I was studying about the markov property in reinforcement learning, which is supposed to be one of the important assumptions of this field. In that it says, that while considering the probability of the future, we consider only the present state and actions and not that of the past. An important corollary that arises when we consider the probability of the present state given future state/action, the future state/action can't be ignored as it has valuable information in the computation of the present probability.
I do not understand this second statement. From the point of view of the future event, the present event seems to be the past for this future event. Then why are we considering this past event?