Simulation experimental design Firm Choice

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 5M 36.174S .

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 thus is 46.875.

Statistics and power

Here you see the statistics of your parameters for the 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	0.013	0.124	0.116	0.190	0.196	0.066	0.080	0.000	0.000	0.151	0.380	0.246	0.151	0.257	-0.071
bheat	1000	1000	0.10	0.101	0.101	0.009	0.009	0.075	0.076	0.139	0.137	0.063	0.061	0.000	0.000	0.101	0.169	0.373	0.100	0.288	0.247
btax	1000	1000	-0.16	-0.162	-0.161	0.010	0.012	-0.203	-0.207	-0.133	-0.129	0.070	0.079	0.000	0.000	-0.161	-0.385	0.282	-0.161	-0.333	0.110
boptout	1000	1000	1.00	1.013	1.009	0.356	0.302	-0.108	0.113	2.395	1.887	2.502	1.773	0.011	0.010	1.001	0.164	0.331	1.002	0.080	-0.222
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	31.520	992.689	0.000	27.766	815.725
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	31.315	983.790	0.000	31.212	979.301
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	31.406	987.755	0.000	27.177	776.474
rob_pval0_boptout	1000	1000	NA	0.036	0.015	0.101	0.051	0.000	0.000	0.935	0.691	0.935	0.691	0.003	0.002	0.004	5.432	35.362	0.001	7.405	69.598

all_designs[["powa"]]

$utilitybayesian

FALSE  TRUE 
 16.4  83.6 

$utilityfixed

FALSE  TRUE 
  7.2  92.8 

Illustration of simulated parameter values

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

$bflood

$bheat

$btax

$boptout