The actions taken by an automated decision-making agent can be enhanced by including mechanisms that enable the agent to categorize concepts effectively. We pose a utility-based approach to categorization based on the idea that categorization should be carried out in the service of action. The choice of concepts is critical in the effective selection of actions under resource constraints. We propose a decision-theoretic framework for categorization which involves reasoning about alternative categorization models consisting of sets of interrelated concepts at varying levels of abstraction. Categorization models that are too abstract may overlook details that are critical for selecting the most appropriate actions. Categorization models that are too detailed, however, may be too expensive to process and may contain irrelevant information. Categorization models are therefore evaluated on the basis of the expected value of their recommended action, taking into account the resource cost of their evaluation. A knowledge representation scheme, known as probabilistic conceptual networks, has been developed to support the dynamic construction of models at varying levels of abstraction. This scheme combines the formalisms of influence diagrams from decision analysis and inheritance/abstraction hierarchies from AI. We also propose an incremental approach to categorical reasoning. By applying decision-theoretic control of model refinement, a resource-constrained actor iteratively decides between continuing to improve the current level of abstraction in the model, or to act immediately.>