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

Assuming that people with higher buying prices might have higher expectations and therefore rate the decision to buy the car higher, we can make the following observations:

- "Very high" buying prices are more likely to be associated with a "very good" rating for the decision to buy the car.

- "High" buying prices might also be associated with a "good" or "very good" rating.

- "Medium" buying prices could be associated with an "acceptable" or "good" rating.

- "Low" buying prices might be associated with an "acceptable" or "unacceptable" rating, as people with very limited budgets might have less satisfaction with their purchase.

With this analysis in mind, we can create a dictionary with the following relationships:

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

Please note that these relationships are subjective and are based on assumptions. The actual relationships might vary depending on the specific dataset and the opinions of car buyers.