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Computationally feasible automated mechanism design: General approach and case studies

Publication ,  Journal Article
Guo, M; Conitzer, V
Published in: Proceedings of the National Conference on Artificial Intelligence
January 1, 2010

In many multiagent settings, a decision must be made based on the preferences of multiple agents, and agents may lie about their preferences if this is to their benefit. In mechanism design, the goal is to design procedures (mechanisms) for making the decision that work in spite of such strategic behavior, usually by making untruthful behavior suboptimal. In automated mechanism design, the idea is to computationally search through the space of feasible mechanisms, rather than to design them analytically by hand. Unfortunately, the most straightforward approach to automated mechanism de sign does not scale to large instances, because it requires searching over a very large space of possible functions. In this paper, we describe an approach to automated mechanism design that is computationally feasible. Instead of optimizing over all feasible mechanisms, we carefully choose a parameterized subfamily of mechanisms. Then we optimize over mechanisms within this family, and analyze whether and to what extent the resulting mechanism is suboptimal outside the subfamily. We demonstrate the usefulness of our approach with two case studies. Copyright © 2010, Association for the Advancement of Artificial Intelligence (www.aaai.org). All rights reserved.

Duke Scholars

Published In

Proceedings of the National Conference on Artificial Intelligence

Publication Date

January 1, 2010

Volume

3

Start / End Page

1676 / 1679
 

Citation

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MLA
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Guo, M., & Conitzer, V. (2010). Computationally feasible automated mechanism design: General approach and case studies. Proceedings of the National Conference on Artificial Intelligence, 3, 1676–1679.
Guo, M., and V. Conitzer. “Computationally feasible automated mechanism design: General approach and case studies.” Proceedings of the National Conference on Artificial Intelligence 3 (January 1, 2010): 1676–79.
Guo M, Conitzer V. Computationally feasible automated mechanism design: General approach and case studies. Proceedings of the National Conference on Artificial Intelligence. 2010 Jan 1;3:1676–9.
Guo, M., and V. Conitzer. “Computationally feasible automated mechanism design: General approach and case studies.” Proceedings of the National Conference on Artificial Intelligence, vol. 3, Jan. 2010, pp. 1676–79.
Guo M, Conitzer V. Computationally feasible automated mechanism design: General approach and case studies. Proceedings of the National Conference on Artificial Intelligence. 2010 Jan 1;3:1676–1679.

Published In

Proceedings of the National Conference on Artificial Intelligence

Publication Date

January 1, 2010

Volume

3

Start / End Page

1676 / 1679