Fig 1.
Number of clusters obtained for various ε and γ.
For ε > 0.4 the population always converges to 1 cluster, so we omitted the range from the plot, for better visualisation. Values are averaged over 100 runs.
Fig 2.
Mean opinion distance obtained for various ε and γ.
Values are averaged over 100 runs.
Fig 3.
Total number of interactions required for convergence normalized by the number of individuals, averaged over 100 runs.
Fig 4.
Normalized total, non-null difference and active number of interactions required for convergence for ε = 0.4. The reference 35exp(γ3.4) is shown as a visual aid only.
Fig 5.
Evolution of the population of opinions for various γ and ε values.
The first row corresponds to the case where ε = 1, the second row corresponds to ε = 0.32 while the last row corresponds to ε = 0.2. In all cases γ ∈ {0, 1, 1.1, 1.6} (left to right).
Fig 6.
Finite size effects: Number of clusters without algorithmic bias.
Effective number of clusters obtained for various ε and N, when γ = 0. Values are averaged over 200, 150, 100, 100 and 100 runs for N ∈ {100, 250, 500, 750, 1000}, respectively. Error bars show one standard deviation from the mean.
Fig 7.
Finite size effects: Number of clusters with algorithmic bias.
Effective number of clusters obtained for various ε, γ and N. Values are averaged over 200, 150, 100, 100 and 100 runs for N ∈ {100, 250, 500, 750, 1000}, respectively. Error bars show one standard deviation from the mean.
Fig 8.
Finite size effects: Opinion distance with algorithmic bias.
Mean opinion distance obtained for various ε, γ and N. Values are averaged over 200, 150, 100, 100 and 100 runs for N ∈ {100, 250, 500, 750, 1000}, respectively. Error bars show one standard deviation from the mean.
Fig 9.
Initial condition: Number of clusters.
Effect of the initial condition on the effective number of clusters (averages over 100 runs).
Fig 10.
Initial condition: Opinion distance.
Effect of the initial condition on the average opinion distance (averages over 100 runs).
Fig 11.
Initial condition: Time to convergence.
Effect of the initial condition on the convergence time measured in number of active interactions (averages over 100 runs).