MAN326 · Decision Analyses
Undergraduate
Objectives
This course builds on the Operations Research I and II courses and is primarily designed to construct a thorough understanding of the decision-making process. The course aims to equip students with the fundamental concepts and skills needed to (1) define decision problems and generate creative alternatives, (2) measure uncertainty, (3) quantify subjective judgement, and (4) integrate all of these elements within an optimal choice model. The course material covers both theoretical and practical foundations for developing computer-aided decision-making models, including an introduction to decisions and decision environments, recognition of decision problems, and an analytical thinking approach for making sound decisions. It also introduces advanced decision-making techniques such as dynamic programming and reinforcement learning.
References
• Taha, H. (2011). Operations Research: An Introduction. Pearson.
• Sutton, R. & Barto, A. (2015). Reinforcement Learning.
• Winston, W. L. & Goldberg, J. B. (2004). Operations Research: Applications and Algorithms. Duxbury Press.
• Powell, W. B. (2015). Approximate Dynamic Programming.
• Abbas, A. E. & Howard, R. A. (2016). Foundations of Decision Analysis, Global Edition, 12. baskı, Prentice Hall.
This course builds on the Operations Research I and II courses and is primarily designed to construct a thorough understanding of the decision-making process. The course aims to equip students with the fundamental concepts and skills needed to (1) define decision problems and generate creative alternatives, (2) measure uncertainty, (3) quantify subjective judgement, and (4) integrate all of these elements within an optimal choice model. The course material covers both theoretical and practical foundations for developing computer-aided decision-making models, including an introduction to decisions and decision environments, recognition of decision problems, and an analytical thinking approach for making sound decisions. It also introduces advanced decision-making techniques such as dynamic programming and reinforcement learning.
References
• Taha, H. (2011). Operations Research: An Introduction. Pearson.
• Sutton, R. & Barto, A. (2015). Reinforcement Learning.
• Winston, W. L. & Goldberg, J. B. (2004). Operations Research: Applications and Algorithms. Duxbury Press.
• Powell, W. B. (2015). Approximate Dynamic Programming.
• Abbas, A. E. & Howard, R. A. (2016). Foundations of Decision Analysis, Global Edition, 12. baskı, Prentice Hall.
Materials
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