人类动力学中的爆发和重尾模型
Modeling bursts and heavy tails in human dynamics
摘要：
目前的人类动力学模型，从风险评估到通信，假设人类的行为是随机分布的时间，因此很好地近似泊松过程。我们提供的直接证据表明，五个人类活动模式的个人行动的时间遵循非泊松统计，其特征在于由长期不活动分开的快速发生的事件的爆发。我们表明，人类行为的突发性是一个基于决策的排队过程的结果：当个人执行任务的基础上，一些感知的优先级，任务的时间将是沉重的尾巴，大多数任务被迅速执行，而一些经历了很长的等待时间。我们讨论了两个捕捉人类活动的模型。第一个模型假设个人在任何时候可以处理的任务数量没有限制，预测单个任务的等待时间遵循指数α =3/2的重尾分布。第二个模型对队列长度施加了限制，导致alpha=1。我们提供的实证证据支持这两个模型的相关性，人类活动模式。最后，我们讨论了可能的扩展所提出的模型，并概述了一些未来的挑战，在探索人类动力学的统计机制。
英文摘要：
Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. We provide direct evidence that for five human activity patterns the timing of individual human actions follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. We show that the bursty nature of human behavior is a consequence of a decision based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, most tasks being rapidly executed, while a few experiencing very long waiting times. We discuss two queueing models that capture human activity. The first model assumes that there are no limitations on the number of tasks an individual can hadle at any time, predicting that the waiting time of the individual tasks follow a heavy tailed distribution with exponent alpha=3/2. The second model imposes limitations on the queue length, resulting in alpha=1. We provide empirical evidence supporting the relevance of these two models to human activity patterns. Finally, we discuss possible extension of the proposed queueing models and outline some future challenges in exploring the statistical mechanisms of human dynamics.
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