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Abstract

Models of language evolution have demonstrated how aspects of human language, such as compositionality, can arise in populations of interacting agents. This paper analyzes how languages change as the result of a particular form of interaction: agents learning from one another. We show that, when the learners are rational Bayesian agents, this process of iterated learning converges to the prior distribution over languages assumed by those learners. The rate of convergence is set by the amount of information conveyed by the data seen by each generation; the less informative the data, the faster the process converges to the prior.

@inproceedings{Griffiths05BayesianView,
  author={Thomas L. Griffiths and Michael L. Kalish},
  title={A Bayesian view of language evolution by iterated learning},
  year={2005},
  booktitle={Proceedings of the 27th Annual Conference of the Cognitive Science Society},
  url={http://groups.lis.illinois.edu/amag/langev/paper/Griffiths05BayesianView.html}
}