Language Evolution and Computation Bibliography

Why and how have languages died out? We have devised a mathematical model to help us understand how languages go extinct. We use the model to ask whether language extinction can be prevented in the future and why it may have occurred in the past. A growing number of mathematical models of language dynamics have been developed to study the conditions for language coexistence and death, yet their phenomenological approach compromises their ability to influence language revitalization policy. In contrast, here we model the mechanisms underlying language competition and look at how these mechanisms are influenced by specific language revitalization interventions, namely, private interventions to raise the status of the language and thus promote language learning at home, public interventions to increase the use of the minority language, and explicit teaching of the minority language in schools. Our model reveals that it is possible to preserve a minority language but that continued long-term interventions will likely be necessary. We identify the parameters that determine which interventions work best under certain linguistic and societal circumstances. In this way the efficacy of interventions of various types can be identified and predicted. Although there are qualitative arguments for these parameter values (e.g., the responsiveness of children to learning a language as a function of the proportion of conversations heard in that language, the relative importance of conversations heard in the family and elsewhere, and the amplification of spoken to heard conversations of the high-status language because of the media), extensive quantitative data are lacking in this field. We propose a way to measure these parameters, allowing our model, as well as others models in the field, to be validated.

Attempts to describe language competition and extinction in a mathematical way have enjoyed increased popularity recently. In this paper I review recent modeling approaches and, based on these findings, propose a model of reaction-diffusion type. I analyze the dynamics of interactions of a population with two monolingual groups and a group that is bilingual in these two languages. The results show that demographic factors, such as population growth or population dispersal, play an important role in the competition dynamic. Furthermore, I consider the impact of two strategies for language maintenance: adjusting the status of the endangered language and adjusting the availability of monolingual and bilingual educational resources.

Previous empirical studies of population size and language change have produced equivocal results. We therefore address the question with a new set of lexical data from nearly one half of the world's languages. We first show that relative population sizes of modern languages can be extrapolated to ancestral languages, albeit with diminishing accuracy, up to several thousand years into the past. We then test for an effect of population against the null hypothesis that the ultrametric inequality is satisfied by lexical distances among triples of related languages. The test shows mainly negligible effects of population, the exception being an apparently faster rate of change in the larger of two very closely related variants. A possible explanation for the exception may be the influence on emerging standard (or cross-regional) variants from speakers that shift from different dialects to the standard. Our results strongly indicate that the sizes of speaker populations do not in and of themselves determine rates of language change. Comparison of this empirical finding with previously published computer simulations suggests that the most plausible model for language change is one in which changes propagate at a local level in a type of network where the individuals have different degrees of connectivity.

In this article I provide a review of studies that have modeled interactions between language evolution and demographic processes. The models are classified in terms of three different approaches: analytical modeling, agent-based analytical modeling, and agent-based cognitive modeling. I show that these approaches differ in the complexity of interactions that they can handle and that the agent-based cognitive models allow for the most detailed and realistic simulations. Thus readers are provided with a guideline for selecting which approach to use for a given problem. The analytical models are useful for studying interactions between demography and language evolution in terms of high-level processes; the agent-based analytical models are good for studying such interactions in terms of social dynamics without bothering too much about the cognitive mechanisms of language processing; and the agent-based cognitive models are best suited for the study of the interactions between the complex sociocognitive mechanisms underlying language evolution.