Although languages around the world display an overwhelming variety in ways to describe colours, most of the research in the domain of colour has focussed on the use of single colour terms. This approach has allowed researchers in a wide range of fields to tackle interesting questions, such as the extent to which colour categories are innate or learned. In the field of artificial language evolution, the focus on single colour terms has enabled researchers to build computational models in which populations of linguistic agents can construct and coordinate their own colour category system so that they become successful in communication.

A few descriptive studies report on describing colours beyond the restriction of using a single colour term. The results of these studies seem conclusive: only a small minority (around 15%) of all colour samples would be described using a single colour term. Most samples are described using more elaborate expressions, for example by using modifiers or combinations of colour terms.

In this book, I show how the current models in artificial language evolution can be extended to allow for richer descriptions of colour samples. In order to do so, I deploy two powerful formalisms that have been developed to support this kind of experiments: Incremental Recruitment Language (IRL) to represent the semantics, or meaning, of linguistic utterances and Fluid Construction Grammar (FCG) to transform these meanings into linguistic utterances and back.