Logistic Patterns of Language Change

Niyogi and Berwick have developed a deterministic dynamical model of (E-)language change from which they analytically derive logistic, S-shaped spread of a linguistic variant through a speech community given certain assumptions about the language learning algorithm, the linguistic environment, and so forth. I will demonstrate that the same assumptions embedded in a stochastic model of (E-)language change lead to dramatically diierent predictions. I will go on to argue that stochastic models are more appropriate and can support greater demographic and (psycho)linguistic realism, leading to more insightful accounts of the growth rates of attested changes. I will also argue that properties of the language learning algorithm are critical for realistic modelling of (E-)languages as dynamical systems.