语言变化和进化模型中的连贯阈值：噪声、动态和交互网络的影响
Coherence thresholds in models of language change and evolution: the effects of noise, dynamics and network of interactions
摘要：
\cite{K_N}提出了一个简单的语言进化模型，其特征是交际功能的回报，以及学习中的错误，这衡量了语言习得的准确性。在平均场近似中，该模型表现出临界的一致性阈值，即在学习过程中需要最小的精度来保持语言的一致性。在这项工作中，我们详细分析了不同的健身为基础的动力学驱动语言的连贯性和网络的相互作用的连贯性阈值的性质的影响，通过进行数值模拟和理论分析的广义复制增变动力学的人口与两种类型的结构：完全连接的网络和定期随机图。我们发现，虽然复制-增变进化模型的阈值是强大的网络结构的接触，相关的健身驱动模型的一致性阈值可能会受到强烈的影响，这个功能。
英文摘要：
A simple model of language evolution, proposed in \cite{K_N}, is characterized by a pay-off in communicative function, and by an error in learning, that measures the accuracy in language acquisition. In the mean field approximation, this model exhibits a critical coherence threshold, i.e. a minimal accuracy in the learning process is required to maintain linguistic coherence. In this work, we analyse in detail the effects of different fitness based dynamics driving linguistic coherence and of the network of interactions on the nature of the coherence threshold, by performing numerical simulations and theoretical analyses of generalized replicator-mutator dynamics in populations with two types of structure: fully connected networks and regular random graphs. We find that although the threshold of the replicator-mutator evolutionary model is robust with respect to the structure of the network of contacts, the coherence threshold of related fitness driven models may be strongly affected by this feature.
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