CU15: Models and Applications in Decision Analysis
Course Summary
This course covers critical topics in decision analysis, addressing the complexity of decision-making processes involving multiple objectives and uncertainties. Students will explore heuristic methods, additive value models, and techniques such as value trees, performance descriptors, and sensitivity analyses. Key methodologies covered include SMART, SMARTER, and MACBETH approaches, as well as decision-making under uncertainty, decision trees, risk management, group decision-making, and resource allocation problems. The course emphasizes real-world application of decision analysis tools, developing students’ analytical and critical skills.Course Highlights
- Complexity in Decision-Making Processes
- Heuristics and Multi-objective Decision Methods
- Additive Value Models and Value Trees
- Sensitivity Analysis and Performance Descriptors
- SMART, SMARTER, and MACBETH Techniques
- Decision-making under Uncertainty and Decision Trees
- Risk Management and Group Decision-Making
- Resource Allocation and Optimization