论文标题
具有随机参数化的意见动力学模型
Models of Opinion Dynamics with Random Parametrisation
论文作者
论文摘要
我们分析了二元观点动力学的GALAM模型的概括,在该模型中,迭代讨论在当地的个体群体中进行,并研究与多数群体的随机偏差的影响。偏差或翻转的概率取决于多数的幅度。根据给出偏差概率的翻转参数的值,该模型显示了各种各样的行为。当flip参数本身在某些概率分布之后,我们对模型的特征感兴趣。这些特征的示例是大多数和纽带是吸引子还是驱动器,还是模型动力学中的固定点的数量。该模型的哪个功能可能会出现?哪些不太可能是因为它们仅作为针对翻转参数的分布的低概率事件的存在?此类问题的答案使我们能够区分在各种假设下稳定的数学属性,这些假设是关于翻转参数的分布与特征的分布,这些特征非常罕见,因此比实际利益更重要。在本文中,我们介绍了FLIP参数的特定分布和小讨论组的特定分布的确切数值结果,并以大型讨论组的极限定理的形式进行了严格的结果。小型讨论小组模拟朋友或工作组 - 那些亲自认识并经常花时间在一起的人。大型团体代表社交媒体或政治实体,例如城市,州或国家等场景。
We analyse a generalisation of the Galam model of binary opinion dynamics in which iterative discussions take place in local groups of individuals and study the effects of random deviations from the group majority. The probability of a deviation or flip depends on the magnitude of the majority. Depending on the values of the flip parameters which give the probability of a deviation, the model shows a wide variety of behaviour. We are interested in the characteristics of the model when the flip parameters are themselves randomly selected, following some probability distribution. Examples of these characteristics are whether large majorities and ties are attractors or repulsors, or the number of fixed points in the dynamics of the model. Which of the features of the model are likely to appear? Which ones are unlikely because they only present as events of low probability with respect to the distribution of the flip parameters? Answers to such questions allow us to distinguish mathematical properties which are stable under a variety of assumptions on the distribution of the flip parameters from features which are very rare and thus more of theoretical than practical interest. In this article, we present both exact numerical results for specific distributions of the flip parameters and small discussion groups and rigorous results in the form of limit theorems for large discussion groups. Small discussion groups model friend or work groups -- people that personally know each other and frequently spend time together. Large groups represent scenarios such as social media or political entities such as cities, states, or countries.