Iterative Combinatorial Auctions: Theory and Practice

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Iterative Combinatorial Auctions: Theory and Practice

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Title: Iterative Combinatorial Auctions: Theory and Practice
Author: Parkes, David C.; Ungar, Lyle H.

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

Citation: Parkes, David C., and Lyle H. Ungar. 2000. Iterative combinatorial auctions: Theory and practice. In Proceedings: Seventeenth National Conference on Artificial Intelligence (AAAI-2000): Twelfth Innovative Applications of Artificial Intelligence Conference (IAAI-2000), ed. American Association for Artificial Intelligence, 74-81. Menlo Park, C.A.: AAAI Press ; Cambridge, M.A.: MIT Press.
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Abstract: Combinatorial auctions, which allow agents to bid directly for bundles of resources, are necessary for optimal auction-based solutions to resource allocation problems with agents that have non-additive values for resources, such as distributed scheduling and task assignment problems. We introduce iBundle, the first iterative combinatorial auction that is optimal for a reasonable agent bidding strategy, in this case myopic best-response bidding. Its optimality is proved with a novel connection to primal-dual optimization theory. We demonstrate orders of magnitude performance improvements over the only other known optimal combinatorial auction, the Generalized Vickrey Auction.
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/ibundle00.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:4101023

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  • FAS Scholarly Articles [7078]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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