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On ($3,12^2$), ($4,6,12$) and ($4,8^2$) Archimedean lattices, the critical properties of majority-vote model are considered and studied using the Glauber transition rate proposed by Kwak {it et all.} [Phys. Rev. E, {bf 75}, 061110 (2007)] rather than the traditional majority-vote with noise [Jose Mario de Oliveira, J. Stat. Phys. {bf 66}, 273 (1992)]. The critical temperature and the critical exponents for this transition rate are obtained from extensive Monte Carlo simulations and with a finite size scaling analysis. The calculated values of the critical temperatures Binder cumulant are $T_c=0.363(2)$ and $U_4^*=0.577(4)$; $T_c=0.651(3)$ and $U_4^*=0.612(5)$; and $T_c=0.667(2)$ and $U_4^*=0.613(5)$ for ($3,12^2$), ($4,6,12$) and ($4,8^2$) lattices, respectively. The critical exponents $beta/ u$, $gamma/ u$ and $1/ u$ for this model are $0.237(6)$, $0.73(10)$, and $ 0.83(5)$; $0.105(8)$, $1.28(11)$, and $1.16(5)$; $0.113(2)$, $1.60(4)$, and $0.84(6)$ for ($3,12^2$), ($4,6,12$) and ($4,8^2$) lattices, respectively. These results differ from the usual Ising model results and the majority-vote model on so-far studied regular lattices or complex networks.
We study a nonequilibrium model with up-down symmetry and a noise parameter $q$ known as majority-vote model of M.J. Oliveira 1992 with heterogeneous agents on square lattice. By Monte Carlo simulations and finite-size scaling relations the critical
The majority-vote model with noise is one of the simplest nonequilibrium statistical model that has been extensively studied in the context of complex networks. However, the relationship between the critical noise where the order-disorder phase trans
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We generalize the original majority-vote model by incorporating an inertia into the microscopic dynamics of the spin flipping, where the spin-flip probability of any individual depends not only on the states of its neighbors, but also on its own stat
The dynamics of opinion formation in a society is a complex phenomenon where many variables play an important role. Recently, the influence of algorithms to filter which content is fed to social networks users has come under scrutiny. Supposedly, the