Floriana Gargiulo and Alberto Mazzoni (2008)
Can Extremism Guarantee Pluralism?
Journal of Artificial Societies and Social Simulation
vol. 11, no. 4 9
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Received: 26-Mar-2008 Accepted: 14-Jul-2008 Published: 31-Oct-2008
where ki is the degree of the pre-existing agent.
|Figure 1. Homophily in network formation. Probability of connection between the new node (N) and a node ( i) already included in the network as a function of their opinion. The left plot is realized with a parameter β=2 in Equation 1, and the right one for β=10|
|Figure 2. Network structure. Degree distribution (left plot) and clustering coefficient as a function of the degree (right plot) for a network of 10000 nodes and for different values of β. The result is averaged over 100 realizations of the network|
|Figure 3. Neighbours opinion at the end of network formation. The continuous line represents the average opinion of the neighbours of an agent as a function of the opinion of the agent. The three plots correspond to three different values of the parameter. The black points represent all the couples (oi, oj) for all the links of the network|
|Figure 4. Results of opinion dynamics with α=0 (tolerance and interaction do not depend on opinion) on network structures built with different values of β. Each line represents the opinion evolution of one agent. Only a subset of 100 agents out of 1000 is displayed. The plot is the sketch of one realization of the simulation. Notice the difference in the x-scale of the fourth panel.|
|Figure 5. Results of opinion dynamics with α=1 (tolerance and interaction strongly depend on opinion) on network structures built with different values of β. Each line represents the opinion evolution of one agent. Only a subset of 100 agents out of 1000 is displayed. The plot is the sketch of one realization of the simulation|
|Figure 6. Results of opinion evolution on networks built with β=0 (Barabasi Albert network) for dynamics using different values of α. Each line represents the opinion evolution of one agent. Only a subset of 100 agents out of 1000 is displayed. The plot is the sketch of one realization of the simulation|
|Figure 7. Results of opinion evolution on networks built with β=50 (strong homophily) for dynamics using different values of α. Each line represents the opinion evolution of one agent. Only a subset of 100 agents out of 1000 is displayed. The plot is the sketch of one realization of the simulation|
|Figure 8. Neighbours opinion at the end of the opinion dynamics process. The plots correspond to values of the parameter &beta = 0 (top) and 10 (bottom). The points represent all the couples (oi,o) for all the links of the network|
|gdim=(nag in the giant cluster) / Nag|
|Figure 9. Cluster statistics for different values of α as a function of the static parameter β. Relative size of the giant cluster (A), number of clusters (B) and average secondary cluster size (C). Results are averaged over 100 simulations|
|Figure 10. Cluster statistics for different values of β as a function of the dynamical parameter α. Giant cluster dimension (A), number of clusters (B) and extremist cluster dimension (C). Results are averaged over 100 simulations|
|Figure 11. Giant cluster dimension vs α for a complete graph and an opinion dependent scale free structure with β=0. Each point is obtained as the average of 100 simulations with 1000 agents|
As observed in figure 12, also in this case a very good superposition is observed.
|Figure 12. Giant cluster dimension vs α for an opinion dependent scale free structure and an opinion dependent random degree structure, both with β=3. Each point is obtained as the average of 100 simulations with 1000 agents|
|Figure 13. Different regimes identified by number of clusters as a function of (α,β). Each point is obtained as the average of 25 simulations with 1000 agents|
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