This research project develops new ways to model the US economy by using the assumption that economic decision makers learn by experience over time about underlying optimal decision rules. Individuals and organizations are faced with complex real world economic decisions and the best decision may not be immediately obvious. Many macroeconomic models assume that these decision makers have full information and always make decisions that best advance their interests. In contrast, this project models economic decision makers by using artificial intelligence in a model of learning through experience, and incorporates this new framework in classic economic cost-benefit tradeoffs. The new framework provides more realistic economic models that can better approximate the actual behavior of people and firms. These models can provide new insights into how U.S. government fiscal and monetary decisions can achieve desired economic outcomes. The project’s starting point is the observation that people and firms in real life typically learn in two ways about optimal behavior. The first one is reasoning: through introspective, abstract deliberations, economic units can better figure out their optimal course of action. The second one is accumulated experiences: the realized outcomes of past actions can update the perceived benefit of these past decisions. These two sources of information are conceptually distinct but limited, as experiences are observed only along the actual path taken by