Most existing approaches to reasoning under uncertainty and with incomplete information appeal to formal theories, with relatively little attention to the phenomena they are intended to capture. This has had two major consequences. First, it has led to spurious disputes, in which participants criticize alternative approaches in the belief that they are competing, when in fact they are investigating different aspects of related phenomena, and should ultimately be viewed as cooperative efforts. Second, it has led to wasted effort on models which fail to reflect important aspects of kinds of reasoning which they are trying to capture, because necessary distinctions among kinds of generalization have not been made, and so the representational requirements have not been adequately spelled out. This paper delineates several different kinds of reasoning under uncertainty, establishes some distinctions within the field, and attempts to begin setting some ground rules for representational adequacy.