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

Conventions spreading in open-ended systems

We introduce a simple open-ended model that describes the emergence of a shared vocabulary. The ordering transition toward consensus is generated only by an agreement mechanism. This interaction defines a finite and small number of states, despite each individual having the ability to invent an unlimited number of new words. The existence of a phase transition is studied by analyzing the convergence times, the cognitive efforts of the agents and the scaling behavior in memory and timeComment: 11 pages, 5 figure

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

How Do You Like Me in This: User Embodiment Preferences for Companion Agents

We investigate the relationship between the embodiment of an artificial companion and user perception and interaction with it. In a Wizard of Oz study, 42 users interacted with one of two embodiments: a physical robot or a virtual agent on a screen through a role-play of secretarial tasks in an office, with the companion providing essential assistance. Findings showed that participants in both condition groups when given the choice would prefer to interact with the robot companion, mainly for its greater physical or social presence. Subjects also found the robot less annoying and talked to it more naturally. However, this preference for the robotic embodiment is not reflected in the users’ actual rating of the companion or their interaction with it. We reflect on this contradiction and conclude that in a task-based context a user focuses much more on a companion’s behaviour than its embodiment. This underlines the feasibility of our efforts in creating companions that migrate between embodiments while maintaining a consistent identity from the user’s point of view

Self-organizing communication in language games

From the point of view of semiotic dynamics language is an evolving complex dynamical system. In this perspective, unrevealing the mechanisms that allow for the birth of shared conventions is a major issue. Here we describe a very simple model in which agents negotiate conventions and reach a global agreement without any intervention from the outside. In particular we focus on the possibility of predicting on which of the several competing conventions the agreement is reached. We find from simulations that early created conventions are favored in the competition process and this advantage can be quantified. Beyond the specific results presented here, we think that this paper provides an example of a new way of investigating language features where simple models allow for the investigation of precise problems and, possibly, for analytical approaches

THSim v3.2: The Talking Heads simulation tool

The field of language evolution and computation may benefit from using efficient and robust simulation tools that are based on widely exploited principles within the field. The tool presented in this paper is one that could fulfil such needs. The paper presents an overview of the tool -- THSim v3.2 -- and discusses some research questions that can be investigated with it

Nonequilibrium dynamics of language games on complex networks

The naming game is a model of nonequilibrium dynamics for the self-organized emergence of a linguistic convention or a communication system in a population of agents with pairwise local interactions. We present an extensive study of its dynamics on complex networks, that can be considered as the most natural topological embedding for agents involved in language games and opinion dynamics. Except for some community structured networks on which metastable phases can be observed, agents playing the naming game always manage to reach a global consensus. This convergence is obtained after a time generically scaling with the population’s size N as tconv∼N1.4±0.1, i.e., much faster than for agents embedded on regular lattices. Moreover, the memory capacity required by the system scales only linearly with its size. Particular attention is given to heterogenous networks, in which the dynamical activity pattern of a node depends on its degree. High-degree nodes have a fundamental role, but require larger memory capacity. They govern the dynamics acting as spreaders of (linguistic) conventions. The effects of other properties, such as the average degree and the clustering, are also discussed

In-depth analysis of the Naming Game dynamics: the homogeneous mixing case

Language emergence and evolution has recently gained growing attention through multi-agent 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.Comment: 30 pages, 19 figures (few in reduced definition). In press in IJMP

Information and Computation

In this chapter, concepts related to information and computation are reviewed in the context of human computation. A brief introduction to information theory and different types of computation is given. Two examples of human computation systems, online social networks and Wikipedia, are used to illustrate how these can be described and compared in terms of information and computation.Comment: 9 pages, 3 figures. Draft of a chapter to be published in Michelucci, P. (Ed.) Handbook of Human Computation, Springe