INRES-Horticultural Sciences, University of Bonn, Auf dem Huegel 6, 53121 Bonn, Germany

Many of the variables decision makers need to consider in development cannot be precisely quantified, at least not without unreasonable effort. The major objective of (prescriptive) decision analysis is to support the decision-making processes when faced with this problem (E. Luedeling and Shepherd 2016). Decision analysis can make forecasts of decision outcomes without precise numbers.

The decisionSupport package (Eike Luedeling et al. 2024) implements this as a Monte Carlo simulation, which generates a large number of plausible system outcomes, based on random numbers for each input variable, drawn from user-specified probability distributions.

- an R function that predicts decision outcomes based on the variables
named in a separate data table. This R function is customized by the
user to address a particular decision problem to provide the decision
analysis model.

- an input table (in .csv format) specifying the names and probability distributions for all variables used in the decision model. These distributions aim to represent the full range of possible values for each component of the model.

These two inputs are provided as arguments to the
`mcSimulation`

function, which conducts a Monte Carlo
analysis with repeated model runs based on probability distributions for
all uncertain variables. The data table and model are customized to fit
the particulars of a specific decision.

`decisionSupport`

package can be installed from CRAN with
the `install.packages()`

function.

The package can be loaded with the `library()`

function
for further use in this example.

We showcase a partial example of a Monte-Carlo-based selection of a sedimentation management strategy for a reservoir in the Upper Volta River Basin of Burkina Faso (Lanzanova et al. 2019). These reservoirs have multiple benefits for rural communities and are important for food security and livelihoods. Sedimentation is a major impediment for the functioning of these reservoirs. The design of an efficient sedimentation management intervention requires assessment of multiple uncertain quantities and risks. Decision analysis is highly applicable in such data-scarce environments, where science has traditionally struggled to provide actionable information to policy-makers, development practitioners, NGOs and rural communities.

Here we walk through a simple example of the use of various
decisionSupport functions, which use the `tidyverse`

libraries (Wickham et al. 2019) including
`ggplot2`

(Wickham et al.
2024), `plyr`

(Wickham
2023) and `dplyr`

(Wickham et
al. 2023) among others in the R programming language (R Core Team 2023).

Here we generate a model as a function using
`decisionSupport`

library. The example is a simple version of
the model applied in Burkina Faso (Lanzanova et
al. 2019). We use the `decisionSupport`

functions
`vv()`

to produce time series with variation from a
pre-defined mean and coefficient of variation,
`chance_event()`

to simulate whether events occur and
`discount()`

to discount values along a time series.

```
example_decision_function <- function(x, varnames){
# calculate ex-ante risks: impact the implementation of interventions ####
intervention_NonPopInvolvEvent <-
chance_event(intervention_NonPopInvolv, 1, 0, n = 1)
# pre-calculation of common random draws for all intervention model runs ####
# profits from Tropical Livestock Units (TLU)
TLU <- vv(TLU_no_intervention, var_CV, n_years)
TLU_profit <- vv(profit_per_TLU, var_CV, n_years)
# benefits of fruit
precalc_intervention_fruit_benefits <-
vv(intervention_fruit_area_ha, var_CV, n_years) *
vv(intervention_fruit_yield_t_ha, var_CV, n_years) *
vv(intervention_fruit_profit_USD_t, var_CV, n_years)
# benefits of vegetables
precalc_intervention_vegetable_benefits <-
vv(intervention_vegetable_area_ha, var_CV, n_years) *
vv(intervention_vegetable_yield_t_ha, var_CV, n_years) *
vv(intervention_vegetable_profit_USD_t, var_CV, n_years)
# benefits of rainfed crops
precalc_intervention_rainfed_crop_benefits <-
vv(intervention_rainfed_crop_area_ha, var_CV, n_years) *
vv(intervention_rainfed_crop_yield_t_ha, var_CV, n_years) *
vv(intervention_rainfed_crop_profit_USD_t, var_CV, n_years)
# Intervention ####
for (decision_intervention_strips in c(FALSE,TRUE))
{
if (decision_intervention_strips)
{
intervention_strips <- TRUE
intervention_strips_PlanningCost <- TRUE
intervention_strips_cost <- TRUE
} else
{
intervention_strips <- FALSE
intervention_strips_PlanningCost <- FALSE
intervention_strips_cost <- FALSE
}
if (intervention_NonPopInvolvEvent) {
intervention_strips <- FALSE
intervention_strips_cost <- FALSE
}
# Costs ####
if (intervention_strips_cost) {
cost_intervention_strips <-
intervention_adaptation_cost + intervention_tech_devices_cost + intervention_nursery_cost +
intervention_wells_cost +
intervention_training_cost + intervention_mngmt_oprt_cost + intervention_mngmt_follow_cost +
intervention_mngmt_audit_cost
} else
cost_intervention_strips <- 0
if (intervention_strips_PlanningCost) {
plan_cost_intervention_strips <-
intervention_communication_cost + intervention_zoning_cost
} else
plan_cost_intervention_strips <- 0
maintenance_cost <- rep(0, n_years)
if (intervention_strips)
maintenance_cost <-
maintenance_cost + vv(maintenance_intervention_strips, var_CV, n_years)
intervention_cost <- maintenance_cost
intervention_cost[1] <-
intervention_cost[1] + cost_intervention_strips + plan_cost_intervention_strips
# Benefits from cultivation in the intervention strips ####
intervention_fruit_benefits <-
as.numeric(intervention_strips) * precalc_intervention_fruit_benefits
intervention_vegetable_benefits <-
as.numeric(intervention_strips) * precalc_intervention_vegetable_benefits
intervention_rainfed_crop_benefits <-
as.numeric(intervention_strips) * precalc_intervention_rainfed_crop_benefits
# Total benefits from crop production (agricultural development and riparian zone) ####
crop_production <-
intervention_fruit_benefits +
intervention_vegetable_benefits +
intervention_rainfed_crop_benefits
# Benefits from livestock ####
# The following allows considering that intervention strips may
# restrict access to the reservoir for livestock.
if (intervention_strips)
TLU_intervention <-
TLU * (1 + change_TLU_intervention_perc / 100)
else
TLU_intervention <- TLU
if (decision_intervention_strips){
livestock_benefits <- TLU_intervention * TLU_profit
total_benefits <- crop_production + livestock_benefits
net_benefits <- total_benefits - intervention_cost
result_interv <- net_benefits}
if (!decision_intervention_strips){
livestock_benefits <- TLU_no_intervention * TLU_profit
total_benefits <- livestock_benefits
net_benefits <- total_benefits - intervention_cost
result_n_interv <- net_benefits}
} #close intervention loop bracket
NPV_interv <-
discount(result_interv, discount_rate, calculate_NPV = TRUE)
NPV_n_interv <-
discount(result_n_interv, discount_rate, calculate_NPV = TRUE)
# Beware, if you do not name your outputs (left-hand side of the equal sign) in the return section,
# the variables will be called output_1, _2, etc.
return(list(Interv_NPV = NPV_interv,
NO_Interv_NPV = NPV_n_interv,
NPV_decision_do = NPV_interv - NPV_n_interv,
Cashflow_decision_do = result_interv - result_n_interv))
}
```

Using the model function above, we can perform a Monte Carlo
simulation with the `mcSimulation()`

function from
`decisionSupport`

. This function generates distributions of
all variables in the input table as well as the specified model outputs
(see `return()`

function above) by calculating random draws
in our defined `example_decision_function()`

. Make sure that
all the variables in the input table are included in the model
(erroneous variables listed there can cause issues with some of the
post-hoc analyses).

The `numberOfModelRuns`

argument is an integer indicating
the number of model runs for the Monte Carlo simulation. Unless the
model function is very complex, 10,000 runs is a reasonable choice (for
complex models, 10,000 model runs can take a while, so especially when
the model is still under development, it often makes sense to use a
lower number).

We can use the `plot_distributions()`

function to produce
one of the several plotting options for distribution outputs. This shows
us an overlay of the full results of the Monte Carlo model of the
decision options, i.e. the expected NPV if we choose to do the
intervention `Interv_NPV`

or not do the intervention
`NO_Interv_NPV`

.

```
decisionSupport::plot_distributions(mcSimulation_object = mcSimulation_results,
vars = c("Interv_NPV", "NO_Interv_NPV"),
method = 'smooth_simple_overlay',
base_size = 7)
```

We use the same function to show the distributions of the ‘do’ and
‘do not do’ decision scenarios as boxplots. This can be useful when
comparing multiple outputs by illustrating the spread of the data
resulting from the decision model. Boxplots show the median (central
line), the 25^{th} and 75^{th} percentiles (sides of
boxes) and any outliers (light circles outside of boxes).

```
decisionSupport::plot_distributions(mcSimulation_object = mcSimulation_results,
vars = c("Interv_NPV",
"NO_Interv_NPV"),
method = 'boxplot')
```

We can use the same function for the value of the decision (difference in NPV between do and do not do). This is more helpful for us since it shows us the outcome distribution of the decision itself.

Here we plot the distribution of annual cashflow over the entire
simulated period for the intervention. For this we use the
`plot_cashflow()`

function which uses the specified cashflow
outputs from the `mcSimulation()`

function (in our case
`Cashflow_decision_do`

) to show cashflow over time.

We apply a post-hoc analysis to the `mcSimulation()`

outputs with `plsr.mcSimulation()`

to determine the Variable
Importance in the Projection (VIP) score and coefficients of a
Projection to Latent Structures (PLS) regression model. This function
uses the outputs of the `mcSimulation()`

selecting all the
input variables from the decision analysis function in the parameter
`object`

and then runs a PLS regression with an outcome
variable defined in the parameter `resultName`

. We use the
code `names(mcSimulation_results$y)[3]`

to select the outcome
variable NPV_decision_do, which is the third element of the list
`y`

in our `mcSimulation_results`

outputs (this
must be a character element).

```
pls_result <- plsr.mcSimulation(object = mcSimulation_results,
resultName = names(mcSimulation_results$y)[3], ncomp = 1)
```

We run the `plot_pls()`

on the results from
`plsr.mcSimulation()`

with a number of standard settings. The
length of the bars is equal to VIP with a vertical line at ‘1’ on the
x-axis indicating a standard cut-off for VIP used for variable selection
(Whitney et al. 2017). The overall plot
only shows those variables with a VIP > 0.8, which is the common
threshold for variable selection E. Luedeling and
Shepherd (2016). The colors of the bars represent the positive or
negative coefficient of the given input variable with the output
variable.

Here we import the input table again to replace the labels for the
variables on the y-axis. The input table can include a ‘label’ and
‘variable’ column. The standard labels (from the ‘variable’ column) are
usually computer readable and not very nice for a plot. The
`plot_pls()`

function uses the text in the ‘label’ column as
replacement for the default text in the ‘variable’ column.

We calculate Value of Information (VoI) analysis with the Expected Value of Perfect Information (EVPI). EVPI measures the expected opportunity loss that is incurred when the decision-maker does not have perfect information about a particular variable. EVPI is determined by examining the influence of that variable on the output value of a decision model.

We use the function `data.frame()`

to transform the x and
y outputs of the `mcSimulation()`

function for EVPI
calculation. We use the `multi_EVPI()`

function to calculate
the EVPI for multiple independent variables. For the first_out_var
argument we choose ‘intervention_mngmt_audit_cost’ from the input table
since this is the first variable after the NPV and cashflow model
outputs, which we would like to exclude from the EVPI analysis.

```
#here we subset the outputs from the mcSimulation function (y) by selecting the correct variables
# this should be done by the user (be sure to run the multi_EVPI only on the variables that the user wants)
mcSimulation_table <- data.frame(mcSimulation_results$x, mcSimulation_results$y[1:3])
evpi <- multi_EVPI(mc = mcSimulation_table, first_out_var = "Interv_NPV")
#> [1] "Processing 3 output variables. This can take some time."
#> [1] "Output variable 1 (Interv_NPV) completed."
#> [1] "Output variable 2 (NO_Interv_NPV) completed."
#> [1] "Output variable 3 (NPV_decision_do) completed."
```

We use the function `plot_evpi()`

on the results from
`multi_EVPI()`

to plot the Expected Value of Perfect
Information (EVPI). Here we show the results with the standard settings.
The length of the bars is equal to EVPI.

Finally, we provide a single function for a quick assessment. The
`compound_figure()`

function can be used to run the full
decision assessment for a simple binary decision (‘do’ or ‘do not
do’).

Lanzanova, Denis, Cory Whitney, Keith Shepherd, and Eike Luedeling.
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Luedeling, Eike, Lutz Goehring, Katja Schiffers, Cory Whitney, and
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Luedeling, E., and K. Shepherd. 2016. “Decision-Focused
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Whitney, Cory, John R. S. Tabuti, Oliver Hensel, Ching-Hua Yeh, Jens
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