Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.

We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The c...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Jianlei Zhang, Chunyan Zhang, Tianguang Chu, Matjaž Perc
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2011
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/9e779df2a7a94b968abe8fc9639d6f27
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:9e779df2a7a94b968abe8fc9639d6f27
record_format	dspace
spelling	oai:doaj.org-article:9e779df2a7a94b968abe8fc9639d6f272021-11-18T06:50:37ZResolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.1932-620310.1371/journal.pone.0021787https://doaj.org/article/9e779df2a7a94b968abe8fc9639d6f272011-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/21760906/pdf/?tool=EBIhttps://doaj.org/toc/1932-6203We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.Jianlei ZhangChunyan ZhangTianguang ChuMatjaž PercPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 6, Iss 7, p e21787 (2011)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Jianlei Zhang Chunyan Zhang Tianguang Chu Matjaž Perc Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.
description	We study the evolution of cooperation among selfish individuals in the stochastic strategy spatial prisoner's dilemma game. We equip players with the particle swarm optimization technique, and find that it may lead to highly cooperative states even if the temptations to defect are strong. The concept of particle swarm optimization was originally introduced within a simple model of social dynamics that can describe the formation of a swarm, i.e., analogous to a swarm of bees searching for a food source. Essentially, particle swarm optimization foresees changes in the velocity profile of each player, such that the best locations are targeted and eventually occupied. In our case, each player keeps track of the highest payoff attained within a local topological neighborhood and its individual highest payoff. Thus, players make use of their own memory that keeps score of the most profitable strategy in previous actions, as well as use of the knowledge gained by the swarm as a whole, to find the best available strategy for themselves and the society. Following extensive simulations of this setup, we find a significant increase in the level of cooperation for a wide range of parameters, and also a full resolution of the prisoner's dilemma. We also demonstrate extreme efficiency of the optimization algorithm when dealing with environments that strongly favor the proliferation of defection, which in turn suggests that swarming could be an important phenomenon by means of which cooperation can be sustained even under highly unfavorable conditions. We thus present an alternative way of understanding the evolution of cooperative behavior and its ubiquitous presence in nature, and we hope that this study will be inspirational for future efforts aimed in this direction.
format	article
author	Jianlei Zhang Chunyan Zhang Tianguang Chu Matjaž Perc
author_facet	Jianlei Zhang Chunyan Zhang Tianguang Chu Matjaž Perc
author_sort	Jianlei Zhang
title	Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.
title_short	Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.
title_full	Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.
title_fullStr	Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.
title_full_unstemmed	Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.
title_sort	resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.
publisher	Public Library of Science (PLoS)
publishDate	2011
url	https://doaj.org/article/9e779df2a7a94b968abe8fc9639d6f27
work_keys_str_mv	AT jianleizhang resolutionofthestochasticstrategyspatialprisonersdilemmabymeansofparticleswarmoptimization AT chunyanzhang resolutionofthestochasticstrategyspatialprisonersdilemmabymeansofparticleswarmoptimization AT tianguangchu resolutionofthestochasticstrategyspatialprisonersdilemmabymeansofparticleswarmoptimization AT matjazperc resolutionofthestochasticstrategyspatialprisonersdilemmabymeansofparticleswarmoptimization
_version_	1718424309494448128

Resolution of the stochastic strategy spatial prisoner's dilemma by means of particle swarm optimization.

Ejemplares similares