Modeling Human Temporal Dynamics in Agent-Based Simulations

James Flamino, Weike Dai, B. Szymanski
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引用次数: 2

Abstract

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.
基于agent的仿真中人类时间动态建模
基于时间的习惯性行为在人类中普遍存在。考虑到睡眠对我们的日常活动有如此固有的影响,为了解其影响程度而对睡眠周期模式进行建模,也使我们能够捕捉到可用于预测措施的稳定行为特征。在本文中,我们展示了时间偏好模式在几个真实世界数据集的用户之间是一致和有弹性的。此外,我们将这些模式集成到大规模基于代理的模型中,以模拟相关数据集中用户的活动,以验证预测的准确性。下面的模拟表明,将基于时间的行为的聚类特征结合到基于代理的模型中,不仅可以显著减少计算开销,而且还可以获得与基线模型相当的预测精度。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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