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Payment rules through discriminant-based classifiers

Dütting, Paul, Fischer, Felix, Jirapinyo, Pichayut, Lai, John K., Lubin, Benjamin and Parkes, David C. (2012) Payment rules through discriminant-based classifiers. In: Faltings, Boi, Leyton-Brown, Kevin and Ipeirotis, Panos, (eds.) Proceedings of the 13th ACM Conference on Electronic Commerce. Association for Computing Machinery, New York, NY, pp. 477-494. ISBN 9781450314152

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Identification Number: 10.1145/2229012.2229048


In mechanism design it is typical to impose incentive compatibility and then derive an optimal mechanism subject to this constraint. By replacing the incentive compatibility requirement with the goal of minimizing expected ex post regret, we are able to adapt statistical machine learning techniques to the design of payment rules. This computational approach to mechanism design is applicable to domains with multi-dimensional types and situations where computational efficiency is a concern. Specifically, given an outcome rule and access to a type distribution, we train a support vector machine with a special discriminant function structure such that it implicitly establishes a payment rule with desirable incentive properties. We discuss applications to a multi-minded combinatorial auction with a greedy winner-determination algorithm and to an assignment problem with egalitarian outcome rule. Experimental results demonstrate both that the construction produces payment rules with low ex post regret, and that penalizing classification errors is effective in preventing failures of ex post individual rationality.

Item Type: Book Section
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Additional Information: © 2012 Association for Computing Machinery
Divisions: Mathematics
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Date Deposited: 16 Nov 2017 15:54
Last Modified: 16 May 2024 05:34

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