If you model X as "rude person", then you expect him to be rude with a high[er than average] probability cases, period.

However, if you model X as an agent that believes that rudeness is appropriate in common situations A,B,C, then you expect that he might behave less rudely (a) if he would percieve that this instance of a common 'rude' situation is nuanced and that rudeness is not appropriate there; or (b) if he could be convinced that rudeness in situations like that is contrary to his goals, whatever those may be.

In essence, it's simpler and faster to evaluate expected reactions for people that you model as just complex systems, you can usually do that right away. But if you model goal-oriented behavior, "walk a mile in his shoes" and try to understand the intent of every [non]action and the causes of that, then it tends to be tricky but allows you more depth in both accurate expectations, and ability to affect the behavior.

However, if you do it poorly, or simply lack data neccessary to properly understand the reasons/motivations of that person then you'll tend to get gross misunderstandings.

One has a particular belief, while the other follows a particular pattern of behavior? Not sure I see what you're getting at.