Based on my prior knowledge, I can analyze the relationship between the feature "persons" and the target variable "decision to buy this car" by considering how the capacity of the car to carry a certain number of persons might affect the rating of the decision.

Here's my analysis:

1. Unacceptable: It is likely that the decision to buy the car would be rated as unacceptable if the car's capacity is insufficient to carry the required number of persons. In this case, we can expect that the "persons" values of '2' or '4' would be present in the "unacceptable" class.

2. Acceptable: The decision to buy the car would be considered acceptable if the car's capacity can accommodate the required number of persons. It is possible that the "persons" values of '4' or 'more' could be present in the "acceptable" class.

3. Good: If the car's capacity is more than what is required, it might be rated as a good decision. Hence, it is possible that the "persons" value of 'more' could be present in the "good" class.

4. Very Good: Similar to the "good" class, if the car's capacity to carry persons is significantly higher than required, it might be rated as a very good decision. In this case, the "persons" value of 'more' could be present in the "very good" class.

Based on this analysis, I will now create the dictionary:

```json
{
	"unacceptable": ["2", "4"],
	"acceptable": ["4", "more"],
	"good": ["more"],
	"very good": ["more"]
}
```
Please note that for the specific task and feature described, it is not necessary to include all possible values of the "persons" feature in each target class if they are hard to predict. However, each target class should have a non-empty list.