  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.
