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OIT 674: Decision-making and Learning under Model Uncertainty: Theory and Applications

In most real-world problems, decision-makers often face uncertainty with respect to the underlying models that drive the rewards/costs associated with potential strategies. The uncertainty in the problem can be modeled in a number of ways (e.g., a probability distribution over some parameters or an uncertainty set for some variables) and a selection of an appropriate framework depends on various considerations ranging from the availability of historical data (or lack thereof) to the robustness of resulting strategies or the tractability of the formulation. In addition, once a framework is selected, further challenges often arise when considering dynamic settings, in which the level of uncertainty may be updated from one period to another. The high-level objectives of this course are: (1) to introduce various frameworks for decision-making under model uncertainty (2) to introduce tools to solve such problems, including ones to develop optimal or near-optimal learning strategies (3) to discuss the various trade-offs that arise such as tractability vs. performance, exploration vs. exploitation, and remembering vs. forgetting (4) to explore research papers that demonstrate applications of discussed methods and models to various problems areas such as dynamic pricing, revenue management, product recommendations, and assortment selection
Terms: Spr | Units: 3
Instructors: Gur, Y. (PI)
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