The iterated learning model is an agent-based model of language evolution notable for demonstrating the emergence of compositional language. In its original form, it modelled language evolution along a single chain of teacher-pupil interactions; here we modify the model to allow more complex patterns of communication within a population and use the extended model to quantify the effect of within-community and between-community communication frequency on language development. We find that a small amount of between-community communication can lead to population-wide language convergence but that this global language amalgamation is more difficult to achieve when communities are spatially embedded.

翻译：迭代学习模型是一种基于智能体的语言演化模型，以其展现出组合语言的涌现而著称。在原始形式中，该模型沿单一师徒互动链模拟语言演化；本文对模型进行改进，允许群体内部更复杂的通信模式，并利用扩展模型量化社群内部与社群间的通信频率对语言发展的影响。我们发现，少量社群间通信可导致群体层面的语言趋同，但当社群呈现空间嵌入结构时，这种全局性语言融合更难以实现。