Global ETD Search
Search theses and dissertations gathered from participating repositories worldwide. Every result links back to the library that holds it. No account is needed.
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Showing 1 to 20 of 73 for “"Decision-making under uncertainty"”.
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Problems on decision making under uncertainty
… must often make rational choices in the face of uncertainty. Determining decisions, actions, choices, or alternatives that optimize objectives for real-world problems is computationally difficult. This dissertation proposes novel solutions to such optimization problems for both deterministic and …
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ESSAYS ON DECISION MAKING UNDER UNCERTAINTY
Chapter 1 studies a decision maker who approaches a decision problem under uncertainty by formulating a set of plausible probabilistic models of the environment, while being aware that these models are only stylized and incomplete approximations. The decision maker faces two layers of uncertainty. …
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Essays on Decision Making Under Uncertainty
… types of interacting individuals that reproduce under random environmental conditions. We show that not only does the evolutionarily dominant behavior maximize the number of offspring of each type, it also minimizes the correlation between the number of offspring of each type, driving it towards …
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Decision-Making Under Uncertainty: From Theory to Practice
… increased the use of algorithms to automate decisions for a plethora of problems. This thesis focuses on developing data-driven methodologies for sequential decision-making under uncertainty. Specifically, we develop solutions to address practical issues that can arise when operationalizing …
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Data-driven decision-making under uncertainty in power systems
In this dissertation, we construct decision-making frameworks and decision-support tools that seek to aid power system operators (SOs) in delivering an economical and reliable power system operation under uncertainty. The dissertation comprises nine publications, each of which lays out an …
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Sequential Decision-making Under Uncertainty: Novel Methodologies and Applications
In sequential decision-making under uncertainty, multistage stochastic mixed-integer programming (MSMIP) is a tool for addressing optimization problems with a given probability distribution and the goal of optimizing a performance measure over a planning horizon. If there is no knowledge about the …
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Optimizing Decision-Making under Uncertainty -- A Data-Driven Perspective
Decision-making processes are fundamental to many aspects of daily life, from allocating educational resources and optimizing logistics routes to scheduling renewable energy generation and distributing vaccines. These complex problems are typically framed as mathematical optimization problems, …
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Exploring the role data-driven decision-making under uncertainty
Decision making requires managers to carefully analyse the business environment and make sense of existing information in a bid to direct and influence particular courses of action for organisations. However, there is complexity of this process in uncertainty, such as that exemplified by the year …
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An integrated approach to dynamic decision making under uncertainty
Thesis (Ph. D.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1994.
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Three essays on fair division and decision making under uncertainty
… are based on two working papers of mine in decision making under uncertainty. In the second chapter, I study the wealth effect under uncertainty --- how the wealth level impacts a decision maker's degree of uncertainty aversion. I axiomatize a class of preferences displaying decreasing …
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A goal-oriented design evaluation framework for decision making under uncertainty
Thesis (Ph.D.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 1999.
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Decision-making under uncertainty for electric power system operation and expansion planning
Decision-making under uncertainty is required in a multiplicity of situations in power system operation and capacity expansion planning. This thesis investigates the drivers and impact of uncertainty on power system infrastructure planning and proposes several methods to design and operate a power …
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Demand forecasting and decision making under uncertainty for long-term production planning in aviation industry
… capital investments, and highly variable demand. Making important decisions with intensive capital investments requires accurate forecasting of future demand. However, this can be challenging because of significant variability in future scenarios. The purpose of this research is to develop an …
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Decision-making under uncertainty using a new chance constrained programming technique: A groundwater reclamation application
… value at any point uncertain. Because of this uncertainty a reliability based method is required. The chance constrained method is developed to produce a trade off curve of least cost versus reliability. A set of realizations of any statistical distribution can be generated and used to develop …
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Integrated design and operational decision making under uncertainty for enhanced lifecycle performances of engineering systems
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2025-12-01
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A phenomenological study on decision-making under uncertainty in real estate investments in sub-Saharan Africa
… researcher observed that real estate investment decisions were made under uncertainty in sub-Saharan Africa, and that this was due to unreliable and outdated economic and market data. The phenomenological study was an investigation of the investment decision phenomenon on how real estate …
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Exploring the neurobehavioural impacts of combined early life and adult stress on decision making under uncertainty
… from early life to adulthood remain poorly understood. This thesis underscores the importance of translational research in examining the neural and psychological mechanisms underlying behavioural endophenotypes linked to stress-related disorders such as depression and anxiety. A key focus …
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Data-driven robust solution schemes for sequential decision making
… and data-efficient methodologies for sequential decision making under uncertainty, motivated by challenges arising in operations research, control, and machine learning. Classical approaches such as sample average approximation—also referred to as empirical risk minimization in the machine …
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A Control Theoretic Approach to the Stochastic Multi-armed Bandit Problem With Applications in Hyperparameter Optimization
Decision-making under uncertainty is a fundamental problem encountered frequently in many real-world applications. This challenge has been rigorously formulated as the Stochastic Multi-Armed Bandit (SMAB) problem, which consists of a learner interacting with an environment. For each interaction, …
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