Multidimensional Consensus model on a Barabasi-Albert network

TL;DR

This paper simulates a multidimensional consensus model on a directed Barabasi-Albert network, revealing how opinion agreement depends on agent-to-subject ratios and tolerance levels, with most cases lacking absolute consensus.

Contribution

It introduces a multidimensional opinion model on a complex network and analyzes how consensus and clustering depend on key parameters, highlighting the emergence of persistent pluralism.

Findings

Most cases do not reach absolute consensus.

The ratio of agents to subjects influences whether consensus or pluralism occurs.

A second robust cluster often remains, depending on the number of subjects.

Abstract

A Consensus Model according to Deffuant on a directed Barabasi-Albert network was simulated. Agents have opinions on different subjects. A multi-component subject vector was used. The opinions are discrete. The analysis regards distribution and clusters of agents which are on agreement in the opinions of the subjects. Remarkable results are on the one hand, that there mostly exists no absolute consens. It determines depending on the ratio of number of agents to the number of subjects, whether the communication ends in a consens or a pluralism. Mostly a second robust cluster remains, in its size depending on the number of subjects. Two agents agree either in (nearly) all or (nearly) no subject. The operative parameter of the consens-formating-process is the tolerance in change of views of the group-members.