September  2020, 15(3): 489-517. doi: 10.3934/nhm.2020028

Bounded confidence dynamics and graph control: Enforcing consensus

1. 

Georgia Institute of Technology, Program in Quantitative Biosciences, Georgia Institute of Technology School of Physics, Atlanta, GA 30332, USA

2. 

Arizona State University, School of Mathematical and Statistical Sciences, Tempe, AZ 85257-1804, USA

Received  December 2019 Revised  July 2020 Published  September 2020

Fund Project: The second author wishes to thank Benedetto Picolli for helpful discussions

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.

Citation: GuanLin Li, Sebastien Motsch, Dylan Weber. Bounded confidence dynamics and graph control: Enforcing consensus. Networks & Heterogeneous Media, 2020, 15 (3) : 489-517. doi: 10.3934/nhm.2020028
References:
[1]

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E. Estrada, E. Vargas-Estrada and H. Ando, Communicability angles reveal critical edges for network consensus dynamics, Phys. Rev. E (3), 92 (2015), 10pp. doi: 10.1103/PhysRevE.92.052809.  Google Scholar

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R. Hegselmann and U. Krause, Opinion dynamics and bounded confidence: models, analysis and simulation, J. Artificial Societies Social Simulation, 5 (2002). Google Scholar

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J.-B. Hiriart-Urruty and C. Lemaréchal, Fundamentals of Convex Analysis, Grundlehren Text Editions, Springer-Verlag, Berlin, 2001. doi: 10.1007/978-3-642-56468-0.  Google Scholar

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P.-E. Jabin and S. Motsch, Clustering and asymptotic behavior in opinion formation, J. Differential Equations, 257 (2014), 4165-4187.  doi: 10.1016/j.jde.2014.08.005.  Google Scholar

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U. Krause, A discrete nonlinear and non-autonomous model of consensus formation, Communications in Difference Equations, Gordon and Breach, Amsterdam, 2000, 227–236. doi: 10.1201/b16999-21.  Google Scholar

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J. Lorenz, Continuous opinion dynamics under bounded confidence: A survey, Internat. J. Modern Phys. C, 18 (2007), 1819-1838.  doi: 10.1142/S0129183107011789.  Google Scholar

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J. Lorenz, Consensus strikes back in the Hegselmann-Krause model of continuous opinion dynamics under bounded confidence, J. Artificial Societies Social Simulation, (2006). Google Scholar

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S. Motsch and E. Tadmor, Heterophilious dynamics enhances consensus, SIAM Rev., 56 (2014), 577-621.  doi: 10.1137/120901866.  Google Scholar

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R. Olfati-SaberJ. A. Fax and R. M. Murray, Consensus and cooperation in networked multi-agent systems, Proc. IEEE, 95 (2007), 215-233.  doi: 10.1109/TAC.2005.864190.  Google Scholar

[21]

B. PiccoliN. Pouradier Duteil and E. Trélat, Sparse control of Hegselmann–Krause models: Black hole and declustering, SIAM J. Control Optim., 57 (2019), 2628-2659.  doi: 10.1137/18M1168911.  Google Scholar

[22]

L.-A. Poissonnier, S. Motsch, J. Gautrais, J. Buhl and A. Dussutour, Experimental investigation of ant traffic under crowded conditions, eLife, 8 (2019). doi: 10.7554/eLife.48945.  Google Scholar

[23]

W. Quattrociocchi, A. Scala and C. R. Sunstein, Echo Cchambers on Facebook, SSRN, in progress. doi: 10.2139/ssrn.2795110.  Google Scholar

[24]

R. O. Saber and R. M. Murray, Consensus protocols for networks of dynamic agents, Proc. 2003 American Control Conference, Denver, CO, 2003. doi: 10.1109/ACC.2003.1239709.  Google Scholar

[25]

D. Spanos, R. Olfati-Saber and R. Murray, Dynamic consensus on mobile networks, IFAC World Congress, Citeseer, 2005, 1–6. Google Scholar

[26]

T. VicsekA. CzirókE. Ben-JacobI. Cohen and O. Shochet, Novel type of phase transition in a system of self-driven particles, Phys. Rev. Lett., 75 (1995), 1226-1229.  doi: 10.1103/PhysRevLett.75.1226.  Google Scholar

[27]

D. WeberR. Theisen and S. Motsch, Deterministic versus stochastic consensus dynamics on graphs, J. Stat. Phys., 176 (2019), 40-68.  doi: 10.1007/s10955-019-02293-5.  Google Scholar

[28]

H. XiaH. Wang and Z. Xuan, Opinion dynamics: A multidisciplinary review and perspective on future research, Internat. J. Knowledge Syst. Sci. (IJKSS), 2 (2011), 72-91.  doi: 10.4018/978-1-4666-3998-0.ch021.  Google Scholar

[29]

W. YuG. ChenM. Cao and J. Kurths, Second-order consensus for multiagent systems with directed topologies and nonlinear dynamics, IEEE Trans. Syst. Man Cybernetics, Part B, 40 (2010), 881-891.  doi: 10.1109/TSMCB.2009.2031624.  Google Scholar

show all references

References:
[1]

M. Ballerini, N. Cabibbo, R. Candelier, A. Cavagna and E. Cisbani, et al., Empirical investigation of starling flocks: A benchmark study in collective animal behaviour, Animal Behaviour, 76 (2008), 201–215. doi: 10.1016/j.anbehav.2008.02.004.  Google Scholar

[2]

V. D. BlondelJ. M. Hendrickx and J. N. Tsitsiklis, On Krause's multi-agent consensus model with state-dependent connectivity, IEEE Trans. Automat. Control, 54 (2009), 2586-2597.  doi: 10.1109/TAC.2009.2031211.  Google Scholar

[3]

V. D. Blondel, J. M. Hendrickx and J. N. Tsitsiklis, On the 2R conjecture for multi-agent systems, 2007 European Control Conference (ECC), Kos, Greece, 2007. doi: 10.23919/ECC.2007.7068885.  Google Scholar

[4]

J. Buhl, D. J. T. Sumpter, I. D. Couzin, J. J. Hale and E. Despland, et al., From disorder to order in marching locusts, Science, 312 (2006), 1402–1406. doi: 10.1126/science.1125142.  Google Scholar

[5]

M. CaponigroM. FornasierB. Piccoli and E. Trélat, Sparse stabilization and optimal control of the Cucker-Smale model, Math. Control Relat. Fields, 3 (2013), 447-466.  doi: 10.3934/mcrf.2013.3.447.  Google Scholar

[6]

C. CastellanoS. Fortunato and V. Loreto, Statistical physics of social dynamics, Rev. Mod. Phys., 81 (2009), 591-646.  doi: 10.1103/RevModPhys.81.591.  Google Scholar

[7]

G. DeffuantD. NeauF. Amblard and G. Weisbuch, Mixing beliefs among interacting agents, Adv. Complex Syst., 3 (2000), 87-98.  doi: 10.1142/S0219525900000078.  Google Scholar

[8]

E. Estrada, E. Vargas-Estrada and H. Ando, Communicability angles reveal critical edges for network consensus dynamics, Phys. Rev. E (3), 92 (2015), 10pp. doi: 10.1103/PhysRevE.92.052809.  Google Scholar

[9]

K. Garimella, G. De Francisci Morales, A. Gionis and M. Mathioudakis, Political discourse on social media: Echo chambers, gatekeepers, and the price of bipartisanship, Proc. 2018 World Wide Web Conference, 2018, 913–922. doi: 10.1145/3178876.3186139.  Google Scholar

[10]

E. Gilbert, T. Bergstrom and K. Karahalios, Blogs are echo chambers: Blogs are echo chambers, 2009 42nd Hawaii International Conference on System Sciences, Big Island, HI, 2009. doi: 10.1109/HICSS.2009.91.  Google Scholar

[11]

D. Goldie, M. Linick, H. Jabbar and C. Lubienski, Using Bibliometric and social media analyses to explore the "echo chamber" hypothesis, Educational Policy, 28 (2014). doi: 10.1177/0895904813515330.  Google Scholar

[12]

R. Hegselmann and U. Krause, Opinion dynamics and bounded confidence: models, analysis and simulation, J. Artificial Societies Social Simulation, 5 (2002). Google Scholar

[13]

J.-B. Hiriart-Urruty and C. Lemaréchal, Fundamentals of Convex Analysis, Grundlehren Text Editions, Springer-Verlag, Berlin, 2001. doi: 10.1007/978-3-642-56468-0.  Google Scholar

[14]

P.-E. Jabin and S. Motsch, Clustering and asymptotic behavior in opinion formation, J. Differential Equations, 257 (2014), 4165-4187.  doi: 10.1016/j.jde.2014.08.005.  Google Scholar

[15]

D. Kempe, J. Kleinberg and E. Tardos, Maximizing the spread of influence through a social network, Proc. Ninth ACM SIGKDD Internat. Conference Knowledge Discovery Data Mining, 2003, 137–146. doi: 10.1145/956750.956769.  Google Scholar

[16]

U. Krause, A discrete nonlinear and non-autonomous model of consensus formation, Communications in Difference Equations, Gordon and Breach, Amsterdam, 2000, 227–236. doi: 10.1201/b16999-21.  Google Scholar

[17]

J. Lorenz, Continuous opinion dynamics under bounded confidence: A survey, Internat. J. Modern Phys. C, 18 (2007), 1819-1838.  doi: 10.1142/S0129183107011789.  Google Scholar

[18]

J. Lorenz, Consensus strikes back in the Hegselmann-Krause model of continuous opinion dynamics under bounded confidence, J. Artificial Societies Social Simulation, (2006). Google Scholar

[19]

S. Motsch and E. Tadmor, Heterophilious dynamics enhances consensus, SIAM Rev., 56 (2014), 577-621.  doi: 10.1137/120901866.  Google Scholar

[20]

R. Olfati-SaberJ. A. Fax and R. M. Murray, Consensus and cooperation in networked multi-agent systems, Proc. IEEE, 95 (2007), 215-233.  doi: 10.1109/TAC.2005.864190.  Google Scholar

[21]

B. PiccoliN. Pouradier Duteil and E. Trélat, Sparse control of Hegselmann–Krause models: Black hole and declustering, SIAM J. Control Optim., 57 (2019), 2628-2659.  doi: 10.1137/18M1168911.  Google Scholar

[22]

L.-A. Poissonnier, S. Motsch, J. Gautrais, J. Buhl and A. Dussutour, Experimental investigation of ant traffic under crowded conditions, eLife, 8 (2019). doi: 10.7554/eLife.48945.  Google Scholar

[23]

W. Quattrociocchi, A. Scala and C. R. Sunstein, Echo Cchambers on Facebook, SSRN, in progress. doi: 10.2139/ssrn.2795110.  Google Scholar

[24]

R. O. Saber and R. M. Murray, Consensus protocols for networks of dynamic agents, Proc. 2003 American Control Conference, Denver, CO, 2003. doi: 10.1109/ACC.2003.1239709.  Google Scholar

[25]

D. Spanos, R. Olfati-Saber and R. Murray, Dynamic consensus on mobile networks, IFAC World Congress, Citeseer, 2005, 1–6. Google Scholar

[26]

T. VicsekA. CzirókE. Ben-JacobI. Cohen and O. Shochet, Novel type of phase transition in a system of self-driven particles, Phys. Rev. Lett., 75 (1995), 1226-1229.  doi: 10.1103/PhysRevLett.75.1226.  Google Scholar

[27]

D. WeberR. Theisen and S. Motsch, Deterministic versus stochastic consensus dynamics on graphs, J. Stat. Phys., 176 (2019), 40-68.  doi: 10.1007/s10955-019-02293-5.  Google Scholar

[28]

H. XiaH. Wang and Z. Xuan, Opinion dynamics: A multidisciplinary review and perspective on future research, Internat. J. Knowledge Syst. Sci. (IJKSS), 2 (2011), 72-91.  doi: 10.4018/978-1-4666-3998-0.ch021.  Google Scholar

[29]

W. YuG. ChenM. Cao and J. Kurths, Second-order consensus for multiagent systems with directed topologies and nonlinear dynamics, IEEE Trans. Syst. Man Cybernetics, Part B, 40 (2010), 881-891.  doi: 10.1109/TSMCB.2009.2031624.  Google Scholar

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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