There are a number of claims regarding why linguistic com-plexity varies, for example: i) different types of societalstructure (e.g. Wray & Grace, 2007), ii) population size (e.g.Lupyan & Dale, 2010), and iii) the proportion of child vs. adultlearners (e.g. Trudgill, 2011). This simple model of interact-ing agents, capable of learning and innovation, partially sup-ports all these accounts. However, several subtle points arise.Firstly, differences in the capacity or opportunity to learn deter-mine how much complexity can remain stable. Secondly, smallpopulations are susceptible to large amounts of drift and sub-sequent loss, unless innovation is frequent. Conversely, largepopulations remain resilient to change unless there is too muchinnovation, which leads to a collapse in complexity. Next, ifadult learners are prevalent, we can instead expect less sus-tained complexity in large populations. Finally, creolisationdoes not imply simplification in smaller populations.

Languages vary in the way they categorize semantic domains. Incidentally certain semantic systems appear more often than others across the world. Recent research has shown that the attested variability can be explained as the result of languages being a plurality of optimal solutions to efficiency constraints. However, the question of the prevalence remains open. Assuming that languages are a form of culturally transmitted cognitive technology, the Typological Prevalence Hypothesis proposes that the prevalence of a linguistic system is explained by how cognitively natural it is to learn and use. We aim to formalize and test this hypothesis by proposing an information-theoretic measure of communicative and developmental naturalness applied to color typology. While controlling for phylogenetic relatedness, we find that both communicative and developmental naturalness are important predictors of typological prevalence.

Computational model simulations have been very fruitful for gaining insight into how the systematic structure we observe in the world's natural languages could have emerged through cultural evolution. However, these model simulations operate on a toy scale compared to the size of actual human vocabularies, due to the prohibitive computational resource demands that simulations with larger lexicons would pose. Using computational complexity analysis, we show that this is not an implementational artifact, but instead it reflects a deeper theoretical issue: these models are (in their current formulation) computationally intractable. This has important theoretical implications, because it means that there is no way of knowing whether or not the properties and regularities observed for the toy models would scale up. All is not lost however, because awareness of intractability allows us to face the issue of scaling head-on, and can guide the development of our theories.