Axtell, Robert L.Palmer, Nathan Michael2016-04-192016-04-192015https://hdl.handle.net/1920/10160This dissertation expands upon a growing economic literature that uses tools from reinforcement learning and approximate dynamic programming to impose bounded rationality in intertemporal choice problems. My dissertation contributes to the literature by applying these tools to the canonical household consumption under uncertainty problem. The three essays explore individual and social approaches to learning-to-optimize and how these may be brought to data.259 pagesenCopyright 2015 Nathan Michael PalmerEconomicsComputer scienceMathematicsAgent-Based ModelingConsumption and SavingsDynamic programmingEconomicsLearningSimulationDissertation