Automated Mechanism Design without Money via Machine Learning

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Automated Mechanism Design without Money via Machine Learning

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Title: Automated Mechanism Design without Money via Machine Learning
Author: Narasimhan, Harikrishna; Agarwal, Shivani Brinda; Parkes, David C.

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Citation: Narasimhan, Harikrishna, Shivani Agarwal, and David C. Parkes. 2016. Automated Mechanism Design without Money via Machine Learning. In Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI 2016), New York, NY, July 9-15, 2016.
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Abstract: We use statistical machine learning to develop methods for automatically designing mechanisms in domains without money. Our goal is to find a mechanism that best approximates a given target function subject to a design constraint such as strategy-proofness or stability. The proposed approach involves identifying a rich parametrized class of mechanisms that resemble discriminant-based multiclass classifiers, and relaxing the resulting search problem into an SVM-style surrogate optimization problem. We use this methodology to design strategy-proof mechanisms for social choice problems with single-peaked preferences, and stable mechanisms for two-sided matching problems. To the best of our knowledge, ours is the first automated approach for designing stable matching rules. Experiments on synthetic and real-world data confirm the usefulness of our methods.
Published Version: http://www.ijcai.org/Abstract/16/068
Terms of Use: This article is made available under the terms and conditions applicable to Open Access Policy Articles, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#OAP
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:32216360
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