Éminence Grise Coalitions: On the Shaping of Public Opinion

We consider an opinion network of multiple individuals with dynamics evolving via a general time-varying continuous time consensus algorithm. In such a network, a subset of individuals forms an éminence grise coalition (EGC) if the individuals in that subset are capable of leading the entire network to agreeing on any desired opinion through a cooperative choice of their own initial opinions. In this endeavor, the coalition members are assumed to have access to full profile of the coupling graph of the network as well as the initial opinions of all other individuals. We establish the existence of a minimum size EGC and develop a nontrivial set of tight upper and lower bounds on that size. Thus, even when the coupling graph does not guarantee convergence to a global or multiple consensus, a generally restricted coalition of individuals can steer public opinion toward a desired consensus, provided they can cooperatively adjust their own initial opinions. Geometric insights into the structure of EGCs are also given.

[1]  P. Chebotarev,et al.  Forest Matrices Around the Laplaeian Matrix , 2002, math/0508178.

[2]  Behrouz Touri,et al.  On Ergodicity, Infinite Flow, and Consensus in Random Models , 2010, IEEE Transactions on Automatic Control.

[3]  Jie Lin,et al.  Coordination of groups of mobile autonomous agents using nearest neighbor rules , 2003, IEEE Trans. Autom. Control..

[4]  V. Blondel,et al.  Convergence of different linear and non-linear Vicsek models , 2006 .

[5]  J.N. Tsitsiklis,et al.  Convergence in Multiagent Coordination, Consensus, and Flocking , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[6]  E. Seneta,et al.  Towards consensus: some convergence theorems on repeated averaging , 1977, Journal of Applied Probability.

[7]  A. N. Shiryayev,et al.  On The Theory of Markov Chains , 1992 .

[8]  Sadegh Bolouki,et al.  Theorems about ergodicity and class-ergodicity of chains with applications in known consensus models , 2012, 2012 50th Annual Allerton Conference on Communication, Control, and Computing (Allerton).

[9]  Sadegh Bolouki,et al.  On the Limiting Behavior of Linear or Convex Combination Based Updates of Multi-Agent Systems , 2011 .

[10]  Sadegh Bolouki,et al.  On consensus with a general discrete time convex combination based algorithm for multi-agent systems , 2011, 2011 19th Mediterranean Conference on Control & Automation (MED).

[11]  Sadegh Bolouki,et al.  Consensus Algorithms and the Decomposition-Separation Theorem , 2013, IEEE Transactions on Automatic Control.

[12]  Magnus Egerstedt,et al.  Controllability of Multi-Agent Systems from a Graph-Theoretic Perspective , 2009, SIAM J. Control. Optim..

[13]  A. Kolmogoroff Zur Theorie der Markoffschen Ketten , 1936 .

[14]  Albert-László Barabási,et al.  Controllability of complex networks , 2011, Nature.

[15]  Isaac M. Sonin,et al.  The Decomposition-Separation Theorem for Finite Nonhomogeneous Markov Chains and Related Problems , 2008 .

[16]  D. Blackwell Finite Non-Homogeneous Chains , 1945 .

[17]  Luc Moreau,et al.  Stability of multiagent systems with time-dependent communication links , 2005, IEEE Transactions on Automatic Control.

[18]  John N. Tsitsiklis,et al.  Problems in decentralized decision making and computation , 1984 .

[19]  M. G. Kreïn Stability of solutions of differential equations in Banach space , 2007 .

[20]  M. Kanat Camlibel,et al.  Zero Forcing Sets and Controllability of Dynamical Systems Defined on Graphs , 2014, IEEE Transactions on Automatic Control.

[21]  John N. Tsitsiklis,et al.  Convergence of Type-Symmetric and Cut-Balanced Consensus Seeking Systems , 2011, IEEE Transactions on Automatic Control.

[22]  John N. Tsitsiklis,et al.  Parallel and distributed computation , 1989 .

[23]  H.G. Tanner,et al.  On the controllability of nearest neighbor interconnections , 2004, 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601).

[24]  G. F.,et al.  From individuals to aggregations: the interplay between behavior and physics. , 1999, Journal of theoretical biology.

[25]  Felipe Cucker,et al.  Emergent Behavior in Flocks , 2007, IEEE Transactions on Automatic Control.

[26]  Guillaume Deffuant,et al.  Meet, discuss, and segregate! , 2002, Complex..

[27]  Sadegh Bolouki,et al.  Ergodicity and class-ergodicity of balanced asymmetric stochastic chains , 2013, 2013 European Control Conference (ECC).

[28]  Jianhong Shen A Geometric Approach to Ergodic Non-Homogeneous Markov Chains , 2000 .

[29]  M. Degroot Reaching a Consensus , 1974 .

[30]  Vicsek,et al.  Novel type of phase transition in a system of self-driven particles. , 1995, Physical review letters.

[31]  Behrouz Touri,et al.  Product of Random Stochastic Matrices , 2011, IEEE Transactions on Automatic Control.

[32]  Behrouz Touri,et al.  On Approximations and Ergodicity Classes in Random Chains , 2010, IEEE Transactions on Automatic Control.

[33]  Xiaoli Wang,et al.  Consensus controllability, observability and robust design for leader-following linear multi-agent systems , 2013, Autom..

[34]  Diemo Urbig,et al.  About the Power to Enforce and Prevent Consensus by Manipulating Communication Rules , 2007, Adv. Complex Syst..

[35]  R. Brockett Finite Dimensional Linear Systems , 2015 .

[36]  Huaiqing Wang,et al.  J ul 2 00 4 Multi-agent coordination using nearest neighbor rules : a revisit to Vicsek model ∗ , 2008 .

[37]  Huaiqing Wang,et al.  Multi-agent coordination using nearest neighbor rules: revisiting the Vicsek model , 2004, ArXiv.

[38]  Jan Lorenz,et al.  A stabilization theorem for dynamics of continuous opinions , 2005, 0708.2981.

[39]  Behrouz Touri,et al.  On backward product of stochastic matrices , 2011, Autom..

[40]  Rainer Hegselmann,et al.  Opinion dynamics and bounded confidence: models, analysis and simulation , 2002, J. Artif. Soc. Soc. Simul..

[41]  John N. Tsitsiklis,et al.  Distributed Asynchronous Deterministic and Stochastic Gradient Optimization Algorithms , 1984, 1984 American Control Conference.