The decisionSupport package (Eike Luedeling and Göhring 2017) implements this as a Monte Carlo simulation, which generates a large number of plausible system outcomes, based on random numbers for each input variable that are drawn from user-specified probability distributions. It also conducts a sensitivity analysis (based on Projection-to-Latent-Structures regression) to highlight uncertain variables that have large impacts on model outputs (Eike Luedeling and Gassner 2012; Wold, Sjostrom, and Eriksson 2001). This approach is useful for determining whether a clearly preferable course of action can be delineated based on the present state of knowledge without the need for further information. If the distribution of predicted system outcomes doesn’t imply a clearly preferable decision option, variables identified as carrying decision-relevant uncertainty can then be targeted by decision-supporting research (Eike Luedeling et al. 2015). This approach is explained in more detail below.
Publication year
2022
Authors
Language
English
Keywords
wildfires, decision making, forest fires
