An ironing-based approach to adaptive online mechanism design in single-valued domains

Authors:
David C. Parkes;Quang Duong
Affiliations:
School of Engineering and Applied Sciences, Harvard University, Cambridge, MA;School of Engineering and Applied Sciences, Harvard University, Cambridge, MA
Venue:
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Year:
2007

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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.