论文标题
具有个性化信息的选民模型中两极分化的出现
Emergence of polarization in a voter model with personalized information
论文作者
论文摘要
在线社交网络的推荐算法中，假新闻的蓬勃发展受到青睐，这些算法基于以前的用户活动，提供了适合其偏好的内容，因此可以创建过滤器气泡。我们介绍了一个具有个性化信息的可分析选民模型，其中外部领域倾向于将代理人的意见与她过去更经常持有的代理人保持一致。尽管它很简单，但我们的模型表现出令人惊讶的丰富动态。通过数值模拟确认的分析平均场方法使我们能够构建一个相图并预测是否达成共识以及如何达到共识。值得注意的是，仅对于与个性化信息的弱相互作用以及代理数量低于阈值，才能避免极化。我们通过分析计算此临界大小，这取决于以强烈的线性方式的相互作用概率。
The flourishing of fake news is favored by recommendation algorithms of online social networks which, based on previous users activity, provide content adapted to their preferences and so create filter bubbles. We introduce an analytically tractable voter model with personalized information, in which an external field tends to align the agent opinion with the one she held more frequently in the past. Our model shows a surprisingly rich dynamics despite its simplicity. An analytical mean-field approach, confirmed by numerical simulations, allows us to build a phase diagram and to predict if and how consensus is reached. Remarkably, polarization can be avoided only for weak interaction with the personalized information and if the number of agents is below a threshold. We analytically compute this critical size, which depends on the interaction probability in a strongly non linear way.
