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.

The paper proposes a number of models to examine through what mechanisms a population of autonomous agents could arrive at a repertoire of perceptually grounded categories that is sufficiently shared to allow successful communication. The models are inspired by the main approaches to human categorisation being discussed in the literature: nativism, empiricism, and culturalism. Colour is taken as a case study. Although the paper takes no stance on which position is to be accepted as final truth with respect to human categorisation and naming, it points to theoretical constraints that make each position more or less likely and contains clear suggestions on what the best engineering solution would be. Specifically, it argues that the collective choice of a shared repertoire must integrate multiple constraints, including constraints coming from communication.

Language is a shared set of conventions for mapping meanings to utterances. This paper explores self-organization as the primary mechanism for the formation of a vocabulary. It reports on a computational experiment in which a group of distributed agents develop ways to identify each other using names or spatial descriptions. It is also shown that the proposed mechanism copes with the acquisition of an existing vocabulary by new agents entering the community and with an expansion of the set of meanings.

In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPS-equipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geolocalised measurements and further personal annotation to produce a collective noise map. Acknowledgements This work was partially supported by the EU under contract IST-34721 (TAGora). The TAGora project is funded by the Future and Emerging Technologies program (IST-FET) of the European Commission. Matthias Stevens is a research assistant of the Fund for Scientific Research, Flanders (Aspirant van het Fonds Wetenschappelijk OnderzoekVlaanderen). Links Abstract:In this paper we present a new approach for the assessment of noise pollution involving the general public. The goal of this project is to turn GPSequipped mobile phones into noise sensors that enable citizens to measure their personal exposure to noise in their everyday environment. Thus each user can contribute by sharing their geo-localised measurements and further personal annotation to produce a collective noise map.

Human language is the key evolutionary innovation that makes humans different from other species. And yet, the fabric of language is tangled and all levels of description (from semantics to syntax) involve multiple layers of complexity. Recent work indicates that the global traits displayed by such levels can be analyzed in terms of networks of connected words. Here, we review the state of the art on language webs and their potential relevance to cognitive science. The emergence of syntax through language acquisition is used as a case study to illustrate how the approach can shed light into relevant questions concerning language organization and its evolution.

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This paper surveys work on the computational modeling of the origins and evolution of language. The main approaches are described and some example experiments from the domains of the evolution of communication, phonetics, lexicon formation, and syntax are discussed.

Language emergence and evolution has recently gained growing attention through multiagent models and mathematical frameworks to study their behavior. Here we investigate further the Naming Game, a model able to account for the emergence of a shared vocabulary of form-meaning associations through social/cultural learning. Due to the simplicity of both the structure of the agents and their interaction rules, the dynamics of this model can be analyzed in great detail using numerical simulations and analytical arguments. This paper first reviews some existing results and then presents a new overall understanding. 15:56 WSPC/INSTRUCTION FILE ng˙long˙ijmpc˙last3 2 Andrea Baronchelli, Vittorio Loreto and Luc Steels telepathy? The problem has been addressed by several disciplines, but it is only in the last decade that there has been a growing effort to tackle it scientifically using multi-agent models and mathematical approaches (cfr. 1,2,3 for a review). Initially these models focused on the emergence of a shared vocabulary, but increasingly attempts are made to tackle grammar 1,4,5,6,7 .The proposed models can be classified as defending a sociobiological or a sociocultural explanation. The sociobiological approach 8 , which includes the evolutionary language game 1 , is based on the assumption that successful communicators, enjoying a selective advantage, are more likely to reproduce than worse communicators. If communication strategies are innate, then more successful strategies will displace rivals. The term strategy acquires its precise meaning in the context of a particular model. For instance, it can be a strategy for acquiring the lexicon of a language, i.e., a function from samplings of observed behaviors to acquired communicative behavior patterns 8,9,10 , or it can simply coincide with the lexicon of the parents 1 or with some strong disposition to acquire a particular kind of syntax, usually called innate Universal Grammar 11 .In this paper we discuss a model, first proposed in 12 , that belongs to the sociocultural family 13,14,15 . Here, good strategies do not necessarily provide higher reproductive success, but only higher communicative success and greater expressive power, and hence greater success in reaching cooperative goals, with less effort. Agents select better strategies exploiting cultural choices, feedback from communication, and a sense of effort. Agents have not only the ability to acquire an existing system but to expand their rules to deal with new communicative challenges and to adjust their rules based on observing the behavior of others. Global coordination emerges over cultural timescales, and language is seen as an evolving and self-organized system 16 . While the sociobiological approach emphasizes language transmission following a vertical, genetic or generational line, the sociocultural approach emphasizes peer-to-peer interaction 17 .A second, fundamental distinction among the different models concerns the adopted mechanisms of social learning describing how stable dispositions are ...