跳至正文
返回文库全部文档

趋势跟随者如何影响随机网络中的级联

文章 arXiv papers · 作者: Teruyoshi Kobayashi
阅读原文

总结

本文扩展了集体行为阈值模型，纳入代表趋势跟随者的全局节点。在标准的局部级联中，节点会因邻居而激活；在扩展模型中，部分节点也会响应已激活节点的总体比例。该模型考察了行为在网络中传播时，局部影响与群体整体趋势如何相互作用。

分析发现，全局节点可以在趋势形成后加速级联，同时也会降低该趋势最初形成的可能性。因此，它们的总体影响可能是促进或抑制级联；模型表明，趋势跟随者占比处于中间水平时，平均级联规模可能最大。这是关于集体行为的理论网络结果，并非交易信号或市场策略的证据。所提供的描述没有说明是否进行了实证校准，也未表明该结果适用于真实金融市场。

核心观点

  • 模型加入了激活状态取决于群体中活跃节点占比的节点。
  • 趋势出现后，全局趋势跟随行为可能加速级联。
  • 趋势跟随者也可能降低趋势形成的可能性。
  • 在该模型中，趋势跟随者占比处于中间水平时，平均级联规模可能最大。
  • 该描述给出的是理论结果，没有直接交易表现的证据。

标签

统计学趋势跟踪市场情绪
全文
# Trend-driven information cascades on random networks


# Trend-driven information cascades on random networks









Threshold models of global cascades have been extensively used to model real-world collective behavior, such as the contagious spread of fads and the adoption of new technologies. A common property of those cascade models is that a vanishingly small seed fraction can spread to a finite fraction of an infinitely large network through local infections. In social and economic networks, however, individuals' behavior is often influenced not only by what their direct neighbors are doing, but also by what the majority of people are doing as a trend. A trend affects individuals' behavior while individuals' behavior creates a trend. To analyze such a complex interplay between local- and global-scale phenomena, I generalize the standard threshold model by introducing a new type of node, called \textit{global nodes} (or \textit{trend followers}), whose activation probability depends on a global-scale trend; specifically the percentage of activated nodes in the population. The model shows that global nodes play a role as accelerating cascades once a trend emerges while reducing the probability of a trend emerging. Global nodes thus either facilitate or inhibit cascades, suggesting that a moderate share of trend followers may maximize the average size of cascades.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。