Modeling Human Temporal Dynamics in Agent-Based Simulations

Time-based habitual behavior is exhibited in humans globally. Given that sleep has such an innate influence on our daily activities, modeling the patterns of the sleep cycle in order to understand the extent of its impact allows us to also capture stable behavioral features that can be utilized for predictive measures. In this paper we show that patterns of temporal preference are consistent and resilient across users of several real-world datasets. Furthermore, we integrate those patterns into large-scale agent-based models to simulate the activity of users in the involved datasets to validate predictive accuracy. Following simulations reveal that incorporating clustering features based on time-based behavior into agent-based models not only result in a significant decrease in computational overhead, but also result in predictive accuracy comparable to the baseline models.