Cost-Sharing Mechanisms for Network Design

Authors:
Anupam Gupta;Aravind Srinivasan;Éva Tardos
Affiliations:
Carnegie Mellon University, Department of Computer Science, 15232, Pittsburgh, PA, USA;University of Maryland at College Park, Department of Computer Science and University of Maryland Institute for Advanced Computer Studies, 20742, College Park, MD, USA;Cornell University, Department of Computer Science, 14853, Ithaca, NY, USA
Venue:
Algorithmica
Year:
2007

Citing 0
Cited 6

Deterministic Sampling Algorithms for Network Design

ESA '08 Proceedings of the 16th annual European symposium on Algorithms
Quantifying inefficiency in cost-sharing mechanisms

Journal of the ACM (JACM)
On approximate nash equilibria in network design

WINE'10 Proceedings of the 6th international conference on Internet and network economics
Black-box reductions for cost-sharing mechanism design

Proceedings of the twenty-third annual ACM-SIAM symposium on Discrete Algorithms
Minimizing rosenthal potential in multicast games

ICALP'12 Proceedings of the 39th international colloquium conference on Automata, Languages, and Programming - Volume Part II
On Nash Equilibria for a Network Creation Game

ACM Transactions on Economics and Computation

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider a single-source network design problem from a game-theoretic perspective. Gupta, Kumar and Roughgarden (Proc. 35th Annual ACM STOC, pp. 365–372, 2003) developed a simple method for a single-source rent-or-buy problem that also yields the best-known approximation ratio for the problem. We show how to use a variant of this method to develop an approximately budget-balanced and group strategyproof cost-sharing method for the problem. The novelty of our approach stems from our obtaining the cost-sharing methods for the rent-or-buy problem by carefully combining cost-shares for the simpler Steiner tree problem. Our algorithm is conceptually simpler than the previous such cost-sharing method due to Pál and Tardos (Proc. 44th Annual FOCS, pp. 584–593, 2003), and improves the previously-known approximation factor of 15 to 4.6.