We show that strategy-independent adaptations of random interaction networks can induce powerful mechanisms, ranging from the Red Queen to group selection, which promote cooperation in evolutionary social dilemmas. These two mechanisms emerge spontaneously as dynamical processes due to deletions and additions of links, which are performed whenever players adopt new strategies and after a certain number of game iterations, respectively. The potency of cooperation promotion, as well as the mechanism responsible for it, can thereby be tuned via a single parameter determining the frequency of link additions. We thus demonstrate that coevolving random networks may evoke an appropriate mechanism for each social dilemma, such that cooperation prevails even in highly unfavorable conditions.
Szolnoki2009 - Resolving social dilemmas on evolving random networks.pdf:Evolutionary Game Theory/Szolnoki2009 - Resolving social dilemmas on evolving random networks.pdf:PDF;Szolnoki2009a - Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks.pdf:Evolutionary Game Theory/Szolnoki2009a - Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks.pdf:PDF
%0 Journal Article
%1 Szolnoki2009a
%A Szolnoki, Attila
%A Perc, Matjaž
%D 2009
%J EPL
%K game-theory networks coevolution graphs adaptive-networks
%P 30007
%R 10.1209/0295-5075/86/30007
%T Resolving social dilemmans on evolving random networks
%V 86
%X We show that strategy-independent adaptations of random interaction networks can induce powerful mechanisms, ranging from the Red Queen to group selection, which promote cooperation in evolutionary social dilemmas. These two mechanisms emerge spontaneously as dynamical processes due to deletions and additions of links, which are performed whenever players adopt new strategies and after a certain number of game iterations, respectively. The potency of cooperation promotion, as well as the mechanism responsible for it, can thereby be tuned via a single parameter determining the frequency of link additions. We thus demonstrate that coevolving random networks may evoke an appropriate mechanism for each social dilemma, such that cooperation prevails even in highly unfavorable conditions.
@article{Szolnoki2009a,
abstract = {We show that strategy-independent adaptations of random interaction networks can induce powerful mechanisms, ranging from the Red Queen to group selection, which promote cooperation in evolutionary social dilemmas. These two mechanisms emerge spontaneously as dynamical processes due to deletions and additions of links, which are performed whenever players adopt new strategies and after a certain number of game iterations, respectively. The potency of cooperation promotion, as well as the mechanism responsible for it, can thereby be tuned via a single parameter determining the frequency of link additions. We thus demonstrate that coevolving random networks may evoke an appropriate mechanism for each social dilemma, such that cooperation prevails even in highly unfavorable conditions.},
added-at = {2011-01-13T13:26:30.000+0100},
author = {Szolnoki, Attila and Perc, Matjaž},
biburl = {https://www.bibsonomy.org/bibtex/275da85de4be9c49de17be8ae31c848fd/rincedd},
doi = {10.1209/0295-5075/86/30007},
file = {Szolnoki2009 - Resolving social dilemmas on evolving random networks.pdf:Evolutionary Game Theory/Szolnoki2009 - Resolving social dilemmas on evolving random networks.pdf:PDF;Szolnoki2009a - Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks.pdf:Evolutionary Game Theory/Szolnoki2009a - Emergence of multilevel selection in the prisoner's dilemma game on coevolving random networks.pdf:PDF},
groups = {public},
interhash = {50446998596930632e0c9701e4ac8229},
intrahash = {75da85de4be9c49de17be8ae31c848fd},
journal = {EPL},
keywords = {game-theory networks coevolution graphs adaptive-networks},
pages = 30007,
timestamp = {2011-04-05T11:56:00.000+0200},
title = {Resolving social dilemmans on evolving random networks},
username = {rincedd},
volume = 86,
year = 2009
}