Home → Techniques and Tips → @RISK: General Questions → Different Results with the Same Fixed Seed for various distributions
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Problem:
When comparing the random numbers sequence generated for each distribution in a model, they are different when simulated with these scenarios:
Explanation:
The difference can occur because distributions in the model are defined differently. One model could have more distributions or the same number of distributions, but these were defined in a different order.
Since @RISK is sampling a different number of distributions or a list of distributions ordered differently, they are effectively distinct models, and the same results should not be expected. The same model will always produce the same results using the same fixed seed.
For example, suppose I am using a fixed seed that happens to generate the following numbers using RiskUniform(0,1): 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7. (Of course, a pattern like this is enormously unlikely; it's just chosen to make the example easy to follow.)
If I use that same fixed seed and run a simulation with seven iterations, I will always get those values precisely in that order. In other words:
Iteration value of RiskUniform(0,1)
1 0.1
2 0.2
3 0.3
4 0.4
One difference is the number of distributions. So, if I add another RiskUniform(0,1) to the spreadsheet and run seven iterations, the results will be different. The same seed list is generated and used, but now two distributions are sampled from it. In other words:
Iteration Value of first RiskUniform(0,1) Value of second RiskUniform(0,1)
1 0.1 0.2
2 0.3 0.4
3 0.5 0.6
4 0.7 [next value in seeded list]
Another reason for the difference is distributions were defined in a different order. So if I add a RiskPoisson(5) and another RiskUniform(0,1), or first a RiskUniform and then a RiskPoisson and run seven iterations, the results will be different. The same fixed seed is used, but now three distributions are sampled from it, and these are the numbers generated for the RiskPoisson: 2,4,7,5,1,3,6.
In other words:
Iteration 
Value of first RiskUniform(0,1) 
Value of second RiskUniform(0,1) 
value of RiskPoisson(5) 

1 
0.1 
0.2 
7.0 

2 
0.4 
0.5 
3.0 

3 
0.7 
[next value in seeded list] 







Or, with a different order
Iteration 
Value of first RiskUniform(0,1) 
Value of RiskPoisson(5) 
Value of second RiskUniform(0,1) 

1 
0.1 
4.0 
0.3 

2 
0.4 
1.0 
0.6 

3 
0.7 
[next value in seeded list] 
The first model with only one distribution will always produce the same results for the same fixed seed.
The other models with two or three distributions will always have the same random number sequence for the same fixed seed. But the samples will not be the same because they are assigned differently. Ultimately, all samples converge to the same desired distributions, that is, the same statistics, making their results correct and comparable.
Also, consider that If Latin Hypercube sampling is in effect, we can no longer draw purely random samples since we need to ensure that a random sample is drawn from each Latin Hypercube bin. For this reason, you can see that samples will not be identical after a few iterations and will only match the last digits. Read more about this in: Latin Hypercube Versus Monte Carlo Sampling
If identical answers are critical, perhaps another approach is in order. These are some possible solutions:
Last update: 20230209