Nash equilibria in competitive societies, with applications to facility location, traffic routing and auctions

By Adrian R. Vetta. The 43rd Annual IEEE Symposium on Foundations of Computer Science, 2002. Proceedings., 2002

386 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/bfc8388638f3886f2da9218e724999553c6eee7a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Economics and Econometrics

In Management Science and Operations Research

Citations in the corpus

No-Regret Learning in Bayesian Games (2015, background): “Game/Mechanism (λ, μ) POA Reference Simultaneous First Price Auction with Submodular Bidders (1− 1/e, 1) e e−1 [12] First Price Multi-Unit Auction (1− 1/e, 1) e e−1 [5] First Price Position Auction (1/2, 1) 2 [12] All-Pay Auction (1/2, 1) 2 [12] Greedy Combinatorial Auction with d-complements (1− 1/”
The complexity of pure Nash equilibria (2004, background): “libria? Vetta identifies in [25] the “basic utility games” as another class of games where the Nash dynamics converges, as proven by a general potential function.”
The price of stability for network design with fair cost allocation (2004, background): “Recent theoretical work has framed this type of question in the following general form: how much worse is the solution quality at a Nash equilibrium, relative to the quality at a centrally enforced optimum?1 Questions of this genre have received considerable attention in recent years, for problems i”
Competitive Influence Maximization in Social Networks (2007, methodology): “The above lemma implies that for the last player to commit to a strategy, the greedy algorithm of iteratively adding a node with largest marginal gain is within a factor (1 −1/e) of the best response (see [3]), thus proving Theorem 1. Second, as the expected total number of active nodes at the end i”
Maximizing the spread of influence through a social network (2003, background): “Note, however, that a sampling algorithm to approximately choose the next element may need time that depends on the sizes of the weights.”
Near-optimal network design with selfish agents (2003, background): “The price of anarchy has been studied in a number of games dealing with various networking issues, such as load balancing [14,15,28,35], routing [34,36,37], facility location [39], and flow control [2, 16, 38].”
Revisiting log-linear learning: Asynchrony, completeness and payoff-based implementation (2010, background): “Therefore, from a design perspective, having a learning algorithm that guarantees convergence to the most efficient Nash equilibrium is desirable.”
Network games (2004, neutral): “and facility location [33, 8], network design [10].”
Efficiency loss in a network resource allocation game: the case of elastic supply (2004, background): “Most of the results on price of anarchy have focused on routing [12], traffic networks [13, 14], and network design [15, 16], as well as a special class of submodular games including facility location games [17].”