# Uncertainty and Sensitivity Analyses Methods for Agent-Based Mathematical Models: An Introductory Review

arXiv: https://arxiv.org/abs/1911.08429 · HTML: https://mlsanity.com/paper/1911.08429/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Nov 2019.

## Built on by (most cited first, 1 of 1)

- [SSRCA: a novel machine learning pipeline to perform sensitivity analysis for agent-based models](https://mlsanity.com/paper/2506.00168/) (2025)

## Abstract (arXiv)

Multiscale, agent-based mathematical models of biological systems are often associated with model uncertainty and sensitivity to parameter perturbations. Here, three uncertainty and sensitivity analyses methods, that are suitable to use when working with agent-based models, are discussed. These methods are namely Consistency Analysis, Robustness Analysis and Latin Hypercube Analysis. This introductory review discusses origins, conventions, implementation and result interpretation of the aforementioned methods. Information on how to implement the discussed methods in MATLAB is included.