GrowRange: Anytime VCG-Based Mechanisms
| Title: | GrowRange: Anytime VCG-Based Mechanisms |
| Author: |
Parkes, David C.; Schoenebeck, Grant
Note: Order does not necessarily reflect citation order of authors. |
| Citation: | Parkes, David C. and Grant Schoenebeck. 2004. GrowRange: Anytime VCG-based mechanisms. In Proceedings of the 19th national conference on artifical intelligence(AAAI-04): Sixteenth innovative applications of artificial intelligence conference (IAAI-04), ed. American Association for Artificial Intelligence, 34-41. Menlo Park, C.A.: AAAI Press; Cambridge, M.A.: MIT Press. |
| Full Text & Related Files: |
Parkes_GrowRange.pdf (179.3Kb; PDF) 
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| Abstract: | We introduce anytime mechanisms for distributed optimization with self-interested agents. Anytime mechanisms retain good incentive properties even when interrupted before the optimal solution is computed, and provide better quality solutions when given additional time. Anytime mechanisms can solve easy instances of a hard problem quickly and optimally, while providing approximate solutions on very hard instances. In a particular instantiation, GROWRANGE, we successively expand the range of outcomes considered, computing the optimal solution for each range. Truth-revelation remains a dominant strategy equilibrium with a stage-based interruption, and is a best-response with high probability when the interruption is time-based. |
| Published Version: | http://www.aaai.org/Papers/AAAI/2004/AAAI04-006.pdf |
| Other Sources: | http://www.eecs.harvard.edu/econcs/pubs/anytime.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:4039784 |
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