使用机器学习仿真基于Agent的仿真
Using Machine Learning to Emulate Agent-Based Simulations
摘要：
在这项概念验证工作中，我们评估了多种机器学习方法作为统计仿真器的性能，用于分析基于代理的模型（ABM）。分析ABM输出可能具有挑战性，因为即使在相对简单的模型中，输入参数之间的关系也可能是非线性的，甚至是混乱的，并且每个模型运行可能需要大量的CPU时间。统计仿真，其中的反弹道导弹的统计模型的构建，以方便详细的模型分析，已被提出作为一种替代计算昂贵的蒙特卡罗方法。在这里，我们比较了ABM仿真的多种机器学习方法，以确定最适合仿真ABM复杂行为的方法。我们的研究结果表明，在大多数情况下，人工神经网络（ANN）和梯度提升树优于高斯过程仿真器，目前最常用的方法来模拟复杂的计算模型。人工神经网络在模型运行次数较多的情况下产生了最准确的模型复制，尽管训练时间比其他方法长。我们建议基于代理的建模将受益于使用机器学习方法进行仿真，因为这可以促进模型的更稳健的灵敏度分析，同时在校准和分析仿真时减少CPU时间消耗。
英文摘要：
In this proof-of-concept work, we evaluate the performance of multiple machine-learning methods as statistical emulators for use in the analysis of agent-based models (ABMs). Analysing ABM outputs can be challenging, as the relationships between input parameters can be non-linear or even chaotic even in relatively simple models, and each model run can require significant CPU time. Statistical emulation, in which a statistical model of the ABM is constructed to facilitate detailed model analyses, has been proposed as an alternative to computationally costly Monte Carlo methods. Here we compare multiple machine-learning methods for ABM emulation in order to determine the approaches best suited to emulating the complex behaviour of ABMs. Our results suggest that, in most scenarios, artificial neural networks (ANNs) and gradient-boosted trees outperform Gaussian process emulators, currently the most commonly used method for the emulation of complex computational models. ANNs produced the most accurate model replications in scenarios with high numbers of model runs, although training times were longer than the other methods. We propose that agent-based modelling would benefit from using machine-learning methods for emulation, as this can facilitate more robust sensitivity analyses for the models while also reducing CPU time consumption when calibrating and analysing the simulation.
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