Maximizing the Effect of Local Disturbance in the Dynamics of Opinion Formation

The dynamics of opinion formation process in a social network is of great interest for many non-equilibrium systems, such as election, competition of market share in advertising etc. By introducing local disturbance in the social network, such as the implantation of an agent, we can use numerical simulation to measure the effect of this agent on the result of the election, which has a deadline. By extending the statistical physics of damage spreading in spin models on lattice to social network, we investigate the effect of one agent on a two-party election on the time to dominance as a function of the given time to the deadline of the election. We find that certain rewiring mechanism of the social network will enhance the speed to dominance by the party that implant the agent. Using genetic algorithm, we also find good methods of rewiring that can greatly increase the efficiency of the agent. Our model is an Ising model defined on a Watts-Strogatz network. We perform Monte Carlo simulations on the effect of interaction and use a genetic algorithm with a mutation matrix to find the best way of rewiring to amplify the effect of the agent in influencing the result of the election. We also discuss the general topological feature of an optimal rewiring condition in maximizing the effect of the local disturbance in opinion formation.