Approximation algorithms
Proceedings of the twenty-second annual symposium on Principles of distributed computing
Local Search Heuristics for k-Median and Facility Location Problems
SIAM Journal on Computing
On nash equilibria for a network creation game
SODA '06 Proceedings of the seventeenth annual ACM-SIAM symposium on Discrete algorithm
Proceedings of the twenty-second annual ACM symposium on Parallelism in algorithms and architectures
The price of anarchy in network creation games is (mostly) constant
SAGT'10 Proceedings of the Third international conference on Algorithmic game theory
On approximate nash equilibria in network design
WINE'10 Proceedings of the 6th international conference on Internet and network economics
The price of anarchy in cooperative network creation games
ACM SIGecom Exchanges
On a bounded budget network creation game
Proceedings of the twenty-third annual ACM symposium on Parallelism in algorithms and architectures
On dynamics in basic network creation games
SAGT'11 Proceedings of the 4th international conference on Algorithmic game theory
The price of anarchy in network creation games
ACM Transactions on Algorithms (TALG)
Asymmetric swap-equilibrium: a unifying equilibrium concept for network creation games
MFCS'12 Proceedings of the 37th international conference on Mathematical Foundations of Computer Science
ACM SIGMETRICS Performance Evaluation Review
Basic network creation games with communication interests
SAGT'12 Proceedings of the 5th international conference on Algorithmic Game Theory
On dynamics in selfish network creation
Proceedings of the twenty-fifth annual ACM symposium on Parallelism in algorithms and architectures
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We introduce and analyze greedy equilibria (GE) for the well-known model of selfish network creation by Fabrikant et al. [PODC'03]. GE are interesting for two reasons: (1) they model outcomes found by agents which prefer smooth adaptations over radical strategy-changes, (2) GE are outcomes found by agents which do not have enough computational resources to play optimally. In the model of Fabrikant et al. agents correspond to Internet Service Providers which buy network links to improve their quality of network usage. It is known that computing a best response in this model is NP-hard. Hence, poly-time agents are likely not to play optimally. But how good are networks created by such agents? We answer this question for very simple agents. Quite surprisingly, naive greedy play suffices to create remarkably stable networks. Specifically, we show that in the Sum version, where agents attempt to minimize their average distance to all other agents, GE capture Nash equilibria (NE) on trees and that any GE is in 3-approximate NE on general networks. For the latter we also provide a lower bound of $\tfrac{3}{2}$ on the approximation ratio. For the Max version, where agents attempt to minimize their maximum distance, we show that any GE-star is in 2-approximate NE and any GE-tree having larger diameter is in $\tfrac{6}{5}$-approximate NE. Both bounds are tight. We contrast these positive results by providing a linear lower bound on the approximation ratio for the Max version on general networks in GE. This result implies a locality gap of Ω(n) for the metric min-max facility location problem, where n is the number of clients.