Presenter: Thodoris Lykouris
Topic: Contextual search in the presence of irrational agents
Spring 2021 Seminar
The EconCS Group holds an Economics and Computer Science research seminar each semester.
Abstract: 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 for 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 or the fact that some agents do not subscribe to the dominant behavioral model. Motivated by this practical concern, I will focus on two canonical revenue management settings (contextual pricing and online advertising) and will introduce an algorithmic framework for achieving robustness to such model misspecifications.
Paper information: The main technical part of the talk will be based on joint work with Akshay Krishnamurthy, Chara Podimata, and Rob Schapire that will appear in STOC'21. The paper can be found in the following link: https://arxiv.org/abs/2002.11650.