标题： 加权中位数网络意见模型中的长时和短时动力学 显示英文标题

标题： Long-Time and Short-Time Dynamics in a Weighted-Median Opinion Model on Networks

摘要： 社会互动影响人们的观点。 在某些情况下，这些互动最终会产生共识观点；而在其他情况下，它们可能导致观点的碎片化，并形成不同观点群体，以“回音室”的形式存在。 考虑一个具有连续值标量观点的个体社交网络，并假设他们在相互互动时可以改变自己的观点。 在许多关于网络中个体观点动态的模型中，观点更新通常依赖于相互作用个体的平均观点。 作为一种可能更符合某些情况的替代方案，我们研究了一个观点更新规则依赖于相互作用个体观点加权中位数的观点模型。 通过我们中位数更新观点模型的数值模拟，我们研究了最终观点分布如何依赖于网络结构。 对于配置模型网络，我们还推导了当有无限多个个体时观点分布渐近动态的平均场近似。 我们数值地研究了它在各种网络上的短期观点动态中的准确性。 显示英文摘要

摘要： Social interactions influence people's opinions. In some situations, these interactions eventually yield a consensus opinion; in others, they can lead to opinion fragmentation and the formation of different opinion groups in the form of ``echo chambers''. Consider a social network of individuals with continuous-valued scalar opinions, and suppose that they can change their opinions when they interact with each other. In many models of the opinion dynamics of individuals in a network, it is common for opinion updates to depend on the mean opinion of interacting individuals. As an alternative, which may be more realistic in some situations, we study an opinion model with an opinion-update rule that depends on the weighted median of the opinions of interacting individuals. Through numerical simulations of our median-update opinion model, we investigate how the final opinion distribution depends on network structure. For configuration-model networks, we also derive a mean-field approximation for the asymptotic dynamics of the opinion distribution when there are infinitely many individuals. We numerically investigate its accuracy for short-time opinion dynamics on various networks.