Information sharing and sorting in a community

TL;DR

This paper presents a numerical study of a community information sharing model, revealing unique exponents and cluster formation phenomena, with detailed analysis of the largest cluster growth and finite-size scaling.

Contribution

It introduces a less restricted information sharing model and demonstrates that its critical exponents differ from the well-known naming game, highlighting new non-trivial behaviors.

Findings

Exponents differ from the naming game.

Clusters of agents with the same information form.

Finite-size scaling of the largest cluster analyzed.

Abstract

We present the results of detailed numerical study of a model for the sharing and sorting of informations in a community consisting of a large number of agents. The information gathering takes place in a sequence of mutual bipartite interactions where randomly selected pairs of agents communicate with each other to enhance their knowledge and sort out the common information. Though our model is less restricted compared to the well established naming game, yet the numerical results strongly indicate that the whole set of exponents characterizing this model are different from those of the naming game and they assume non-trivial values. Finally it appears that in analogy to the emergence of clusters in the phenomenon of percolation, one can define clusters of agents here having the same information. We have studied in detail the growth of the largest cluster in this article and performed its finite-size scaling analysis.