Effects of compression on language evolution
Artificial Life
The emergence of linguistic structure: an overview of the iterated learning model
Simulating the evolution of language
Natural language from artificial life
Artificial Life
ECAL '99 Proceedings of the 5th European Conference on Advances in Artificial Life
The input for syntactic acquisition: solutions from language change modeling
PMHLA '05 Proceedings of the Workshop on Psychocomputational Models of Human Language Acquisition
Combining data and mathematical models of language change
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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This paper considers the problem of language change. Linguists must explain not only how languages are learned but also how and why they have evolved along certain trajectories and not others. While the language learning problem has focused on the behavior of individuals and how they acquire a particular grammar from a class of grammars ${\cal G}$, here we consider a {\it population\/} of such learners and investigate the emergent, global population characteristics of linguistic communities over several generations. We argue that language change follows logically from specific assumptions about grammatical theories and learning paradigms. In particular, we are able to transform parameterized theories and memoryless acquisition algorithms into grammatical dynamical systems, whose evolution depicts a population''s evolving linguistic composition. We investigate the linguistic and computational consequences of this model, showing that the formalization allows one to ask questions about diachronic that one otherwise could not ask, such as the effect of varying initial conditions on the resulting diachronic trajectories. From a more programmatic perspective, we give an example of how the dynamical system model for language change can serve as a way to distinguish among alternative grammatical theories, introducing a formal diachronic adequacy criterion for linguistic theories.