More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives
DSpace/Manakin Repository
More on the Power of Demand Queries in Combinatorial Auctions: Learning Atomic Languages and Handling Incentives
| 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. |
| Full Text & Related Files: |
Lahaie_More.pdf (162.0Kb; PDF) 
|
| 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 |
This item appears in the following Collection(s)
-
FAS Scholarly Articles [5171]
Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
Contact administrator regarding this item (to report mistakes or request changes)
