The central idea underlying natural computation is constraint propagation (e.g., Pylyshyn, 2003). Imagine a set of locations to which labels can be assigned, where each label is a possible property that is present at a location. Underdetermination exists when more than one label is possible at various locations. However, constraints can be applied to remove these ambiguities. Imagine that if some label x is assigned to one location then this prevents some other label y from being assigned to a neighboring location, because the labels are mutually inconsistent. Say that there is good evidence to assign label x to the first location. Once this is done, a constraint can propagate outwards from this location to its neighbors, removing label y as a possibility for them, and reducing ambiguity. A typical algorithm for performing constraint propagation is called relaxation labeling; connectionist networks are also well-suited to propagate constraints to solve problems of underdetermination.