Consensus in networks of mobile communicating agents

Abstract

Populations of mobile and communicating agents describe a vast array of technological and natural systems, ranging from sensor networks to animal groups. Here, we investigate how a group-level agreement may emerge in the continuously evolving network defined by the local interactions of the moving individuals. We adopt a general scheme of motion in two dimensions and we let the individuals interact through the minimal naming game, a prototypical scheme to investigate social consensus. We distinguish different regimes of convergence determined by the emission range of the agents and by their mobility, and we identify the corresponding scaling behaviors of the consensus time. In the same way, we rationalize also the behavior of the maximum memory used during the convergence process, which determines the minimum cognitive/storage capacity needed by the individuals. Overall, we believe that the simple and general model presented in this paper can represent a helpful reference for a better understanding of the behavior of populations of mobile agents.

Controlling Contagion Processes in Activity Driven Networks

“…Although activity driven models in their simplest formulation do not account for many features such as link persistence, homophily etc. [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22], they allow the analytical formulation of the concurrent network and contagion process dynamics in the form of appropriate mean-field equations, thus allowing the quantitative study of the dynamical process of interest [23][24][25][26][27]. We study contagion processes in activity-driven models using the basic SIS model [30].…”

“…Real world time-varying networks add a number of complications to the simplified picture offered by activity driven networks. Indeed they exhibit correlations among nodes, persistency of links, burstiness of the activity pattern, just to cite a few [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. It is therefore extremely important to validate, at least in its basic phenomenology, the above mean-field framework in real world datasets.…”

A consensus opinion model based on the evolutionary game

“…Some models display a disorderorder transition [7][8][9][10][11][12], from a regime in which opinions are arbitrarily diverse to one in which most individuals hold the same opinion. Other models focus the emergence of a global consensus [13][14][15][16][17][18], in which all agents finally share the same opinion.In this Letter, we propose an opinion model based on the evolutionary game. Evolutionary game theory as a powerful mathematical framework, has been widely used to understand cooperative behavior [19,20], traffic flow [21,22], epidemic spreading [23,24] and so on.…”

“…Some models display a disorderorder transition [7][8][9][10][11][12], from a regime in which opinions are arbitrarily diverse to one in which most individuals hold the same opinion. Other models focus the emergence of a global consensus [13][14][15][16][17][18], in which all agents finally share the same opinion.…”

Control Strategies of Contagion Processes in Time-Varying Networks

“…Indeed, spreading processes have been typically considered to take place in either static (τ P τ G ) or annealed (τ P τ G ) networks. While this approximation can be used to study a range of processes such as the spreading of some diseases in contact networks or the propagation of energy in power grids it fails to describe many others phenomena in which the two timescales are comparable [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]. In these cases, such as the spreading of ideas, memes, information and some type of diseases the diffusion processes take place in timevarying networks [41,42,43].…”