RISK- AND AMBIGUITY-AVERSE OPTIMIZATION MADE MORE TRACTABLE AND LESS CONSERVATIVE

This thesis builds on the recent developments in distributionally robust optimization and satisficing and proposes how robust decisions under uncertainty can be efficiently made when the decision maker exhibits risk aversion, ambiguity aversion and regret aversion. Theoretically, we develop a new regret-based robust satisficing framework and devise an upper confidence bound algorithm to tackle two-stage optimization and online learning, respectively. The thesis is complemented by several practical case studies revolving around inventory management, monopoly pricing and portfolio selection.

Author dc:creator

• XUE YILIN

• regret minimization
• online learning
• satisficing
• distributionally robust optimization
• Robust optimization

Licence

• https://scholarbank.nus.edu.sg/bitstreams/0df0cae7-70f4-4268-bc18-c4ca5ea3c26b/download