Agent-based modelling is a new analytical method for social sciences. It enables one to build models where individual entities and their interactions are directly represented. As compared to variable-based approaches or system-based approaches, agent-based simulation offers the possibility of modelling individual heterogeneity, representing agents’ decision rules explicitly, and situating agents in a geographical or another type of space. Basically, it allows one to represent multiple scales of analysis in a natural and efficient way.
3.1.1 Literature on Agent-based Simulation
It was started by Hurford to model the biological and cultural evolution of language. Earlier work in this area sought to explain the role of interaction and negotiation, or biases of learners in shaping communication systems, focusing mainly on the conditions under which communicatively optimal, socially learnt communication systems would emerge. Thereafter, researchers tried to find out how linguistic structure can arise from iterated learning. Emphasis was given on the role of bottleneck learning, which was thought to be the driving force behind the evolution of structure, since language learners must try to learn an infinitely expressive linguistic system on the basis of a small set of linguistic data.
A major finding is that compositional languages emerge from unstructured languages due to repeated transmission through the learning bottleneck – language structure appears as an adaptive response by language per se to the problem of being transmitted through a narrow bottleneck, since the presence of compositional rules enables a learner to infer from a small sample rules underpinning the whole language.
There is another model that represents the emergence of systematicity in phonological systems through communicative interaction and iterated learning. One example would be De Boer who looked at the cultural evolution of vowel systems, showing that the universal features of the organisation of all the vowels in the world can arise through repeated interaction between simulated agents under certain reasonable articulatory and perceptual constraints.
These variety of agent-based models give evidence to key design features of language being emerged from iterated learning.