Agent-Based Models (ABMs) can be used to numerically simulate highly non-linear phenomena that emerge from local interactions of multiple, independent entities. ABMs are often used to understand dynamic processes such as animal migration in ecology and pathogenesis in biomedicine, in which group-level patterns and space-time constraints are of interest. However, high fidelity ABMs, especially those in biomedical applications, usually have a large number of parameters, creating substantial uncertainty and high dimensionality at both local and global levels. Uncertainty analysis (output variance estimation) and sensitivity analysis are essential steps in investigating model robustness. In addition to allocating uncertainty to each parameter, sensitivity analysis can also be used to reduce ABM dimensionality for effective model calibration and optimization. We review common sensitivity analysis methods that have been used to decrease the number of parameters in complex ABMs, highlighting Garg et al. (2019)'s paper, where random forests - a non-parametric ensemble algorithm - are used as a sensitivity analysis method for ranking parameters in a biomedical ABM.
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