A unified computer model for internal and external constraints in
language evolution

Christophe Coupé

Laboratoire Dynamique du Langage, CNRS a Université Lyon 2, Lyon, France

Christophe.Coupe@ish-lyon.cnrs.fr

During the last decade, an increasing number of computational approaches
have been developed to study human language, which have employed notions
such as dynamic complex systems or self-organization.  The emergence of
linguistic structures has especially received a great deal of attention;
whether they focus on the lexicon (Steels 1996), phonetic inventories
(de Boer 1999) or sets of syntactic rules (Batali 1997, Kirby 2002,
Steels 2000), all studies share the goal of explaining how a coherent
global system can emerge from simple local interactions.  In parallel, a
few computer studies have addressed the role of social parameters in
language evolution, such as Nettle’s study on social factors (1999),
application of Steels’ naming games to language contact (Steels 1997,
Marsico & al 2000), or Niyogi & Berwick’(1997)s work on the competition
of linguistic variants across generations.  Such topics can benefit from
the advantages of computer models and remain puzzling to most linguists:
does a larger population evolve faster or slower? What parameters affect
linguistic diversity? Is it possible to refine the glottochronology with
additional social settings?

Because of a legitimate difference of focus, models of the emergence of
language are usually limited when it comes to study social factors, as
models on the evolution of languages often reduce language to an
extremely simplified system.  While these limitations are helpful to
delimitate the role of each parameter, additional phenomena might emerge
from the interactions of external (social) and internal (cognitive,
production/perception) constraints. 

To better investigate such interactions, we propose a model which offers
a unified mathematical framework for both types of constraints weighting
on a set of linguistic systems, also called agents, which can correspond
to either idiolects or communal languages.  To this end, we rely on the
two key notions of fitness landscape and social network.  Internal
constraints are defined by a fitness landscape on which linguistic
systems draw evolutionary trajectories.  All possible states are
predefined and no emergence occurs, but the shape of the landscape can
be computed from a large variety of situations.  Furthermore, a simple
model of social network (Milroy 1993) leads to additional attractions or
repulsions between agents, following intuitive statements such as
Bloomsfield’s proposal about the convergence of idiolects of closely and
friendly related individuals (Labov 2002).  Such a general social model
allows investigating a large number of situations, from uniform
populations of various sizes, to complex communities with more or less
connected sub-networks. 

For each agent, the direction of change is determined by the constraints
derived from the local slope of the fitness landscape and his social
environment.  Random draws following probabilistic Gaussian
distributions modelling the former constraints lead to the probabilistic
evolution of linguistic systems.  The global diversity of the population
of agents and the mean rate of change can be measured from the
trajectories of the systems in the landscape. 

We present the conclusions of experiments which first consider the two
types of constraints independently, and then combine them to evaluate
their respective influence.  Among others, Nettle’s results are
reproduced, and the two types of constraints appear to be operationally
independent.  We also try to link various topologies of the social
network with hypotheses about the prehistory of languages and its
characteristic (diversity, rate of evolution).  Further enhancements are
finally reported, such as the on-going extraction of a fitness landscape
from the UPSID database of phonological inventories in world’s languages
(Maddieson 1984). 

Batali, J.  (1997).  Computational Simulations of the Emergence of
Grammar.  Cambridge University Press, Oxford.  p.  405-426

De Boer, B.  (1999).  Self-organization in vowel systems.  Phd thesis,
Vrije Universiteit Brussels. 

Kirby, S.  (2001).  Spontaneous Evolution of Linguistic Structure: an
iterated learning model of the emergence of regularity and irregularity. 
Special Issue of IEEE Transactions on Evolutionary Computation on
Evolutionary Computation and Cognitive Science 5(2):102-10. 

Labov, W.  (2002).  Driving forces in linguistic change.  International
Conference on Korean Linguistics, Seoul National University. 

Maddieson, I.  1984.  Patterns of sound.  Cambridge: Cambridge
University Press. 

Marsico, E., Coupe, C., & Pellegrino, F.  (2000).  Evaluating the
influence of language contact on lexical changes.  Proceedings of the
third Conference on the Evolution of Language, p.  154-155. 

Milroy, J.  (1992).  Linguistic Variation and Change.  Blackwell,
Oxford. 

Nettle, D.  (1999).  Is the rate of linguistic change constant? Lingua
108:119-136. 

Niyogi, P.  & Berwick, R.  C.  (1997).  A Dynamical Systems Model for
Language Change.  Complex Systems 11:161-204. 

Steels, L.  (1996).  Self-organising vocabularies.  MIT Press,
Cambridge, MA. 

Steels, L.  (1997a).  Language Learning and Language Contact.  In
Daelemans, W.  (ed.), Proceedings of the workshop on Empirical
Approaches to Language Aquisition, p.  11-24. 

Steels, L.  (2000).  A brain for language.  In The Ecological.  Brain. 
Sony CSL - Paris - 2000 Symposium.