Simulation experimental design Firm Choice

Published

March 5, 2026

The following code shows the selected priors and the corresponding WTP values as well as the deterministic choices

The simulation has 300 respondents and 1000 runs. The simulation itself took 6M 52.5889999999999S .

Prior selection and deterministic utility.

The following priors have been selected and they result in the following WTP values

# Priors chosen in this simulation
b_flood <- all_designs$arguements$`Beta values`$bflood
b_heat <- all_designs$arguements$`Beta values`$bheat
b_tax <- all_designs$arguements$`Beta values`$btax


(c(b_flood,b_heat,b_tax))

[1]  0.15  0.10 -0.16

# Calculate WTP values
wtp_flood <- -b_flood / b_tax
wtp_heat <- -b_heat / b_tax


# Print the WTP values
cat("WTP for Flood Reduction: ", wtp_flood, "\n")

WTP for Flood Reduction:  0.9375

cat("WTP for Heat Reduction: ", wtp_heat, "\n")

WTP for Heat Reduction:  0.625

For a 1% reduction in flood risk respondents are willing to accept a tax increase 0.9375 percentage points. 50% reduction in flood risk is worth a 46.875 percentage point increase in taxes.

Statistics and power

Here you see the statistics of the parameters for 1000 runs.

kable(all_designs[["summaryall"]] ,digits = 3) %>% kable_styling()

parname	utilitybayesian.n	utilityfixed.n	truepar	utilitybayesian.mean	utilityfixed.mean	utilitybayesian.sd	utilityfixed.sd	utilitybayesian.min	utilityfixed.min	utilitybayesian.max	utilityfixed.max	utilitybayesian.range	utilityfixed.range	utilitybayesian.se	utilityfixed.se	utilitybayesian.median	utilitybayesian.skew	utilitybayesian.kurtosis	utilityfixed.median	utilityfixed.skew	utilityfixed.kurtosis
bflood	1000	1000	0.15	0.152	0.151	0.011	0.012	0.122	0.119	0.201	0.195	0.079	0.076	0.000	0.000	0.152	0.370	0.413	0.151	0.230	-0.060
bheat	1000	1000	0.10	0.102	0.101	0.011	0.009	0.072	0.075	0.146	0.132	0.074	0.058	0.000	0.000	0.102	0.335	0.094	0.100	0.081	-0.254
btax	1000	1000	-0.16	-0.163	-0.161	0.012	0.011	-0.215	-0.200	-0.132	-0.131	0.083	0.069	0.000	0.000	-0.162	-0.381	0.331	-0.161	-0.247	-0.114
boptout	1000	1000	1.00	1.034	1.016	0.314	0.268	-0.193	0.233	2.298	1.860	2.492	1.628	0.010	0.008	1.033	-0.010	0.448	1.021	0.073	-0.125
rob_pval0_bflood	1000	1000	NA	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	NaN	0.000	NaN	NaN
rob_pval0_bheat	1000	1000	NA	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	NaN	0.000	NaN	NaN
rob_pval0_btax	1000	1000	NA	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	0.000	NaN	NaN	0.000	NaN	NaN
rob_pval0_boptout	1000	1000	NA	0.016	0.004	0.066	0.025	0.000	0.000	0.890	0.360	0.890	0.360	0.002	0.001	0.000	7.736	73.633	0.000	10.175	114.098

all_designs[["powa"]]

$utilitybayesian

FALSE  TRUE 
  7.4  92.6 

$utilityfixed

FALSE  TRUE 
  1.7  98.3

Illustration of simulated parameter values

To facilitate interpretation and judgement of the different designs, we plot the densities of simulated parameter values from the different experimental designs.

$bflood


$bheat


$btax


$boptout