If you are conducting simulations for a CBC study and the questionnaire contains choice tasks including a "None" option, then the option of None can be included in simulations. However, the share of respondents predicted to choose None will only be correct if the number of products in the simulation is the same as the number of products in the CBC questionnaire. This is another problematic outcome of the IIA rule and especially affects simulators built upon aggregate logit runs. With individual-level modeling under CBC/HB, we have seen that the share of "None" is approximately correct irrespective of the number of products used in the simulator (within a reasonable range of product alternatives).
The market simulator lets you specify a "None" weight from Scenario Specification dialog by clicking the Advanced Settings... button. By default, the None weight is set to "0," which means that we do not report a None percentage and we assume that all respondents are in the market and must "buy" a product.
If you are using an aggregate logit run in your market simulations and are using a different number of products than were reflected in your CBC questionnaire, you may need to consider the suggestions below to deal with the problems of IIA and the "None" weight. If you are using individual-level utilities from CBC/HB, then you these considerations are less an issue for you.
None Calibration for Aggregate Logit Models
Logit (Share of Preference) models tend to give too much share to products that are similar to one another, and to penalize products that are unique (this is especially the case with aggregate logit solutions). The None option does not have specified levels on any of the conjoint attributes, and is therefore unique as compared to the products in the simulation.
If you do a simulation with a few products plus None, and then try another that includes those same products plus others, you will find that the share predicted for None will be smaller when there are more products. That may not be an accurate reflection of reality. If the respondents who chose None did so because they would never buy a product in that category, then it would clearly be incorrect to assume that fewer would choose none just because they are offered an array of more products. On the other hand, if respondents are really candidates for those products, then one would expect the share choosing None to decrease when respondents are offered a richer set of choices.
We know of no way to tell how the share choosing None should vary as the number of products in the simulation changes, although we think some allowance should be made in those cases where more products are in the simulation than in the original choice tasks.
For that reason, we provide the user with the capability of adjusting the percentage choosing None, through multiplication by a constant supplied by the user. In general, we think the None weight should be somewhere between unity and the fraction n/t, where n is the number of products in the simulation and t is the number of products in the average choice task. With individual-level utilities, less (or no) adjustment for the None may be needed. With aggregate utilities, more adjustment may be appropriate.
