The repository is a working copy of the supplementary for Lanzanova et al. (2018). It demonstrates the use of probabilistic decision analysis. In it we developed a causal impact pathway model based on the local knowledge of expert stakeholders. Input parameters are described by probability distributions derived from estimated confidence intervals. The model was run in a Monte Carlo simulation using the decisionSupport package (Luedeling & Goehring 2018) to generate the range of plausible decision outcomes, quantified as the net present value and the annual cash flow. We used Partial Least Squares regression analysis to identify the parameters that most affected projected intervention outcomes and we computed the Expected Value of Perfect Information (EVPI) to highlight critical uncertainties. Read it as an html.

Lanzanova D, Whitney C, Shepherd K, Luedeling E (2019b) Improving development efficiency through decision analysis: Reservoir protection in Burkina Faso. Environmental Modelling & Software 115:164–175. doi: 10.1016/j.envsoft.2019.01.016

Luedeling E, Goehring L (2018) decisionSupport package version 1.103.7

Improving development efficiency through decision analysis: reservoir protection in Burkina Faso, CGspace

Reservoir management in Burkina Faso - decision analysis repository, Harvard DataVerse

Data for: Improving development efficiency through decision analysis: reservoir protection in Burkina Faso Original Dataset

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