The main purpose of this article is to argue the merits of ‘population thinking’ in gaining insight into linguistic and, in particular, syntactic change. Population-level thinking and modelling can shed new light on many issues in the study of language acquisition and language change, and leads directly to a precise and useful characterisation of E-language. Something which is lacking in current generative linguistics. Moreover, this way of thinking is fully compatible with the major insights of the latter, and integrates them into a framework in which language variation and change are inherent and inevitable, rather than peripheral and/or accidental, properties of language. I will argue that (E-)languages are best modelled as particular kinds of dynamical systems; namely, complex adaptive systems (where these terms are used in technical senses made precise below). The article both introduces some relevant ideas and techniques from modern evolutionary theory, and from the mathematical and computational study of dynamical systems, and also offers a critique and review of some recent work on syntactic change in this emerging framework, arguing that a useful population model needs to support overlapping generations of language users and learners and to allow quite detailed modelling of differing demographic scenarios. I utilise simple linguistic scenarios based on constituent order changes to illustrate the ideas and techniques clearly. I abstract away from the sociolinguistic detail of the actuation

The emergence and evolution of human language has been the focus of increasing amounts of research activity in recent years. This increasing interest has been coincident with the increased use of computer simulation, particularly using one or more of the methods and techniques of ‘Artificial Life’, to investigate a wide range of evolutionary problems and questions. There is now a significant body of work that uses such computer simulations to investigate the evolution of language. In this thesis a broad review of work on the evolution of language is presented, showing that language evolution occurs as two distinct evolutionary processes. The ability to use language is clearly the result of biological evolution. But the changes that occur over time to all spoken languages can also be viewed as being part of a process of cultural evolution. In this thesis, work using artificial life models to investigate each of these processes is reviewed. A review of the methods and techniques used in artificial life is also presented early in the work. A novel model is developed which is used to explore the conditions necessary for the

We review some of the main theories about how language emerged. We suggest that including the study of the emergence of artificial languages, in simulation settings, allows us to ask a more general question, namely, what are the minimal initial conditions for the emergence of language? This is a very important question from a technological viewpoint, because it is very closely tied to questions of intelligence and autonomy. We identify anticipation as being a key underlying computational principle in the emergence of language. We suggest that this is in fact present implicitly in many of the theories in contention today. Focused simulations that address precise questions are necessary to isolate the roles of the minimal initial conditions for the emergence of language. 1 What is the problem of language emergence? It is very hard to imagine what life would be like without language. Before some point in our evolutionary history, however, our ancestors did not have language.

For the benefit of linguists new to the field of language evolution, the author sets out the issues that need to be distinguished in any research on it. He offers a guided tour of contemporary approaches, including the