Berchtold, A., Raftery, A.: The mixture transition distribution model for high-order Markov chains and non-Gaussian time series. Stat. Sci. 7, 328–356 (2002)
MATH
MathSciNet
Google Scholar
Berger, R.L.: A necessary and sufficient condition for reaching a consensus using DeGroot’s method. J. Amer. Stat. Assoc. 76, 415–418 (1981)
CrossRef
MATH
MathSciNet
Google Scholar
Chatterjee, S., Seneta, E.: Towards consensus: some convergence theorems on repeated averaging. J. Appl. Prob. 14, 89–97 (1977)
CrossRef
MATH
MathSciNet
Google Scholar
De Groot, M.H.: Reaching a consensus. J. Amer. Stat. Assoc. 69, 118–121 (1974)
CrossRef
MATH
Google Scholar
Hegselmann, R., Krause, U.: Opinion dynamics and bounded confidence: models, analysis and simulation. J. Art. Soc. Soc. Sim. 5(3), 1–33 (2002)
Google Scholar
Hegselmann, R., Krause, U.: Opinion dynamics driven by various ways of averaging. Comp. Econ. 25, 381–405 (2005)
CrossRef
MATH
Google Scholar
Krause, U.: A discrete nonlinear and non-autonomous model of consensus formation. In: Elaydi, S., et al. (eds.) Communications in Difference Equations, pp. 227–236. Gordon and Breach, Amsterdam (2000)
CrossRef
Google Scholar
Krause, U.: Compromise, consensus, and the iteration of means. Elem. Math. 64, 1–8 (2009)
CrossRef
MATH
MathSciNet
Google Scholar
Krause, U.: Markov chains, Gauss soups, and compromise dynamics. J. Cont. Math. Anal. 44(2), 111–116 (2009)
CrossRef
MATH
MathSciNet
Google Scholar
Krause, U.: Opinion dynamics – local and global. In: Liz, E., Manosa, V. (eds.) Proceedings of the Workshop Future Directions in Difference Equations, pp. 113–119. Universidade de Vigo, Vigo (2011)
Google Scholar
Krause, U.: Positive Dynamical Systems in Discrete Time: Theory, Models, and Applications. Walter de Gruyter (2015)
Google Scholar
Kolokoltsov, V.: Nonlinear Markov Processes and Kinetic Equations. Cambridge University Press (2010)
Google Scholar
Lyubich, Y.I.: Mathematical Structures in Population Genetics. Springer (1992)
Google Scholar
Lu, J., Yu, X., Chen, G., Yu, W.: Complex Systems and Networks: Dynamics. Springer, Controls and Applications (2016)
CrossRef
MATH
Google Scholar
Raftery, A.: A model of high-order Markov chains. J. Roy. Stat. Soc. 47, 528–539 (1985)
MATH
MathSciNet
Google Scholar
Saburov, M.: Ergodicity of nonlinear Markov operators on the finite dimensional space. Non. Anal. Theo. Met. Appl. 143, 105–119 (2016)
CrossRef
MATH
MathSciNet
Google Scholar
Saburov, M., Saburov, Kh: Reaching a consensus in multi-agent systems: A time invariant nonlinear rule. J. Educ. Vocat. Res. 4(5), 130–133 (2013)
MATH
Google Scholar
Saburov, M., Saburov, Kh: Mathematical models of nonlinear uniform consensus. Sci. Asia 40(4), 306–312 (2014)
CrossRef
MATH
Google Scholar
Saburov, M., Saburov, Kh: Reaching a nonlinear consensus: polynomial stochastic operators. Inter. J. Cont. Auto. Sys. 12(6), 1276–1282 (2014)
CrossRef
MATH
MathSciNet
Google Scholar
Saburov, M., Saburov, Kh: Reaching a nonlinear consensus: a discrete nonlinear time-varying case. Inter. J. Sys. Sci. 47(10), 2449–2457 (2016)
CrossRef
MATH
MathSciNet
Google Scholar
Saburov, M., Yusof, N.A.: Counterexamples to the conjecture on stationary probability vectors of the second-order Markov chains. Linear Algebra Appl. 507, 153–157 (2016)
CrossRef
MATH
MathSciNet
Google Scholar
Seneta, E.: Nonnegative Matrices and Markov Chains. Springer-Verlag (1981)
Google Scholar