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dc.contributor.advisorCynthia Barnhart and David Simchi-Levi.en_US
dc.contributor.authorBeeler, Michael Francis.en_US
dc.contributor.otherMassachusetts Institute of Technology. Operations Research Center.en_US
dc.date.accessioned2020-02-10T21:39:26Z
dc.date.available2020-02-10T21:39:26Z
dc.date.copyright2019en_US
dc.date.issued2019en_US
dc.identifier.urihttps://hdl.handle.net/1721.1/123732
dc.descriptionThesis: Ph. D., Massachusetts Institute of Technology, Sloan School of Management, Operations Research Center, 2019en_US
dc.descriptionCataloged from PDF version of thesis.en_US
dc.descriptionIncludes bibliographical references (pages 207-213).en_US
dc.description.abstractThis thesis develops inference and decision models to address challenges of particular relevance in low-income countries (LICs). The areas studied include intelligent tutoring systems (ITS), network infrastructure pricing, and anti-counterfeiting. The ITS chapter identifies previously unknown and serious limitations to Bayesian Knowledge Tracing and Deep Knowledge Tracing, which are two highly-cited methods designed to aid adaptive educational software. The work on Deep Knowledge Tracing led to new data augmentation methods for training recurrent neural networks to be robust in the face of unseen input sequences. We propose a statistically consistent, efficient, and unbiased alternative inference method for questions engaging one skill at a time. The network infrastructure pricing chapters examine how to allocate the cost of a future infrastructure network whose structure depends on the price-taking decisions of potential users. In a multi-period setting, strategic joining delay by users typically leads to lower utility. We develop a cost-allocation rule that uses rebates to prevent strategic delay. In the single-period setting, we derive closed-form solutions to the expected value of offering to build a simple 1D network and use the 1D solution to establish a lower-bound estimate for more complex 2D networks. The anti-counterfeiting chapter investigates the strategic procurement of counterfeits by retailers and the effects of shared retailer reputation on equilibrium procurement decisions using models that are more flexible and tractable than those previously appearing in the literature.en_US
dc.description.statementofresponsibilityby Michael Francis Beeler.en_US
dc.format.extent213 pagesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsMIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectOperations Research Center.en_US
dc.titleInference and decision models for regulatory and business challenges in low-Income countriesen_US
dc.typeThesisen_US
dc.description.degreePh. D.en_US
dc.contributor.departmentMassachusetts Institute of Technology. Operations Research Centeren_US
dc.contributor.departmentSloan School of Management
dc.identifier.oclc1138876147en_US
dc.description.collectionPh.D. Massachusetts Institute of Technology, Sloan School of Management, Operations Research Centeren_US
dspace.imported2020-02-10T21:39:25Zen_US
mit.thesis.degreeDoctoralen_US
mit.thesis.departmentSloanen_US
mit.thesis.departmentOperResen_US


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