Robust global sensitivity analysis under deep uncertainty via scenario analysis |
| |
Affiliation: | 1. The University of Ngaoundéré, University Institute of Technology of Ngaoundéré, Department of Computer Engineering, Laboratory of Analysis, Simulations, and Tests, Ngaoundéré, P.O. Box 455, Cameroon;2. The University of Yaoundé 1, Faculty of Science, Department of Physics, Laboratory of Biophysics, Yaoundé P.O. Box 812, Cameroon;3. The University of Yaoundé 1, Higher Technical Teacher’s Training College, Department of Maintenance, Ebolowa P.O. Box 886, Cameroon;4. African Center for Advanced Studies, Yaoundé, P.O. Box 4477, Cameroon;5. The Central University of Punjab, School of Basic and Applied Sciences, Department of Mathematics and Statistics, Bathinda, Punjab, 151001, India |
| |
Abstract: | Complex social-ecological systems models typically need to consider deeply uncertain long run future conditions. The influence of this deep (i.e. incalculable, uncontrollable) uncertainty on model parameter sensitivities needs to be understood and robustly quantified to reliably inform investment in data collection and model refinement. Using a variance-based global sensitivity analysis method (eFAST), we produced comprehensive model diagnostics of a complex social-ecological systems model under deep uncertainty characterised by four global change scenarios. The uncertainty of the outputs, and the influence of input parameters differed substantially between scenarios. We then developed sensitivity indicators that were robust to this deep uncertainty using four criteria from decision theory. The proposed methods can increase our understanding of the effects of deep uncertainty on output uncertainty and parameter sensitivity, and incorporate the decision maker's risk preference into modelling-related activities to obtain greater resilience of decisions to surprise. |
| |
Keywords: | Global sensitivity analysis Robust sensitivity analysis eFAST Decision theory Land use change Deep uncertainty |
本文献已被 ScienceDirect 等数据库收录! |
|