Prescriptive decision analysis is a quantitative exercise. Options are assigned weights and probabilities to calculate an expected value. However, we view decision making as a qualitative process. Instead of converting qualitative features into numbers, we advocate converting numbers into qualitative features. As part of a larger effort to develop a decision simulation system [Slade et al. 1995], we are motivated to provide a principled means for automated, qualitative analysis. Following the artificial intelligence tradition of qualitative physics, we have developed an intentional arithmetic for interpreting quantitative data in a qualitative manner. Unlike the physical world, intentional domains require the analysis of the underlying goals of the decision maker. These goals, and their relative importance, provide a useful device for interpreting otherwise ambiguous data. In the next section, we discuss the background work in qualitative reasonsing and its relevance for decision making. We then describe our proposed intentional arithmetic for qualitative decision making