COMPLEXITY. Human languages are massively complex, but, paradoxically, children have no worries in mastering them. Languages thus seem to be at a level of `masterable complexity'. Recent work in the theory of complexity has shed light on the region of the phase change between orderly and chaotic systems, and shown how many natural systems tend to evolve toward a level of complexity just at or below `the edge of chaos'.

INTERCONNECTEDNESS. `Une langue est un systeme ou tout se tient'. This maxim of Linguistics could be translated as `a language is a system in which everything depends on everything else'. Until recently there has been no analytical machinery for understanding systems in which everything is interdependent. But now, with the advent of network models, we are beginning to see new ways of conceiving the properties of such systems.

STABILITY. Languages (and speaker's judgements about examples from them) are very stable, but subject to small changes over time.

DIVERSITY. There are many human languages. Putting aside differences in vocabulary, there are probably no two languages out of the 6000 extant languages with exactly the same grammatical system. Of course, the number of possible humanly-learnable grammatical systems must be far greater.

UNDERDETERMINEDNESS. The complete knowledge of a language reached by an adult is only partially determined by experiential input.

Specifically, in the research reported here, a language is modelled as an attractor of a Boolean net. (Groups of) nodes in the net might be thought of as linguistic principles or parameters as posited by Chomskyan theory of the 1980s. According to this theory, the task of the language learner is to set parameters to appropriate values, on the basis of very limited experience of the language in use. The setting of one parameter can have a complex effect on the settings of other parameters. A random Boolean net is generated and run to find an attractor. A state from this attractor is degraded, to represent the degenerate input of language to the language learner, and this degraded state is then input to a net with the same connectiviuty and activation functions as the original net, to see whether it converges on the same attractor as the original. In practice, many nets fail to converge on the original attractor, and degenerate into attractors representing complete uncertainty. Other nets settle at intermediate levels of uncertainty. And some nets manage to overcome the incompleteness of input and converge on attractors identical to that from which the original inputs were (de)generated. Finally, a genetic algorithm is used to select a population of such successful nets, and the properties of the connections and activation functions of these successfully evolved nets are examined.