PhD Dissertation Proposal by SHAO Qian | Real-world Considerations in Human Decision Making

Human decision making involves evaluating information, considering preferences and anticipating outcomes. Real-world considerations can shape the decision-making process. Understanding and incorporating these considerations are crucial for aligning choices with practical realities and achieving goals. This thesis addresses real-world problems in different domains, including minimizing the gap between optimized and preferred routes, imitation learning in cost-constrained settings, and constrained pricing under the multinomial logit model. By considering human preferences, cost constraints, and pricing dynamics, this research bridges the gap between theoretical models and real-world decision-making practices. The insights gained can improve decision-making processes, enhance operational efficiency, optimize resource allocation, and boost revenue generation.

Shao Qian is a Ph.D. student in Computer Science at SMU SCIS, supervised by Prof. Cheng Shih-Fen. Her research interests are last-mile logistics, imitation learning and approximate dynamic programming.