Keeping up with the Neighbours - An Agent-Based Simulation of the Divergence of the Standard Dutch Pronunciations in the Netherlands and Belgium

Abstract

While the Netherlandic standard Dutch pronunciation norm around 1930 was still very much like the Belgian norm, it shifted considerably in the course of the 20th century (Van de Velde 1996, Van de Velde et al. 2010). In Belgium, no such evolution occurred, which caused the pronunciation of both language varieties to diverge. As of yet, there is no conclusive evidence as to why this divergence has happened. Because there is not enough data to investigate the divergence empirically, it is examined using an agent-based simulation model in Python. Though we cannot ‘prove’ that the mechanisms described in the theories from the literature actually happened in reality, we can test their plausibility by checking whether the effects described in the theories also appear in our model which attempts to mimic real-world circumstances. Four research questions based on theories found in the literature are tested: 1. Can a reduced contact between speakers from the Netherlands and Belgium result in a divergence between the standard pronunciations of both countries in the model? 2. Can an increased pace of language change in Dutch speakers cause a divergence between the standard pronunciations of the Netherlands and Belgium in the model? 3. Can we relate increased ethnocentrism in Belgian speakers to less adoption of Netherlandic innovations or even divergence in the model? 4. Can an increased media influence amplify the existing tendencies for language change (acceleration or inhibition) in Belgium in the model? The results show that a lack of contact between both countries can indeed lead to divergence in the model, but only if abroad travel is at least 5000 times less likely than domestic travel. The pace of language change in the Netherlands does not have a sizeable impact on convergence or divergence tendencies in Belgium in the model. High values for ethnocentrism in Belgian agents are able to lead to divergence in the model, as long as these high values are shared by the entire population. If ethnocentrism decreases along with how close agents live to the border, it has little effect. Media receptiveness in agents always kickstarts convergence in the model and it accelerates this convergence as well. Since media influence is implemented as a powerful force in the simulation, this result must be interpreted from the viewpoint of media having a sizeable impact on language change.