基于自适应兴趣的人体动力学建模
Modeling Human Dynamics with Adaptive Interest
摘要：
近年来，越来越多的经验证据表明，人类各种行为的时间间隔分布中广泛存在重尾现象。基于排队论，巴拉巴西模型及其变体表明，最高优先级优先协议是这些重尾的潜在来源。然而，一些人类活动模式，也显示了重尾的时间统计，不能用基于任务的机制来解释。本文不同于主流的模型，提出了一种基于兴趣的模型。模拟和分析表明，事件间隔时间服从指数为-1的幂律分布，这与人类启动系统的一些经验观测结果相一致。
英文摘要：
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 Barabási 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.
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