Paper detail

A stochastic opinion dynamics model with domain size dependent dynamic evolution

We introduce a stochastic model of binary opinion dynamics in one dimension. The binary opinions $\pm 1$ are analogous to up and down Ising spins and in the equivalent spin system, only the spins at the domain boundary can flip. The probability that a spin at the boundary is up is taken as $P_{up} = \frac {s_{up}} {s_{up} + δs_{down}}$ where $s_{up} (s_{down})$ denotes the size of the domain with up (down) spins neighbouring it. With $x$ fraction of up spins initially, a phase transition is observed in terms of the exit probability and the phase boundary is obtained in the $δ-x$ plane. In addition, we investigate the coarsening behaviour starting from a completely random state; conventional scaling is observed only at the phase transition point $δ= 1$. The scaling behaviour is compared to other dynamical phenomena; the model apparently belongs to a new dynamical universility class as far as persistence is concerned although the dynamical exponent, equal to one, is identical to a similar model with no stochasticity.