Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6962652 | Environmental Modelling & Software | 2016 | 13 Pages |
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.
Related Topics
Physical Sciences and Engineering
Computer Science
Software
Authors
Lei Gao, Brett A. Bryan, Martin Nolan, Jeffery D. Connor, Xiaodong Song, Gang Zhao,