Pathfinding, as a process of selecting a fixed route, has long been studied in Computer Science and Mathematics. Decision making, as a similar, but intrinsically different, process of determining a control policy, is much less studied. Here, I propose a problem that appears to be of the first class, which would suggest that it is easily solvable with a modern machine, but that would be too easy, it turns out. By allowing a pathfinding to anticipate and respond to information, without setting restrictions on the \structure" of this anticipation, selecting the \best step" appears to be an intractable problem. After introducing the necessary foundations and stepping through the strangeness of “safest-with-sight," I attempt to develop an method of approximating the success rate associated with each potential decision; the results suggest something fundamental about decision making itself, that information that is collected at a moment that it is not immediately “consumable", i.e. non-incident, is not as necessary to anticipate than the contrary, i.e. incident information. This is significant because (i) it speaks about when the information should be anticipated, a moment in decision-making long before the information is actually collected, and (ii) whenever the model is restricted to only incident anticipation the problem again becomes tractable. When we only anticipate what is most important, solutions become easy to compute, but attempting to anticipate any more than that and solutions may become impossible to find on any realistic machine.