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% A simplified example for simulation decomposition
% Monte Carlo simulation
n=100000; % number of runs
var1=0+100*rand(n,1); % uncertain variable with uniform distribution [0 100]
var2=0+150*rand(n,1); % uncertain variable with uniform distribution [0 150]
result=var1+var2; % resulting variable
Simple probability distribution
figure
histogram(result,100)
Simulation decomposition
setting scenarios
let var1 have two states low [0 50] && high (50 100] and var2 three: low [0 50] && medium (50 100] && high (100
150]
sc=zeros(n,1);
sc=((var2<=50)+(var2>50&var2<=100)*2....
+(var2>100)*3)+(var1>50)*3;
% colors
color=[0 .8 1; 0 .6 1; 0 .4 1;...
1 .8 .4; 1 .6 .4; 1 .4 .4];
% legend
legends={'low var1 & low var2','low var1 & medium var2','low var1 & high var2',....
'high var1 & low var2','high var1 & medium var2','high var1 & high var2'};
% building the graph
sm = simdec(result,sc,color,legends,[]);
Published with MATLAB® R2015b

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Content uploaded by Mariia Kozlova
Author content
... SimDec's innovative visual analytics capabilities have already been considered in a diverse range of environmental decision-making problems (Kozlova et al., 2016;Kozlova and Yeomans, 2019;Deviatkin et al., 2020;Hietanen, 2020;Sadyhova, 2020). Although not commercially available, downloadable versions of SimDec code are readily accessible in Matlab (Kozlova et al., 2018a(Kozlova et al., , 2018b, VBA for Excel (Kozlova and Yeomans, 2020), Python, and R (Sadyhova, 2020). ...
Article
Full-text available
Monte Carlo (MC) simulation is widely used in many different disciplines in order to analyze problems that involve uncertainty. Simulation decomposition has recently provided a simple, but powerful, advancement to the standard Monte Carlo approach. Its value for better informing decision making has been previously shown in the investment-analysis field. In this paper, we demonstrate that simulation decomposition can enhance problem analysis in a wide array of domains by applying it to three very different disciplines: geology, business, and environmental science. Further extensions to such disciplines as engineering, natural sciences, and social sciences are discussed. We propose that by incorporating simulation decomposition into pedagogical practices, we expect students to significantly advance their problem-understanding and problem-solving skills.
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