Bayesian algorithmic mechanism design

• Published 2010 in BQGT

Abstract

The principal problem in algorithmic mechanism design is in merging the incentive constraints imposed by selfish behavior with the algorithmic constraints imposed by computational intractability. This field is motivated by the observation that the preeminent approach for designing incentive compatible mechanisms, namely that of Vickrey, Clarke, and Groves; and the central approach for circumventing computational obstacles, that of approximation algorithms, are fundamentally incompatible: natural applications of the VCG approach to an approximation algorithm fails to yield an incentive compatible mechanism. We consider relaxing the desideratum of (ex post) incentive compatibility (IC) to Bayesian incentive compatibility (BIC), where truthtelling is a Bayes-Nash equilibrium (the standard notion of incentive compatibility in economics). For welfare maximization in single-parameter agent settings, we give a general black-box reduction that turns any approximation algorithm into a Bayesian incentive compatible mechanism with essentially the same approximation factor.

DOI: 10.1145/1806689.1806732

Statistics

Citations per Year

102 Citations

Semantic Scholar estimates that this publication has 102 citations based on the available data.

See our FAQ for additional information.

Cite this paper

@inproceedings{Hartline2010BayesianAM, title={Bayesian algorithmic mechanism design}, author={Jason D. Hartline and Brendan Lucier}, booktitle={BQGT}, year={2010} }