 Relevance serves to explicate unpredictability. Intuitively, the less relevant past events are for \(A^t\), the less predictable the system is. This basic idea can then be refined in various ways. First, the type of unpredictability we obtain depends on the type of events to which (R) is applied. For instance, the degree of the unpredictability of \(A^t\) increases if its probability is independent not only of \(B^{t_1}\) or other ‘isolated’ past events, but rather the entire past. Second, the unpredictability of an event \(A^t\) increases if the probabilistic dependence of that event on past events \(B^{t_1}\) decreases rapidly with the increase of the temporal distance between the events. Third, the probability of \(A^t\) may be independent of past events simpliciter, or it may be independent of such events only on average. These ideas underlie the analysis of EH as a hierarchy of unpredictability.