Individual and Social Learning: An Implementation of Bounded Rationality from First Principles

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dc.contributor.advisorAxtell, Robert L.
dc.contributor.authorPalmer, Nathan Michael
dc.creatorPalmer, Nathan Michael
dc.date.accessioned2016-04-19T19:27:26Z
dc.date.available2016-04-19T19:27:26Z
dc.date.issued2015
dc.description.abstractThis 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.
dc.format.extent259 pages
dc.identifier.urihttps://hdl.handle.net/1920/10160
dc.language.isoen
dc.rightsCopyright 2015 Nathan Michael Palmer
dc.subjectEconomics
dc.subjectComputer science
dc.subjectMathematics
dc.subjectAgent-Based Modeling
dc.subjectConsumption and Savings
dc.subjectDynamic programming
dc.subjectEconomics
dc.subjectLearning
dc.subjectSimulation
dc.titleIndividual and Social Learning: An Implementation of Bounded Rationality from First Principles
dc.typeDissertation
thesis.degree.disciplineComputational Social Science
thesis.degree.grantorGeorge Mason University
thesis.degree.levelDoctoral

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