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Computing Nash equilibria through computational intelligence methods

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Computing Nash equilibria through computational intelligence methods. / Pavlidis, Nicos; Parsopoulos, Kostantinos E.; Vrahatis, Michael N.
In: Journal of Computational and Applied Mathematics, Vol. 175, No. 1, 03.2005, p. 113-136.

Research output: Contribution to Journal/Magazine › Journal article › peer-review

Harvard

Pavlidis, N, Parsopoulos, KE & Vrahatis, MN 2005, 'Computing Nash equilibria through computational intelligence methods', Journal of Computational and Applied Mathematics, vol. 175, no. 1, pp. 113-136. https://doi.org/10.1016/j.cam.2004.06.005

APA

Pavlidis, N., Parsopoulos, K. E., & Vrahatis, M. N. (2005). Computing Nash equilibria through computational intelligence methods. Journal of Computational and Applied Mathematics, 175(1), 113-136. https://doi.org/10.1016/j.cam.2004.06.005

Vancouver

Pavlidis N, Parsopoulos KE, Vrahatis MN. Computing Nash equilibria through computational intelligence methods. Journal of Computational and Applied Mathematics. 2005 Mar;175(1):113-136. doi: 10.1016/j.cam.2004.06.005

Author

Pavlidis, Nicos ; Parsopoulos, Kostantinos E. ; Vrahatis, Michael N. / Computing Nash equilibria through computational intelligence methods. In: Journal of Computational and Applied Mathematics. 2005 ; Vol. 175, No. 1. pp. 113-136.

Bibtex

@article{70a80212315c4f16b71140dfb6737a01,
title = "Computing Nash equilibria through computational intelligence methods",
abstract = "Nash equilibrium constitutes a central solution concept in game theory. The task of detecting the Nash equilibria of a finite strategic game remains a challenging problem up-to-date. This paper investigates the effectiveness of three computational intelligence techniques, namely, covariance matrix adaptation evolution strategies, particle swarm optimization, as well as, differential evolution, to compute Nash equilibria of finite strategic games, as global minima of a real-valued, nonnegative function. An issue of particular interest is to detect more than one Nash equilibria of a game. The performance of the considered computational intelligence methods on this problem is investigated using multistart and deflection.",
keywords = "Nash equilibria, Evolutionary algorithms Differential evolution; Evolution strategies, Particle swarm optimization , Differential evolution , Evolution strategies",
author = "Nicos Pavlidis and Parsopoulos, {Kostantinos E.} and Vrahatis, {Michael N.}",
year = "2005",
month = mar,
doi = "10.1016/j.cam.2004.06.005",
language = "English",
volume = "175",
pages = "113--136",
journal = "Journal of Computational and Applied Mathematics",
issn = "0377-0427",
publisher = "Elsevier",
number = "1",

}

RIS

TY - JOUR

T1 - Computing Nash equilibria through computational intelligence methods

AU - Pavlidis, Nicos

AU - Parsopoulos, Kostantinos E.

AU - Vrahatis, Michael N.

PY - 2005/3

Y1 - 2005/3

N2 - Nash equilibrium constitutes a central solution concept in game theory. The task of detecting the Nash equilibria of a finite strategic game remains a challenging problem up-to-date. This paper investigates the effectiveness of three computational intelligence techniques, namely, covariance matrix adaptation evolution strategies, particle swarm optimization, as well as, differential evolution, to compute Nash equilibria of finite strategic games, as global minima of a real-valued, nonnegative function. An issue of particular interest is to detect more than one Nash equilibria of a game. The performance of the considered computational intelligence methods on this problem is investigated using multistart and deflection.

AB - Nash equilibrium constitutes a central solution concept in game theory. The task of detecting the Nash equilibria of a finite strategic game remains a challenging problem up-to-date. This paper investigates the effectiveness of three computational intelligence techniques, namely, covariance matrix adaptation evolution strategies, particle swarm optimization, as well as, differential evolution, to compute Nash equilibria of finite strategic games, as global minima of a real-valued, nonnegative function. An issue of particular interest is to detect more than one Nash equilibria of a game. The performance of the considered computational intelligence methods on this problem is investigated using multistart and deflection.

KW - Nash equilibria

KW - Evolutionary algorithms Differential evolution; Evolution strategies

KW - Particle swarm optimization

KW - Differential evolution

KW - Evolution strategies

U2 - 10.1016/j.cam.2004.06.005

DO - 10.1016/j.cam.2004.06.005

M3 - Journal article

VL - 175

SP - 113

EP - 136

JO - Journal of Computational and Applied Mathematics

JF - Journal of Computational and Applied Mathematics

SN - 0377-0427

IS - 1

ER -