Discretization in decision analysis

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dc.contributor.advisorBickel, J. Eric
dc.contributor.committeeMemberHasenbein, John
dc.contributor.committeeMemberLeibowicz, Benajmin D
dc.contributor.committeeMemberEzekoye, Ofodike
dc.creatorWoodruff, Joshua Morgan
dc.creator.orcid0000-0002-2025-6869
dc.date.accessioned2021-06-08T05:55:25Z
dc.date.available2021-06-08T05:55:25Z
dc.date.created2020-08
dc.date.issued2020-08
dc.date.submittedAugust 2020
dc.date.updated2021-06-08T05:55:26Z
dc.description.abstractThe choice of discretizations in Decision Analysis impacts the accuracy of the probabilistic analysis of the potential strategies. This dissertation introduces a novel method for creating discretizations for specific problems. Next, we introduce the distance metric, which is borrowed from stochastic optimization. This metric indicates how well two cumulative distribution functions match each other in terms of shape. Discretizations that better match the shape are more accurate in estimating the value of a cumulative distribution function at any given percentile. We determine under which conditions the distance is higher or lower, and which discretizations to choose. Finally, we show what happens to the accuracy of discretizations when there is assessment error and how this impacts the choice of discretizations.
dc.description.departmentOperations Research and Industrial Engineering
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/2152/86322
dc.identifier.urihttp://dx.doi.org/10.26153/tsw/13273
dc.language.isoen
dc.subjectOptimization
dc.subjectDecision analysis
dc.subjectUncertainty
dc.subjectAssessment error
dc.titleDiscretization in decision analysis
dc.typeThesis
dc.type.materialtext
thesis.degree.departmentOperations Research and Industrial Engineering
thesis.degree.disciplineOperations Research and Industrial Engineering
thesis.degree.grantorThe University of Texas at Austin
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy

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