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arXiv 2211.13601physics.soc-phmath.DSmath.PR
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具有随机参数化的意见动力学模型

Models of Opinion Dynamics with Random Parametrisation

Gabor Toth

AI总结:

本文分析广义Galam二元意见动力学模型中随机翻转参数的影响,区分稳定数学性质与罕见特征,并给出小群体精确数值结果及大群体极限定理。

摘要:

我们分析了一个广义的Galam二元意见动力学模型,在该模型中,迭代讨论发生在个体的局部群体中,并研究偏离群体多数意见的随机偏差的影响。偏离或翻转的概率取决于多数意见的幅度。根据给出偏离概率的翻转参数值,该模型表现出多种多样的行为。我们关注当翻转参数本身按照某种概率分布随机选取时模型的特征。这些特征的例子包括:大多数和持平状态是吸引子还是排斥子,或者模型动力学中不动点的数量。模型的哪些特征可能显现?哪些特征不太可能出现,因为它们相对于翻转参数的分布仅表现为低概率事件?对这些问题的回答使我们能够区分在关于翻转参数分布的各种假设下保持稳定的数学性质与非常罕见因而更多具有理论而非实际意义的特征。在本文中,我们针对特定的翻转参数分布和小型讨论群体给出了精确的数值结果,并以大型讨论群体的极限定理形式给出了严格的结果。小型讨论群体模拟朋友或工作群体——即彼此认识并经常共度时光的人。大型群体代表社交媒体或政治实体(如城市、州或国家)等场景。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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