论文标题
从人口动态学习与离散事件模拟模型的个人互动
Learning Individual Interactions from Population Dynamics with Discrete-Event Simulation Model
论文作者
论文摘要
大量数据使研究人员可以追求更强大的计算工具来学习复杂系统的动态，例如神经网络，工程系统和社交网络。传统的机器学习方法通过动态贝叶斯网络和状态空间模型捕获复杂的系统动力学，这很难扩展，因为用稀疏图或微分方程系统开出动力学是非平凡的。或深层神经网络，在其中很难解释学习动力学的分布式表示。在本文中，我们将探讨学习复杂系统动力学的离散事件模拟表示的可能性，假设状态变量的多元正态分布，基于这样的观察，即许多复杂的系统动力学可以分解为局部交互的序列，这些动态可以单独地改变系统状态，仅在系统状态下仅在序列生成复杂和多样的动力学。我们的结果表明，该算法可以在具有有意义事件的几个字段中数据有效地捕获复杂的网络动力学。
The abundance of data affords researchers to pursue more powerful computational tools to learn the dynamics of complex system, such as neural networks, engineered systems and social networks. Traditional machine learning approaches capture complex system dynamics either with dynamic Bayesian networks and state space models, which is hard to scale because it is non-trivial to prescribe the dynamics with a sparse graph or a system of differential equations; or a deep neural networks, where the distributed representation of the learned dynamics is hard to interpret. In this paper, we will explore the possibility of learning a discrete-event simulation representation of complex system dynamics assuming multivariate normal distribution of the state variables, based on the observation that many complex system dynamics can be decomposed into a sequence of local interactions, which individually change the system state only minimally but in sequence generate complex and diverse dynamics. Our results show that the algorithm can data-efficiently capture complex network dynamics in several fields with meaningful events.
