An Ironing-Based Approach to Adaptive Online Mechanism Design in Single-Valued Domains

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An Ironing-Based Approach to Adaptive Online Mechanism Design in Single-Valued Domains

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Title: An Ironing-Based Approach to Adaptive Online Mechanism Design in Single-Valued Domains
Author: Parkes, David C.; Duong, Quang

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

Citation: Parkes, David C. and Quang Duong. 2007. An ironing-based approach to adaptive online mechanism design in single-valued domains. In Proceedings of the Twenty-second AAAI Conference on Artificial Intelligence: July 22-26, 2007, Vancouver, British Columbia, Canada, ed. American Association for Artificial Intelligence, 94-101. Menlo Park, Calif.: AAAI Press.
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Abstract: Online mechanism design considers the problem of sequential decision making in a multi-agent system with self-interested agents. The agent population is dynamic and each agent has private information about its value for a sequence of decisions. We introduce a method ("ironing") to transform an algorithm for online stochastic optimization into one that is incentive-compatible. Ironing achieves this by canceling decisions that violate a form of monotonicity. The approach is applied to the CONSENSUS algorithm and experimental results in a resource allocation domain show that not many decisions need to be canceled and that the overhead of ironing is manageable.
Published Version: http://portal.acm.org/citation.cfm?id=1619645.1619661
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/aaai07.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:4039777

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  • FAS Scholarly Articles [7374]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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