An empirical workflow to integrate uncertainty and sensitivity analysis to evaluate agent-based simulation outputs

Authored by Carolina G Abreu, Celia G Ralha

Date Published: 2018

DOI: 10.1016/j.envsoft.2018.06.013

Sponsors: Brazilian National Council for Scientific and Technological Development (CNPq)

Platforms: Python

Model Documentation: Other Narrative Flow charts

Model Code URLs: https://gitlab.com/InfoKnow/MASE/MASE-BDI/SourceCode

Abstract

This paper presents an empirical study comparing different uncertainty analysis (UA) and sensitivity analysis (SA) methods, focussing their usefulness for the output analysis of land use/land cover change (LUCC) agentbased models (ABMs). As a result, a workflow to integrate UA and SA is presented to evaluate ABMs outputs. We developed a baseline scenario and performed a comprehensive investigation of the impacts that differences in sample sizes, sample techniques, and SA methods may have on the model output. The analysis is done in the context of a particular agent-based simulator with a LUCC model in a Brazilian Cerrado case study. The experiments indicate that there are known challenges to be overcome by the use of statistical methods. Even though the presented analysis was done over a particular simulator, we intend to contribute to the community that understands the importance of statistical validation techniques to improve the level of confidence in agentbased simulation outputs.

Tags

Agent-based model models Dynamics Land-use change Sensitivity Analysis Validation systems Model land-cover change Uncertainty analysis Convergence Future Cover change Monte-carlo World Indexes Spatial simulation Land use cover change Input