WOLFRAM|DEMONSTRATIONS PROJECT

Assessing Total Risk from Interacting Factors

​
from model
probable absolute risk = 0.003155
probable relative risk = 2.226165
from histogram
probable risk = 0.002750
m = 0.003553
s = 0.001026
model
f
3
(
f
1
+
f
2
)
f
1
f
2
+
f
4
f
3
f
1
+
f
2
f
3
+
f
6
(
f
4
+
f
5
)
distribution
normal
lognormal
random trials
500
data
generate
clear
seed value
400
use seed
minima
maxima
r
0
0.001
r
0
0.002
f
1
0.5
f
1
0.75
f
2
1.
f
2
1.5
f
3
0.9
f
3
1.8
f
4
0.8
f
4
1.2
f
5
0.6
f
5
1.4
f
6
1.2
f
6
1.8
Risk assessment models are based mainly on the notion that the effects of factors are either additive or multiplicative. Theoretically, at least, risk factors can interact in other ways. This Demonstration provides hypothetical examples of the risk defined by three different kinds of models of increasing complexity and number of factors. Assuming that the factors can be identified, their interactions described by an algebraic model, and their lower and upper limits specified, the "best risk estimate" is reached by a Monte Carlo simulation method akin to the "expanded Fermi solution". The best estimate can be extracted directly from the generated risk estimates' histogram or calculated as the mode of a superimposed normal or lognormal distribution function for comparison.