctions. Indeed, a recent elicitation framework demonstrated that the equivalence queries of CLT can be usefully simulated with price-based demand queries. In this paper, we validate the flexibility of this framework by defining a learning algorithm for atomic bidding languages, a class that includes XOR and OR. We also handle incentives, characterizing the communication requirements of the Vickrey-Clarke-Groves outcome rule. This motivates an extension to the earlier learning framework that brings truthful responses to queries into an equilibrium.