Essays on Online Decisions, Model Uncertainty and Learning

dc.contributor.advisor	Nau, Robert
dc.contributor.advisor	Keskin, Bora
dc.contributor.author	Nguyen, Van Vinh
dc.date.accessioned	2018-03-20T17:54:36Z
dc.date.available	2018-03-20T17:54:36Z
dc.date.issued	2017
dc.department	Business Administration
dc.description.abstract	This dissertation examines optimal solutions in complex decision problems with one or more of the following components: online decisions, model uncertainty and learning. The first model studies the problem of online selection of a monotone subsequence and provides distributional properties of the optimal objective function. The second model studies the robust optimization approach to the decision problem of an auction bidder who has imperfect information about rivals' bids and wants to maximize her worst-case payoff. The third model analyzes the performance of a myopic Bayesian policy and one of its variants in the dynamic pricing problem of a monopolistic insurer who sells a business interruption insurance product over a planning horizon.
dc.identifier.uri	https://hdl.handle.net/10161/16286
dc.subject	Business administration
dc.subject	Operations research
dc.subject	Economics
dc.subject	Business Interruption
dc.subject	Dynamic Pricing
dc.subject	Economic Order Quantity
dc.subject	Model Uncertainty
dc.subject	Revenue Management
dc.title	Essays on Online Decisions, Model Uncertainty and Learning
dc.type	Dissertation

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