Modified Axelrod Model Showing Opinion Convergence And Polarization In Realistic Scale-Free Networks

Abstract

Axelrod model is an opinion dynamics model such that each agent on a square lattice has a finite number of possible nominal opinions on a finite number of issues that are usually called features in the field. Moreover, its dynamics between two agents is assimilative in the sense that the number of agreeing features between them never decreases upon interaction. Here we modify this model to study opinion convergence, polarization and more importantly to find ways to reduce opinion polarization in an already polarized population. We do so by changing or adding several elements from complex network and continuous opinion dynamics research. First, we put agents in a scale-free network. Second, we adopt the bounded confidence model by representing our agent's opinions by numbers in $[-1,1]$ those distances follow the standard Euclidean metric. Third, our rules allow both convergence and divergence of their resultant opinions after a pair of agents interacts. As a result, our modified model offers a more comprehensive exploration of opinion dynamics. Computer simulation results of our model show scaling behavior and a notable trend in opinion polarization on all features in the majority of reasonable simulation parameters. To mitigate this polarization, we introduce empathetic agents that work actively to reduce opinion differences. However, our findings indicate limited success in the approach for the most effective way is to change the behavior of a significant portion of highly connected agents. This research contributes to the understanding of opinion dynamics within society and highlights the nuanced complexities that arise when considering factors such as network structure and continuous opinion values. Our results prompt further exploration and open avenues for future investigations into effective methods of reducing opinion polarization.