We propose a model for the localization of risky information in social (scale free) networks where we assume that risky information can propagate only between “mutually trusted nodes” (MTN). We propose an algorithm to construct the MTN network and show that there is a critical value m¯c2 of...

We examine the evolution of the vocabulary of a group of individuals (linguistic agents) on a scale-free network, using Monte Carlo simulations and assumptions from evolutionary game theory. It is known that when the agents are arranged in a two-dimensional lattice structure and interact by...

We use Monte Carlo simulations and arguments from percolation theory in order to determine how “confidential” information propagates or localizes on a scale-free network. The basic assumption of our models is that this type of information propagates through the subnetwork of “best...

We use Monte Carlo simulations and assumptions from evolutionary game theory in order to study the evolution of words and the population dynamics of a system made of two interacting species which initially speak two different languages. The species are characterized by their identity,...

We use the detrended fluctuation analysis (DFA) and the Grassberger–Proccacia analysis (GP) methods in order to study language characteristics. Despite that we construct our signals using only word lengths or word frequencies, excluding in this way huge amount of information from language, the...

We use the formulation of equilibrium statistical mechanics in order to study some important characteristics of language. Using a simple expression for the Hamiltonian of a language system, which is directly implied by the Zipf law, we are able to explain several characteristic features of human...