More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives

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More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives

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Title: More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives
Author: Lahaie, Sébastien; Constantin, Florin; Parkes, David C.

Note: Order does not necessarily reflect citation order of authors.

Citation: Lahaie, Sébastien, Florin Constantin, and David C. Parkes. 2005. More on the power of demand queries in combinatorial auctions: Learning atomic languages and handling incentives. In proceedings of the Nineteenth International Joint Conference on Artificial Intelligence: July 30-August 5, 2005, Edinburgh, Scotland, ed. A. Saffiotti, L. Pack Kaelbling, 959-964. Denver, C.O.: International Joint Conferences on Artificial Intelligence.
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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.
Published Version: http://www.ijcai.org/
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/lahaiecopa05.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:4031553

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  • FAS Scholarly Articles [7262]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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