On the Consensus Threshold for the Opinion Dynamics of Krause-Hegselmann

TL;DR

This paper investigates the critical confidence bound for consensus in the Krause-Hegselmann opinion dynamics model, proposing a conjecture that the threshold depends on the average degree of the social graph as population size grows.

Contribution

It introduces a conjecture linking the consensus threshold to the graph's average degree behavior in large populations, supported by numerical evidence.

Findings

Threshold psilon_c equals psilon_i 0.2 for diverging average degree

Threshold psilon_c equals 1/2 for finite average degree

Conjecture distinguishes two regimes based on social network connectivity

Abstract

In the consensus model of Krause-Hegselmann, opinions are real numbers between 0 and 1 and two agents are compatible if the difference of their opinions is smaller than the confidence bound parameter \epsilon. A randomly chosen agent takes the average of the opinions of all neighbouring agents which are compatible with it. We propose a conjecture, based on numerical evidence, on the value of the consensus threshold \epsilon_c of this model. We claim that \epsilon_c can take only two possible values, depending on the behaviour of the average degree d of the graph representing the social relationships, when the population N goes to infinity: if d diverges when N goes to infinity, \epsilon_c equals the consensus threshold \epsilon_i ~ 0.2 on the complete graph; if instead d stays finite when N goes to infinity, \epsilon_c=1/2 as for the model of Deffuant et al.