Abstract For many adaptive complex systems information about the environment is not simply recorded in a look-up table, but is rather encoded in a theory, schema, or model, which compresses information. The grammar of a language can be viewed as such a schema or theory. In a prior study [Teal et al., 1999] we proposed several conjectures about the learning and evolution of language that should follow from these observations: (C1) compression aids in generalization; (C2) compression occurs more easily in a “smooth”, as opposed to a “rugged”, problem space; and (C3) constraints from compression make it likely that natural languages evolve towards smooth string spaces. This previous work found general, if not complete support for these three conjectures. Here we build on that study to clarify the relationship between Minimum Description Length (MDL) and error in our model and examine evolution of certain languages in more detail. Our results suggest a fourth conjecture: that all else being equal, (C4) more complex languages change more rapidly during evolution.
Linguistic evolution driven by network heterogeneity and the Turing mechanism