Tuesday, 15:15 - 15:40 h, Room: MA 004

 

Allison O'Hair
Adaptive, dynamic and robust optimization to learn human preferences

Coauthor: Dimitris Bertsimas

 

Abstract:
In 1944, in one of the most influential works of the twentieth century, John von Neumann and Oskar Morgenstern developed the idea of expected utility theory to make decisions under uncertainty. In 1979, Daniel Kahneman and Amos Tversky, in their Nobel prize winning work, presented a critique of expected utility theory by observing that some of its axioms violate human behavior. Specifically, people are loss averse, are inconsistent and evaluate outcomes with respect to deviations from a reference point. However, they did not propose a constructive method to learn preferences that adhere to the new principles. In this work, we use robust and integer optimization in an adaptive and dynamic way to determine preferences that are consistent with human behavior in agreement with the critique of Kahneman and Tversky. We use robust linear optimization to model loss averse behavior, integer optimization to correct for inconsistent behavior and choice-based conjoint analysis in an adaptive questionnaire to dynamically select pairwise questions. We have implemented an online software that uses the proposed approach and report empirical evidence of its strength.

 

Talk 1 of the invited session Tue.3.MA 004
"Applications of robust optimization II" [...]
Cluster 20
"Robust optimization" [...]

 

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