Figure 6
From: Depolarization of echo chambers by random dynamical nudge
The RDN applied to a selective social influence model of echo chamber formation. A different opinion dynamics model that typically produces echo chambers (a, left)34 can prevent these echo chambers from forming by applying the RDN as in Eq. (7) (a, right). Both simulations in (a) have the same seed conditions. (b) The number of peaks formed after a 5000 time step simulation was dependent on the RDN strength D, but not clearly on sample size n (10 trials each, bars indicate standard error of the mean). Note that the selective social influence model can produce more than 2 echo chambers. (c) Applying the RDN (\(D=10\) and \(n=1\)) to an already-polarized network “Pre RDN” took longer to become depolarized, if at all (\(t_{max} = 50000\)). (d) The number of peaks over time for a range of n and D (average of 10 trials). By \(t_{max}\), most networks had stabilized, with larger D values causing fewer peaks. This change in the number of peaks between “\(t_{max}\)” and “Pre RDN” is shown in (e). Note that for \(D \ge \,0.3\) the simulations could become numerically unstable (any change in opinion of \(>1\) within 10 time steps) and were excluded (grey block in “d” indicates less than 3 stable simulations). The RDN in examples (a, c) has a strength (D) of 0.2 and a sample size (n) of 1. Other parameters were as in the authors’ original model: \(N=100\), \(E=400\), \(m=10\), \(\epsilon = 0.5\), \(\mu =0.5\), \(p=0.5\), and \(q=0.5\).
