Special Seminar in CMS and HSS

Modern online marketplaces require decisions to be made sequentially. These decisions do not only affect the system's performance on the current customer but may also have long-lasting effects, giving rise to a sequence of novel challenges.

In this talk, I will focus on one example of such challenges: the need of robustness to data corruption and other model misspecifications. Classical machine learning approaches rely on collecting a batch of data and fitting a model to it -- this assumes that customers' behavior is identically and independently distributed. However, in practice, the behavioral models assumed are often slightly misspecified, e.g., due to the strategic behavior of participating entities. Motivated by this practical concern, I will focus on two canonical revenue management settings (online advertising and feature-based dynamic pricing) and will introduce an algorithmic framework for achieving robustness to such model misspecifications.

I will end the talk by discussing my broader research agenda on dealing with other practical and societal challenges that arise in sequential decision-making settings where data and decisions are inherently intertwined.