2010
DOI: 10.1088/1367-2630/12/10/103034
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Co-evolution of strategies and update rules in the prisoner's dilemma game on complex networks

Abstract: In this work we study a weak Prisoner's Dilemma game in which both strategies and update rules are subjected to evolutionary pressure. Interactions among agents are specified by complex topologies, and we consider both homogeneous and heterogeneous situations. We consider deterministic and stochastic update rules for the strategies, which in turn may consider single links or full context when selecting agents to copy from. Our results indicate that the co-evolutionary process preserves heterogeneous networks a… Show more

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Cited by 61 publications
(47 citation statements)
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“…This, however, is understandable, since the cooperation-supporting influential players emerge only for a short period of time, but on average the overall positive effect in the stationary state is still clearly there. To conclude, it is worth pointing out that time-dependent perceptions of social dilemmas open the path toward coevolutionary models, as studied previously in the realm of evolutionary games [34,[65][66][67][68][69], and they also invite the consideration of the importance of time scales [70] in evolutionary multigames.…”
Section: Resultsmentioning
confidence: 75%
“…This, however, is understandable, since the cooperation-supporting influential players emerge only for a short period of time, but on average the overall positive effect in the stationary state is still clearly there. To conclude, it is worth pointing out that time-dependent perceptions of social dilemmas open the path toward coevolutionary models, as studied previously in the realm of evolutionary games [34,[65][66][67][68][69], and they also invite the consideration of the importance of time scales [70] in evolutionary multigames.…”
Section: Resultsmentioning
confidence: 75%
“…To better understand the pattern resulting from individual strategic interactions, it is thus necessary to incorporate heterogeneity into update rules and try to deal with the rising complexity. Heterogeneity in update rules can be modeled by assigning different individuals different types of update rules [43,44] or the same type of update rule but realized by different (update) functions [45][46][47][48]. For example, the pioneering work by Kirchkamp [45] explores the evolution of update rules characterized by functions with three parameters.…”
Section: Introductionmentioning
confidence: 99%
“…Classic examples reviewed in [38] include kin selection [39], direct and indirect reciprocity [40,41], network reciprocity [42], as well as group selection [43]. Diffusion and mobility have also been studied prominently [44][45][46][47], as were various coevolutionary models [8], involving network topology, noise, and aspiration [48][49][50][51][52][53][54][55][56], to name but a few examples. In particular, it was found that heterogeneities in the system, sometimes also referred to as diversity [57], independent of its origin, can significantly enhance cooperation levels in social dilemmas [58][59][60][61][62][63][64][65][66].…”
Section: Introductionmentioning
confidence: 99%