Approximately Efficient Online Mechanism Design

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Approximately Efficient Online Mechanism Design

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Title: Approximately Efficient Online Mechanism Design
Author: Parkes, David C.; Singh, Satinder; Dimah, Yanovsky

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Citation: Parkes, David C., Satinder Singh, and Dimah Yanovsky. 2004. Approximately efficient online mechanism design. In Advances in neural information processing systems 17: Proceedings of the 2004 conference, ed. L. K Saul, Y. Weiss, and L. Bottou. Cambridge, M.A., London, England: MIT Press.
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Abstract: Online mechanism design (OMD) addresses the problem of sequential decision making in a stochastic environment with multiple self-interested agents. The goal in OMD is to make value-maximizing decisions despite this self-interest. In previous work we presented a Markov decision process (MDP)-based approach to OMD in large-scale problem domains. In practice the underlying MDP needed to solve OMD is too large and hence the mechanism must consider approximations. This raises the possibility that agents may be able to exploit the approximation for selfish gain. We adopt sparse-sampling-based MDP algorithms to implement efficient policies, and retain truth-revelation as an approximate Bayesian-Nash equilibrium. Our approach is empirically illustrated in the context of the dynamic allocation of WiFi connectivity to users in a coffeehouse.
Published Version: http://books.nips.cc/nips17.html
Other Sources: http://www.eecs.harvard.edu/econcs/pubs/mdp_omd04.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:4054442

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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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