We present and analyze a model of Opinion Dynamics and Bounded Confidence on the stochastic movement world. There are two mechanisms for interaction. ‘Eyeshot’ limits the set of neighbors around the agent and ‘Bounded Confidence’ chooses the agents to exchange the opinion in the set. Every time step, agent i looks for the agents in its eyeshot and adjusts their opinion based on the algorithm of Bounded Confidence. When the exchange ends, every agent moves itself in a random direction and waits for the next time step. There are three special agents in the model, infector, extremist and leader. The infector is specified as an agent with large eyeshot and the extremist is the agent with high confidence. The leader possesses both high confidence and large eyeshot. We simulated the opinion formation process using the proposed model, results show the system is more realistic than the classic BC model.
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