Uncertainty Quantification for Agent Based Models: A Tutorial

TL;DR

This tutorial demonstrates how advanced uncertainty quantification techniques like Gaussian processes and history matching can improve the robustness and predictive accuracy of agent-based models, exemplified through a predator-prey simulation.

Contribution

It introduces and applies sophisticated statistical methods for uncertainty quantification specifically to agent-based models, providing a practical tutorial and demonstrating their effectiveness.

Findings

Uncertainty quantification methods improve model robustness.

Advanced statistical techniques enhance predictive accuracy.

Methods are effective in computationally expensive ABMs.

Abstract

We explore the application of uncertainty quantification methods to agent-based models (ABMs) using a simple sheep and wolf predator-prey model. This work serves as a tutorial on how techniques like emulation can be powerful tools in this context. We also highlight the importance of advanced statistical methods in effectively utilising computationally expensive ABMs. Specifically, we implement stochastic Gaussian processes, Gaussian process classification, sequential design, and history matching to address uncertainties in model input parameters and outputs. Our results show that these methods significantly enhance the robustness, accuracy, and predictive power of ABMs.