Impact of interactions on human dynamics

TL;DR

This paper introduces a queueing model incorporating human-human interactions, revealing that such interactions alter the power-law exponents of interevent time distributions in human activity patterns.

Contribution

The study develops a minimal queueing model that accounts for interactions between individuals, providing new insights into the scaling exponents of interevent times.

Findings

Interevent time distributions exhibit exponents of 2, 3/2, and intermediate values.

Interactions influence the power-law exponents in human activity patterns.

The model enables large-scale simulations and reliable estimation of scaling behaviors.

Abstract

Queueing theory has been recently proposed as a framework to model the heavy tailed statistics of human activity patterns. The main predictions are the existence of a power-law distribution for the interevent time of human actions and two decay exponents $\alpha=1$ and $\alpha=3/2$. Current models lack, however, a key aspect of human dynamics, i.e. several tasks require, or are determined by, interactions between individuals. Here we introduce a minimal queueing model of human dynamics that already takes into account human-human interactions. To achieve large scale simulations we obtain a coarse-grained version of the model, allowing us to reach large interevent times and reliable scaling exponents estimations. Using this we show that the interevent distribution of interacting tasks exhibit the scaling exponents $\alpha=2$, 3/2 and a series of numerable values between 3/2 and 1. This work demonstrates that, within the context of queueing models of human dynamics, interactions change the exponent of the power-law distributed interevent times. Beyond the study of human dynamics, these results are relevant to systems where the event of interest consists of the simultaneous occurrence of two (or more) events.