To assist policymakers, researchers have developed models that simulate the progression of diabetes, expenditures on diabetes care, and effects of interventions. The outputs of these models include costs and health outcomes, such as length of life (often expressed as quality-adjusted life-years [QALYs]), a measure that considers the quality of life in each health state. Two principal types of diabetes models exist. Most researchers use a Markov model, which comprises disease states (for example, normal, impaired glucose tolerance [IGT], and diabetes) represented in a computer program. The computer simulates transitions from one disease state to another as chance events. A second novel type of model, named Archimedes (2–3), uses object-oriented computer programming and complex differential equations to simulate pathophysiologic processes (for example, hepatic glucose output after a meal) that change over time and can lead to disease. For a technical explanation of the Archimedes model, I refer to the accompanying editorial in this issue (4). My editorial focuses on the clinical and policy aspects of the Archimedes model.