Language Change and Social Networks (Communications in Computational Physics, 2008)
Social networks play an important role in determining the dynamics andoutcome of language change. Early empirical studies only examine small-scale local social networks, and focus on the relationship between the individual speakers' linguistic behaviors and their characteristics in the network. In contrast, computermodels can provide an efficient tool to consider large-scale networks with differentstructures and discuss the long-term effect of individuals' learning and interaction onlanguage change. This paper presents an agent-based computer model which simulates language change as a process of innovation diffusion, to address the thresholdproblem of language change. In the model, the population is implemented as a network of agents with age differences and different learning abilities, and the populationis changing, with new agents born periodically to replace old ones. Four typical typesof networks and their effect on the diffusion dynamics are examined. When the functional bias is sufficiently high, innovations always diffuse to the whole population ina linear manner in regular and small-world networks, but diffuse quickly in a sharpS-curve in random and scale-free networks. The success rate of diffusion is higher inregular and small-world networks than in random and scale-free networks. In addition, the model shows that as long as the population contains a small number ofstatistical learners who can learn and use both linguistic variants statistically according to the impact of these variants in the input, there is a very high probability forlinguistic innovations with only small functional advantage to overcome the thresholdof diffusion.