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Bounded confidence dynamics and graph control: Enforcing consensus

The second author wishes to thank Benedetto Picolli for helpful discussions

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  • A generic feature of bounded confidence type models is the formation of clusters of agents. We propose and study a variant of bounded confidence dynamics with the goal of inducing unconditional convergence to a consensus. The defining feature of these dynamics which we name the No one left behind dynamics is the introduction of a local control on the agents which preserves the connectivity of the interaction network. We rigorously demonstrate that these dynamics result in unconditional convergence to a consensus. The qualitative nature of our argument prevents us quantifying how fast a consensus emerges, however we present numerical evidence that sharp convergence rates would be challenging to obtain for such dynamics. Finally, we propose a relaxed version of the control. The dynamics that result maintain many of the qualitative features of the bounded confidence dynamics yet ultimately still converge to a consensus as the control still maintains connectivity of the interaction network.

    Mathematics Subject Classification: 34H05, 82C22, 90C35, 91D30.

    Citation:

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  • Figure 1.  The movement of an agent according to the bounded confidence dynamics (2.3)

    Figure 3.  Simulation of the opinion dynamics without and with control (resp. left and right figure), e.g. solving resp. (2.3) and Model 1 with $ r_{*} = \frac12 $. With the control (right), the dynamics converge to a consensus

    Figure 2.  Illustration of the critical regions (3.1) in $ \mathbb{R} $ (interval behind $ {\bf x}_i $) and $ \mathbb{R}^2 $ (semi-annulus region). The opinion $ {\bf x}_i $ is attracted toward the local average $ \overline{\bf x}_i $ and hence moves with velocity $ \overline{\bf x}_i-{\bf x}_i $. In the "No-left behind dynamics" (1), $ {\bf x}_i $ can only move only if there is no one in its critical region $ \mathcal{B}_i $. Thus, $ {\bf x}_i $ freezes whereas $ {\bf x}_j $ is free to move in the left illustration

    Figure 4.  A configuration of agents (top) and the resulting interaction graph (edge set E, black) and behind graph (edge set $ E^{\mathcal{B}}) $, light blue). Note that the behind graph is a directed subgraph of the interaction graph

    Figure 5.  Counter-example in multi-dimension. Blue arrow is the velocity of each cluster. In this setting, every agent has someone in its critical region $ \mathcal{B}_i $. Thus, the naive control in Model 1 would prevent anyone from moving

    Figure 6.  The velocity of agent $ i $ is the projection of the desired velocity $ \overline{\bf x}_i-{\bf x}_i $ onto the cone of admissible velocity $ \mathcal{C}_{i} $

    Figure 7.  2D simulation of opinion dynamics without and with control (resp, top and bottom figure), e.g. solving resp. (1) and (3.5) with $ r_* = \frac12 $. With the control (bottom), the dynamics converge to a consensus

    Figure 8.  Preserving connectivity does not imply the convergence to a consensus. Here, when $ r_* = 1 $, the extreme points $ x_1 $ and $ x_4 $ will converge towards $ x_2 $ and $ x_3 $ respectively. However, $ x_2 $ and $ x_3 $ cannot move since $ x_1 $ and $ x_4 $ are always in their respective critical regions

    Figure 9.  The convex hull $ \Omega(t_n) $ has to converge to a limit configuration $ \Omega^\infty $. The dynamics converge to a consensus if $ \Omega^\infty $ is reduced to a single point which we prove by contradiction. We distinguish three cases of limit configuration $ \Omega^\infty $ depending on if the extreme point $ {\bf x}_p^\infty $ has a so-called extreme neighbor $ j $, i.e. $ \|{\bf x}_p^\infty-{\bf x}_j^\infty\| = 1 $

    Figure 10.  If the limit configuration $ \{{\bf x}_k^\infty\}_k $ is not a consensus, the extreme point $ {\bf x}_p(t_n) $ will eventually get inside the convex hull $ \Omega^\infty $ which gives a contradiction

    Figure 11.  Situation in the case 2. The extreme point $ x_p $ needs $ x_{p_2} $ the neighbor of its neighbor $ x_{p_1} $ to be pushed further to the right

    Figure 12.  The decay of the diameter $ d(t) $ is first linear and then exponential after the diameter $ d(t) $ becomes less than $ 1 $

    Figure 13.  Left: diameter $ d(t) $ over time for $ 100 $ realizations (quantile representation). Right: stopping time $ \tau $ (4.28) depending on the size of the critical region $ r_* $

    Figure 14.  An example of how the behind graph can be relaxed while still ensuring that the interaction graph remains connected. The interaction graph is represented by undirected and directed edges, the behind graph is represented by only the blue directed edges. Agent 3 is in the behind region of both agent 2 and agent 4 and agents 2 and 4 are connected in the interaction graph therefore we may remove the edge from agent 4 to agent 3

    Figure 15.  The NOLB dynamics do not allow the red agent to disconnect from the blue agent (illustrated with a purple chain). The RNOLB dynamics allow this disconnection to occur but maintain connectivity of the whole configuration

    Figure 16.  The RNOLB dynamics can be seen as an interpolation between NOLB and bounded confidence

    Figure 17.  Diameter, $ d(t) $ over time for 100 realizations of the RNOLB dynamics (quantile representation)

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