Based on prior knowledge, we can analyze the relationship between the "buying price" feature and the target variable "decision to buy this car". 

Considering the buying price as a categorical variable with the categories ['very high', 'high', 'medium', 'low'], we can hypothesize the following relationships:

- Unacceptable: Customers may consider the buying price as unacceptable if it is categorized as 'very high' or 'high', as these price ranges may be seen as too expensive for their expectations or budget.
- Acceptable: The buying price can be categorized as acceptable if it falls under the 'medium' or 'low' range. These price ranges are more affordable and may fit the customer's expectations or budget.
- Good: The buying price being categorized as 'low' can indicate a good decision to buy the car, as it aligns with the customer's expectations or budget.
- Very Good: There can be a questionable relationship between specific buying price categories and the "very good" rating. For example, if the buying price is categorized as 'very high', it may indicate a luxury car with exceptional features that the customer highly values. However, it is difficult to predict other specific buying price categories that would result in a "very good" rating.

Based on this analysis, we can generate the following dictionary:

```json
{
	"unacceptable": ["very high", "high"],
	"acceptable": ["medium", "low"],
	"very good": ["very high"], 
	"good": ["low"]
}
```

Please note that the list for each target class may not include all possible values of the feature "buying price". However, it includes the categories that are likely to be associated with each target class based on prior knowledge.