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Near-Optimality in Covering Games by Exposing Global Information

Near-Optimality in Covering Games by Exposing Global Information Near-Optimality in Covering Games by Exposing Global Information MARIA-FLORINA BALCAN, SARA KREHBIEL, and GEORGIOS PILIOURAS, Georgia Institute of Technology JINWOO SHIN, Korea Advanced Institute of Science and Technology Mechanism design for distributed systems is fundamentally concerned with aligning individual incentives with social welfare to avoid socially inefficient outcomes that can arise from agents acting autonomously. One simple and natural approach is to centrally broadcast nonbinding advice intended to guide the system to a socially near-optimal state while still harnessing the incentives of individual agents. The analytical challenge is proving fast convergence to near optimal states, and in this article we give the first results that carefully constructed advice vectors yield stronger guarantees. We apply this approach to a broad family of potential games modeling vertex cover and set cover optimization problems in a distributed setting. This class of problems is interesting because finding exact solutions to their optimization problems is NP-hard yet highly inefficient equilibria exist, so a solution in which agents simply locally optimize is not satisfactory. We show that with an arbitrary advice vector, a set cover game quickly converges to an equilibrium with cost of the same order as the square of the social cost http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Economics and Computation Association for Computing Machinery

Near-Optimality in Covering Games by Exposing Global Information

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2014 by ACM Inc.
ISSN
2167-8375
DOI
10.1145/2597890
Publisher site
See Article on Publisher Site

Abstract

Near-Optimality in Covering Games by Exposing Global Information MARIA-FLORINA BALCAN, SARA KREHBIEL, and GEORGIOS PILIOURAS, Georgia Institute of Technology JINWOO SHIN, Korea Advanced Institute of Science and Technology Mechanism design for distributed systems is fundamentally concerned with aligning individual incentives with social welfare to avoid socially inefficient outcomes that can arise from agents acting autonomously. One simple and natural approach is to centrally broadcast nonbinding advice intended to guide the system to a socially near-optimal state while still harnessing the incentives of individual agents. The analytical challenge is proving fast convergence to near optimal states, and in this article we give the first results that carefully constructed advice vectors yield stronger guarantees. We apply this approach to a broad family of potential games modeling vertex cover and set cover optimization problems in a distributed setting. This class of problems is interesting because finding exact solutions to their optimization problems is NP-hard yet highly inefficient equilibria exist, so a solution in which agents simply locally optimize is not satisfactory. We show that with an arbitrary advice vector, a set cover game quickly converges to an equilibrium with cost of the same order as the square of the social cost

Journal

ACM Transactions on Economics and ComputationAssociation for Computing Machinery

Published: Oct 28, 2014

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