Time-Varying Priority Queuing Models for Human Dynamics

TL;DR

This paper introduces a time-varying priority queuing model to better understand human task execution, revealing how changing priorities affect waiting time distributions and aligning with real-world data.

Contribution

It develops an analytical framework for priority queues with dynamically changing task priorities, extending previous static models.

Findings

Bimodal and unimodal waiting time distributions with power-law tails identified.

Model aligns with empirical data from arXiv and Physical Review journals.

Provides insights into realistic human task execution dynamics.

Abstract

Queuing models provide insight into the temporal inhomogeneity of human dynamics, characterized by the broad distribution of waiting times of individuals performing tasks. We study the queuing model of an agent trying to execute a task of interest, the priority of which may vary with time due to the agent's "state of mind." However, its execution is disrupted by other tasks of random priorities. By considering the priority of the task of interest either decreasing or increasing algebraically in time, we analytically obtain and numerically confirm the bimodal and unimodal waiting time distributions with power-law decaying tails, respectively. These results are also compared to the updating time distribution of papers in the arXiv.org and the processing time distribution of papers in Physical Review journals. Our analysis helps to understand human task execution in a more realistic scenario.