Understanding the Heavy Tailed Dynamics in Human Behavior
Understanding the Heavy Tailed Dynamics in Human Behavior
复制标题
DOI：
10.1103/physreve.91.062809
复制
发表时间：
2015-05
期刊：
影响因子：
--
通讯作者：
Gordon J. Ross;Tim Jones
中科院分区：
文献类型：
--
作者：
Gordon J. Ross;Tim Jones
最近，包含大量通信数据的电子数据集的可用性使得在比以往任何时候都更大的规模上研究人类行为成为可能。由此，已经发现，在不同范围的数据集上，连续通信事件之间的间隔时间服从重尾幂律动力学。解释这一点已被证明是有争议的，并出现了两个不同的假设。第一种观点认为，这些幂律是基本的，并且源于人类用来调度任务的优先级排队等机制。第二种观点认为，它们是统计上的假象，只有在忽略昼夜节律和突发性等特征时，才会出现在聚合数据中。我们使用了一个大型的社交媒体数据集来测试这些假设，并发现，尽管结合昼夜节律和突发性的模型确实解释了部分观察到的重尾现象，但仍存在剩余的无法解释的重尾行为，这表明了一个更根本的原因。在此基础上，我们开发了一个人类行为的定量模型，改进了现有的方法，并深入了解人类互动的机制。
The recent availability of electronic data sets containing large volumes of communication data has made it possible to study human behavior on a larger scale than ever before. From this, it has been discovered that across a diverse range of data sets, the interevent times between consecutive communication events obey heavy-tailed power law dynamics. Explaining this has proved controversial, and two distinct hypotheses have emerged. The first holds that these power laws are fundamental, and arise from the mechanisms such as priority queuing that humans use to schedule tasks. The second holds that they are statistical artifacts which only occur in aggregated data when features such as circadian rhythms and burstiness are ignored. We use a large social media data set to test these hypotheses, and find that although models that incorporate circadian rhythms and burstiness do explain part of the observed heavy tails, there is residual unexplained heavy-tail behavior which suggests a more fundamental cause. Based on this, we develop a quantitative model of human behavior which improves on existing approaches and gives insight into the mechanisms underlying human interactions.