In this talk, along with many of the other speakers at this workshop, I will explore a different kind of evolutionary approach: one in which the status of languages themselves as complex adaptive systems is taken seriously. In this view, our (innately given) language faculty acts as a set of selection pressures on the persistence of linguistic variants over time. Specifically, for a particular I-language (i.e. competence grammar) to survive it must be repeatedly mapped into E-language (i.e. utterances) by speakers, and mapped back into the same I-language by learners.

The effect of this linguistic (as opposed to natural) selection on the structure of languages can be tested by building working models of populations of learners in computational simulations. With these models we can observe the properties of the languages that emerge over a cultural timescale in populations with particular hand coded (i.e. innately given) properties. In other words, in contrast to a standard evolutionary model, we fix the structure of our computational agents are born with; they are not subject to selection pressure and do not themselves evolve. The only thing that changes is the linguistic system that the agents pass on culturally.

The model starts with no initial language and certain fairly minimal assumptions about language production and language learning. However the results are surprisingly complex: after an initial stage where the population converges on a rudimentary, unstructured communication system, languages emerge that structurally resemble human ones in many respects. In these "evolved" systems, length of utterance correlates inversely with frequency; meanings are typically expressed using a recursively compositional syntax; but highly frequent meanings are expressed idiosyncratically.

I will analyse these results in terms of a competition between two aspects of linguistic transmission: a learning bottleneck which favours a topographic mapping between meanings and strings; and speaker laziness which favours a minimal-length code. The take-home message will be that this competition results in language-like systems through properly linguistic as opposed to biological evolution. In other words, to understand the origins of morphosyntax, rather than looking at the way humans have adapted to be better at learning language, we should appreciate the way language has adapted to being better at being passed on by us.

in press. "Learning, Bottlenecks and the Evolution of Recursive Syntax." in Linguistic Evolution through Language Acquisition: Formal and Computational Models, edited by Ted Briscoe. Cambridge University Press.

Postscript: ling.ed.ac.uk/anonftp/pub/staff/kirby/ted.ps.gz PDF: ling.ed.ac.uk/anonftp/pub/staff/kirby/ted.pdf