Abstract
Abstract This paper presents a framework for simulating language change in social networks derived from Social Impact Theory. In this framework, the language learner samples the speech of individuals from right across his speech community, though he may weight their input differentially according to their social position. This conceptualisation is argued to be more realistic than that provided by other models. Computer simulations are used to investigate the effects on language change of different social structures and biases in language acquisition. From the results of these simulations, it is argued that the fundamental engine driving language change is the combination of inherent variation in language acquisition and differences between individuals in local social influence. Functional biases attaching to different linguistic variants influence the direction of language change.
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