Modeling Human Dynamics with Adaptive Interest

TL;DR

This paper introduces an interest-based model to explain heavy-tailed interevent times in human behaviors, offering an alternative to task-based models and aligning with empirical data.

Contribution

It proposes a novel interest-driven framework for modeling human dynamics, expanding beyond traditional task-priority approaches.

Findings

The model produces a power-law interevent time distribution with exponent -1.

Simulation results match empirical observations of human activity patterns.

The approach explains heavy tails not accounted for by task-based models.

Abstract

Recently, increasing empirical evidence indicates the extensive existence of heavy tails in the interevent time distributions of various human behaviors. Based on the queuing theory, the Barab\'asi model and its variations suggest the highest-priority-first protocol a potential origin of those heavy tails. However, some human activity patterns, also displaying the heavy-tailed temporal statistics, could not be explained by a task-based mechanism. In this paper, different from the mainstream, we propose an interest-based model. Both the simulation and analysis indicate a power-law interevent time distribution with exponent -1, which is in accordance with some empirical observations in human-initiated systems.