Towards the understanding of human dynamics

TL;DR

This paper reviews recent empirical and theoretical research on human activity patterns, highlighting the shift from Poisson-based models to those accounting for heavy-tailed, non-Poisson statistics in various human behaviors.

Contribution

It provides a comprehensive summary of recent findings and models that challenge traditional Poisson assumptions in human dynamics, emphasizing non-stationary, heavy-tailed distributions.

Findings

Human activity patterns follow heavy-tailed distributions.

Traditional Poisson models are insufficient for describing human dynamics.

Recent models better capture the complexity of human behaviors.

Abstract

Quantitative understanding of human behaviors provides elementary comprehension of the complexity of many human-initiated systems. A basic assumption embedded in the previous analyses on human dynamics is that its temporal statistics are uniform and stationary, which can be properly described by a Poisson process. Accordingly, the interevent time distribution should have an exponential tail. However, recently, this assumption is challenged by the extensive evidence, ranging from communication to entertainment and work patterns, that the human dynamics obeys non-Poisson statistics with heavy-tailed interevent time distribution. This review article summarizes the recent empirical explorations on human activity pattern, as well as the corresponding theoretical models for both task-driven and interest-driven systems. Finally, we outline some future open questions in the studies of the statistical mechanics of human dynamics.