73 research outputs found
Sharp transition towards shared vocabularies in multi-agent systems
What processes can explain how very large populations are able to converge on
the use of a particular word or grammatical construction without global
coordination? Answering this question helps to understand why new language
constructs usually propagate along an S-shaped curve with a rather sudden
transition towards global agreement. It also helps to analyze and design new
technologies that support or orchestrate self-organizing communication systems,
such as recent social tagging systems for the web. The article introduces and
studies a microscopic model of communicating autonomous agents performing
language games without any central control. We show that the system undergoes a
disorder/order transition, going trough a sharp symmetry breaking process to
reach a shared set of conventions. Before the transition, the system builds up
non-trivial scale-invariant correlations, for instance in the distribution of
competing synonyms, which display a Zipf-like law. These correlations make the
system ready for the transition towards shared conventions, which, observed on
the time-scale of collective behaviors, becomes sharper and sharper with system
size. This surprising result not only explains why human language can scale up
to very large populations but also suggests ways to optimize artificial
semiotic dynamics.Comment: 12 pages, 4 figure
Modeling the emergence of a new language: Naming Game with hybridization
In recent times, the research field of language dynamics has focused on the
investigation of language evolution, dividing the work in three evolutive
steps, according to the level of complexity: lexicon, categories and grammar.
The Naming Game is a simple model capable of accounting for the emergence of a
lexicon, intended as the set of words through which objects are named. We
introduce a stochastic modification of the Naming Game model with the aim of
characterizing the emergence of a new language as the result of the interaction
of agents. We fix the initial phase by splitting the population in two sets
speaking either language A or B. Whenever the result of the interaction of two
individuals results in an agent able to speak both A and B, we introduce a
finite probability that this state turns into a new idiom C, so to mimic a sort
of hybridization process. We study the system in the space of parameters
defining the interaction, and show that the proposed model displays a rich
variety of behaviours, despite the simple mean field topology of interactions.Comment: 12 pages, 10 figures, presented at IWSOS 2013 Palma de Mallorca, the
final publication will be available at LNCS http://www.springer.com/lnc
Modeling the emergence of universality in color naming patterns
The empirical evidence that human color categorization exhibits some
universal patterns beyond superficial discrepancies across different cultures
is a major breakthrough in cognitive science. As observed in the World Color
Survey (WCS), indeed, any two groups of individuals develop quite different
categorization patterns, but some universal properties can be identified by a
statistical analysis over a large number of populations. Here, we reproduce the
WCS in a numerical model in which different populations develop independently
their own categorization systems by playing elementary language games. We find
that a simple perceptual constraint shared by all humans, namely the human Just
Noticeable Difference (JND), is sufficient to trigger the emergence of
universal patterns that unconstrained cultural interaction fails to produce. We
test the results of our experiment against real data by performing the same
statistical analysis proposed to quantify the universal tendencies shown in the
WCS [Kay P and Regier T. (2003) Proc. Natl. Acad. Sci. USA 100: 9085-9089], and
obtain an excellent quantitative agreement. This work confirms that synthetic
modeling has nowadays reached the maturity to contribute significantly to the
ongoing debate in cognitive science.Comment: Supplementery Information available here
http://www.pnas.org/content/107/6/2403/suppl/DCSupplementa
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