If I remember correctly, when we looked at the None weight for CBC/HB modeling, we only looked at varying the number of products in the simulation by maybe one or two from what was shown to respondents. So, if you showed respondents 5 products per screen, plus the none, you might have reasonably good predictions of the None share when going to 4 or 6 products per screen.
Now, if you are doing a CBC with 40 to 50 SKUs per screen, then maybe you could vary the number of simulated products by a few more and still get pretty accurate predictions of the None%. But, it probably depends on the particular setup and which products you are adding or deleting from the scenarios. The best rule of thumb is to only predict the None% for scenarios with the same complexity (number of concepts) that you've actually shown respondents.
So, the only way to know this for sure would be to add some CBC questions that used different numbers of product alternatives--then you could observe the None% given and compare to the predicted None% from your model.
As a side note: Please be very careful with using more than about 20 product concepts in the market simulator with RFC (randomized first choice). The number of iterations should be increased from the default 200K iterations to something like 500K or even 1MM. That's because the RFC process purposefully adds some noise to the shares (that's how it corrects for product similarity), which noise smoothes out over thousands of iterations. But, if the typical shares are really small (such as <5%), then you need to ask the algorithm to do more iterations than the default so that the random error added isn't too large relative to the size of the final predicted shares.