Electronics and Computer Science, University of Southampton, Southampton, Hampshire, United Kingdom.
PLoS One. 2022 Aug 30;17(8):e0273608. doi: 10.1371/journal.pone.0273608. eCollection 2022.
We propose an evolutionary model for the emergence of shared linguistic convention in a population of agents whose social structure is modelled by complex networks. Through agent-based simulations, we show a process of convergence towards a common language, and explore how the topology of the underlying networks affects its dynamics. We find that small-world effects act to speed up convergence, but observe no effect of topology on the communicative efficiency of common languages. We further explore differences in agent learning, discriminating between scenarios in which new agents learn from their parents (vertical transmission) versus scenarios in which they learn from their neighbors (oblique transmission), finding that vertical transmission results in faster convergence and generally higher communicability. Optimal languages can be formed when parental learning is dominant, but a small amount of neighbor learning is included. As a last point, we illustrate an exclusion effect leading to core-periphery networks in an adaptive networks setting when agents attempt to reconnect towards better communicators in the population.
我们提出了一个在由复杂网络建模的主体群体中出现共享语言惯例的进化模型。通过基于主体的模拟，我们展示了一个朝着共同语言趋同的过程，并探讨了基础网络的拓扑结构如何影响其动态。我们发现小世界效应有助于加快收敛速度，但观察到拓扑结构对通用语言的通信效率没有影响。我们进一步探讨了主体学习的差异，区分了新主体从父母那里学习（垂直传播）与从邻居那里学习（斜向传播）的情景，发现垂直传播导致更快的收敛，并且通常具有更高的可传播性。当父母学习占主导地位时，可以形成最佳语言，但也包含少量邻居学习。最后，我们说明了在主体试图在群体中重新连接到更好的沟通者时，在自适应网络设置中导致核心-外围网络的排除效应。
