  But unpredictability is not sufficient, for it may be that we cannot know everything true of the future outcomes of the process, and those truths might preclude a KML-random sequence. One way to see this draws on our discussion of chaotic dynamics. Let’s say that a system exhibits apparent dependence on initial conditions if states indiscriminable to us may end up arbitrarily far apart after some relatively short period of time. (Another definition, if knowledge obeys a margin for error principle, would be: a system exhibits apparent dependence on initial conditions if, for all we know, it is sensitively dependent on initial conditions.) Apparent dependence on initial conditions can obtain even if sensitive dependence on initial conditions does not. For there may well be a size \(v\) such that, under some partition of the set of states into regions of measure smaller than \(v\), any two points starting in the same cell of the partition evolve to future states which are close to one another—as long as \(v\) is small with respect to our abilities to discriminate. A system which is apparently but not sensitively dependent on initial conditions will be unpredictable to us, but there will exist an algorithm that, given some finite specification of the initial conditions, generates the future evolution of the system perfectly. (The key is that the finite specification must involve more precise data than we could know to characterise the system.) This sequence, if long enough, is not KML-random, but it is  unpredictable.[24]