An empirical workflow to integrate uncertainty and sensitivity analysis to evaluate agent-based simulation outputs. (September 2018) - Abreu, Carolina G.; Ralha, Celia G.
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) agent-based 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 agent-based simulation outputs. Highlights: We investigate the impacts of experimental design in agent-based model outputs. We present an empirical workflow to integrate uncertainty and sensitivity analysis. The workflow analysis was applied to MASE-BDI simulator with a LUCC model. Uncertainty analysis' minimum sample size relies on securing variance stability. We contribute for the ABM community with a structured approach to ABM outputs.