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Bart DeVylder and Karl Tuyls. How to Reach Linguistic Consensus: A Proof of Convergence for the Naming Game. The Journal of Theoretical Biology, 242(4):818–831, 2006.
In this paper we introduce a mathematical model of naming games. Naming gameshave been widely used within research on the origins and evolution of language.Despite the many interesting empirical results these studies have produced, mostof this research lacks a formal elucidating theory. In this paper we show how apopulation of agents can reach linguistic consensus, i.e. learn to use one commonlanguage to communicate with one another. Our approach differs from existingformal work in two important ways: One, we relax the too strong assumption that anagent samples infinitely often during each time interval. This assumption is usuallymade to guarantee convergence of an empirical learning process to a deterministicdynamical system. Two, we provide a proof that under these new realistic conditions,our model converges to a common language for the entire population of agents.Finally the model is experimentally validated.
@ARTICLE{DevJTB06,
author = {Bart DeVylder and Karl Tuyls},
title = {How to Reach Linguistic Consensus: A Proof of Convergence for the
Naming Game},
journal = {The Journal of Theoretical Biology},
year = {2006},
volume = {242(4)},
pages = {818--831},
abstract = {In this paper we introduce a mathematical model of naming games. Naming games
have been widely used within research on the origins and evolution of language.
Despite the many interesting empirical results these studies have produced, most
of this research lacks a formal elucidating theory. In this paper we show how a
population of agents can reach linguistic consensus, i.e. learn to use one common
language to communicate with one another. Our approach differs from existing
formal work in two important ways: One, we relax the too strong assumption that an
agent samples infinitely often during each time interval. This assumption is usually
made to guarantee convergence of an empirical learning process to a deterministic
dynamical system. Two, we provide a proof that under these new realistic conditions,
our model converges to a common language for the entire population of agents.
Finally the model is experimentally validated.},
bib2html_pubtype = {Journal},
bib2html_rescat = {Multi-Agent Learning},
owner = {K.Tuyls},
timestamp = {2006.11.23},
}
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