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Coevolutionary, coexisting learning and teaching agents model for prisoner's dilemma games enhancing cooperation with assortative heterogeneous networks

机译:囚徒困境游戏的共进化,共存的学与教模型,增强了与各种异构网络的合作

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Unlike other natural network systems, assortativity can be observed in most human social networks, although it has been reported that a social dilemma situation represented by the prisoner's dilemma favors dissortativity to enhance cooperation. We established a new coevolutionary model for both agents' strategy and network topology, where teaching and learning agents coexist. Remarkably, this model enables agents' enhancing cooperation more than a learners-only model on a time-frozen scale-free network and produces an underlying assortative network with a fair degree of power-law distribution. The model may imply how and why assortative networks are adaptive in human society.
机译:与其他自然网络系统不同,在大多数人类社交网络中都可以观察到分类性,尽管据报道,以囚徒困境为代表的社会困境有利于进行分类以加强合作。我们建立了针对代理策略和网络拓扑的新的协同进化模型,在该模型中,教学代理与学习代理共存。值得注意的是,该模型使代理可以在无时间限制的无标度网络上增强学习者模型以外的协作能力,并产生具有一定程度的幂律分布的底层分类网络。该模型可能暗示着分类网络如何以及为什么在人类社会中具有适应性。

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