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Using Machine Learning to Emulate Agent-Based Simulations
[article]
2021
arXiv
pre-print
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
arXiv:2005.02077v2
fatcat:rfwgotphkfgllo2pesvw6z3e6y