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An Opinion Interactive Model Based on Individual Persuasiveness

机译:基于个人说服力的观点互动模型

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摘要

In order to study the formation process of group opinion in real life, we put forward a new opinion interactive model based on Deffuant model and its improved models in this paper because current models of opinion dynamics lack considering individual persuasiveness. Our model has following advantages: firstly persuasiveness is added to individual's attributes reflecting the importance of persuasiveness, which means that all the individuals are different from others; secondly probability is introduced in the course of interaction which simulates the uncertainty of interaction. In Monte Carlo simulation experiments, sensitivity analysis including the influence of randomness, initial persuasiveness distribution, and number of individuals is studied at first; what comes next is that the range of common opinion based on the initial persuasiveness distribution can be predicted. Simulation experiment results show that when the initial values of agents are fixed, no matter how many times independently replicated experiments, the common opinion will converge at a certain point; however the number of iterations will not always be the same; the range of common opinion can be predicted when initial distribution of opinion and persuasiveness are given. As a result, this model can reflect and interpret some phenomena of opinion interaction in realistic society.
机译:为了研究现实生活中群体意见的形成过程,本文提出了一种基于Deffuant模型及其改进模型的新的意见交互模型,因为当前的意见动力学模型缺乏考虑个人的说服力。我们的模型具有以下优点:首先,说服力被添加到个体的属性中,反映了说服力的重要性,这意味着所有个体都与众不同。其次,在交互过程中引入概率,以模拟交互的不确定性。在蒙特卡洛模拟实验中,首先研究了敏感性分析,包括随机性,初始说服力分布和个人数量的影响;接下来是可以预测基于初始说服力分布的普遍意见的范围。仿真实验结果表明,当代理的初始值固定时,无论独立重复实验多少次,共识都会在某一点收敛。但是,迭代次数不会总是相同的;当给出初步的意见分配和说服力时,可以预测普遍意见的范围。结果,该模型可以反映和解释现实社会中意见互动的一些现象。

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