Activation Regimes in Opinion Dynamics: Comparing Asynchronous Updating Schemes

Authored by Claudio Cioffi-Revilla, Meysam Alizadeh

Date Published: 2015

Sponsors: No sponsors listed

Platforms: Python

Model Documentation: Other Narrative Mathematical description

Model Code URLs: https://www.comses.net/codebases/4316/releases/1.0.0/

Abstract

Empirical evidences have supported the large heterogeneity in the timing of individuals' activities. Moreover, computational analysis of the agent-based models has shown the importance of the activation regimes. In this paper, we apply four different asynchronous updating schemes including random, uniform, and two state-driven Poisson updating schemes on an agent-based opinion dynamics model. We compare the effect of these activation regimes by measuring the appropriate opinion clustering statistics and also the number of emergent extremists. The results exhibit both qualitative and quantitative difference between different activation regimes which in some cases are counterintuitive. In particular, we find that exposing the radical/moderate agents to more encounters decreases/increases the average number of extremists compared to other types of activation regimes. The results also show that no specific updating scheme can always outperform the others in reaching to consensus.

Tags

Consensus bounded confidence patterns Evolutionary games Extremism propagation Hegselmann