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**Different Results with Same Fixed Seed**

**Problem:**

Several students are submitting @RISK models for homework, but even using the same fixed seed some have different results. I have noticed that some worksheets contain several versions of "scratch" models.**Explanation:**

Since a different number of distributions are being sampled, they are effectively different models, and different results should be expected. The same model will always produce the same results using the same fixed seed.

As an 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 7 iterations, I will always get exactly those values in exactly that order. In other words:

Iteration value of RiskUniform(0,1)

1 0.1

2 0.2

3 0.3

4 0.4

etc

But if I now add another RiskUniform(0,1) to the spreadsheet and run 7 iterations, the results will be different. The same seed list is generated and used, but now two distributions are sampling 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]

The first model will always produce its same results for the same fixed seed. And the second model will always produce its same results for the same fixed seed. But the results between the two models will not be the same.

If identical answers are critical, perhaps another approach is in order.

One alternative is that you could supply the variable data directly. For instance, distribute a list of numbers saying, "Here are the monthly interest rates for the next five years."

Another option is to compare the text of the spreadsheet cells, rather than the final numeric output. In other words, check for a cell definition of "RiskUniform(0,1)", rather than numeric results. Or maybe some combination of these approaches.

See also: Random Number Generation, Seed Values, and Reproducibility.

last edited: 2012-08-08

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