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Language evolution in finite populations

Authors: Michael J. Fox; Jeff S. Shamma;

Language evolution in finite populations

Abstract

We study a simple game-theoretical model of language evolution in finite populations. This model is of particular interest due to a surprising recent result for the infinite population case: under replicator dynamics, the population game converges to socially inefficient outcomes from a set of initial conditions with non-zero Lesbegue measure. If finite population models do not exhibit this feature then support is lent to the idea that small population sizes are a key ingredient in the emergence of linguistic coherence. It has been argued elsewhere that evolution supports efficient languages in finite populations using the method of comparing fixation probabilities of single mutant invaders to the inverse of the population size. We instead analyze an alternative generalization of replicator dynamics to finite populations that leads to the emergence of linguistic coherence in an absolute sense. After a long enough period of time, linguistic coherence is observed with arbitrarily high probability as a mutation rate parameter is taken to zero. We also discuss several variations on our model.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
3
Average
Average
Average
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