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Automated design of robust mechanisms

Publication ,  Conference
Albert, M; Conitzer, V; Stone, P
Published in: 31st AAAI Conference on Artificial Intelligence, AAAI 2017
January 1, 2017

We introduce a new class of mechanisms, robust mechanisms, that is an intermediary between ex-post mechanisms and Bayesian mechanisms. This new class of mechanisms allows the mechanism designer to incorporate imprecise estimates of the distribution over bidder valuations in a way that provides strong guarantees that the mechanism will perform at least as well as ex-post mechanisms, while in many cases performing better. We further extend this class to mechanisms that are with high probability incentive compatible and individually rational, e-robust mechanisms. Using techniques from automated mechanism design and robust optimization, we provide an algorithm polynomial in the number of bidder types to design robust and e-robust mechanisms. We show experimentally that this new class of mechanisms can significantly outperform traditional mechanism design techniques when the mechanism designer has an estimate of the distribution and the bidder's valuation is correlated with an externally verifiable signal.

Duke Scholars

Published In

31st AAAI Conference on Artificial Intelligence, AAAI 2017

Publication Date

January 1, 2017

Start / End Page

298 / 304
 

Citation

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Albert, M., Conitzer, V., & Stone, P. (2017). Automated design of robust mechanisms. In 31st AAAI Conference on Artificial Intelligence, AAAI 2017 (pp. 298–304).
Albert, M., V. Conitzer, and P. Stone. “Automated design of robust mechanisms.” In 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 298–304, 2017.
Albert M, Conitzer V, Stone P. Automated design of robust mechanisms. In: 31st AAAI Conference on Artificial Intelligence, AAAI 2017. 2017. p. 298–304.
Albert, M., et al. “Automated design of robust mechanisms.” 31st AAAI Conference on Artificial Intelligence, AAAI 2017, 2017, pp. 298–304.
Albert M, Conitzer V, Stone P. Automated design of robust mechanisms. 31st AAAI Conference on Artificial Intelligence, AAAI 2017. 2017. p. 298–304.

Published In

31st AAAI Conference on Artificial Intelligence, AAAI 2017

Publication Date

January 1, 2017

Start / End Page

298 / 304