Devil in the Details: Analysis of a Coevolutionary Model of Language Evolution via Relaxation of Selection

Computational modeling is an important tool in the study of language evolution. It is not only used to test hypotheses, but also as a source of data on difficult to observe evolution-ary dynamics. This makes it particularly important to distin-guish the emergent behaviors of evolutionary systems being studied, from the behaviors of specific models. In this paper we provide an in-depth analysis of one recent model of lin-guistic bio-cultural coevolution (Yamauchi and Hashimoto, 2010) and show that several of its reported behaviors are arti-facts produced by the model’s design and parameter settings. Specifically, we show that the model’s population size setting and agent “geography ” place strong limits on both cultural and biological diversity in the model. These limits interact with the model’s learning mechanism and result in a number of semi-stable attractor states. We argue that it is the proper-ties of these attractors that account for the long run behavior of the model, directly conflicting with the analysis given in the original paper. Our results are confirmed by experiments altering the model’s population size parameter which result in a qualitative change in the observed model behavior