Bootstrapping communication in language games

Semiotic dynamics is a fast growing field according to which language can be seen as an evolving and self-organizing system. In this paper we present a simple multi-agent framework able to account for the emergence of shared conventions in a population. Agents perform pairwise games and final consensus is reached without any outside control nor any global knowledge of the system. In particular we discuss how embedding the population in a non trivial interaction topology affects the behavior of the system and forces to carefully consider agents selection strategies. These results cast an interesting framework to address and study more complex issues in semiotic dynamics

Agreement dynamics on interaction networks with diverse topologies

We review the behavior of a recently introduced model of agreement dynamics, called the "Naming Game." This model describes the self-organized emergence of linguistic conventions and the establishment of simple communication systems in a population of agents with pairwise local interactions. The mechanisms of convergence towards agreement strongly depend on the network of possible interactions between the agents. In particular, the mean-field case in which all agents communicate with all the others is not efficient, since a large temporary memory is requested for the agents. On the other hand, regular lattice topologies lead to a fast local convergence but to a slow global dynamics similar to coarsening phenomena. The embedding of the agents in a small-world network represents an interesting tradeoff: a local consensus is easily reached, while the long-range links allow to bypass coarsening-like convergence. We also consider alternative adaptive strategies which can lead to faster global convergence

The role of topology on the dynamics of the Naming Game

The Naming Game captures the essential features leading a population to agree on the use of a semiotic convention (or, more in general, an opinion). Consensus emerges through local negotiations between pairs of agents, in the absence of any central co-ordination. Thus, it is natural that topology, identifying the set of possible interactions, plays a central role in the dynamics of the model. Here, we review the role of different topological properties, pointing out that finite connectivity, combined with the small-world property, ensure the best performances in terms of memory usage and time to reach convergence

Microscopic activity patterns in the Naming Game

The models of statistical physics used to study collective phenomena in some interdisciplinary contexts, such as social dynamics and opinion spreading, do not consider the effects of the memory on individual decision processes. On the contrary, in the Naming Game, a recently proposed model of Language formation, each agent chooses a particular state, or opinion, by means of a memory-based negotiation process, during which a variable number of states is collected and kept in memory. In this perspective, the statistical features of the number of states collected by the agents becomes a relevant quantity to understand the dynamics of the model, and the influence of topological properties on memory-based models. By means of a master equation approach, we analyze the internal agent dynamics of Naming Game in populations embedded on networks, finding that it strongly depends on very general topological properties of the system (e.g. average and fluctuations of the degree). However, the influence of topological properties on the microscopic individual dynamics is a general phenomenon that should characterize all those social interactions that can be modeled by memory-based negotiation processes.Comment: submitted to J. Phys.

Agreement dynamics on small-world networks

In this paper we analyze the effect of a non-trivial topology on the dynamics of the so-called Naming Game, a recently introduced model which addresses the issue of how shared conventions emerge spontaneously in a population of agents. We consider in particular the small-world topology and study the convergence towards the global agreement as a function of the population size N as well as of the parameter p which sets the rate of rewiring leading to the small-world network. As long as p > > 1/N, there exists a crossover time scaling as N/p2 which separates an early one-dimensional–like dynamics from a late-stage mean-field–like behavior. At the beginning of the process, the local quasi–one-dimensional topology induces a coarsening dynamics which allows for a minimization of the cognitive effort (memory) required to the agents. In the late stages, on the other hand, the mean-field–like topology leads to a speed-up of the convergence process with respect to the one-dimensional case