In many economic design settings, strong assumptions are made about the knowledge of the designer. A canonical example from auction design is assuming perfect knowledge of how bidders’ willingness to pay is distributed. In which settings can we achieve designs with similar guarantees as those under full knowledge, despite knowing only a sample or a first moment of the prior distribution?
The goal of this research is to design classifiers robust to strategic behavior of the agents being classified. Here strategic behavior means incurring some cost in order to improve personal features and thus classification. This improvement can be superficial – i.e., gaming the classifier – or substantial, thus leading to true self-improvement. In the latter case (and only in this case), the robust classifier should actually encourage strategic behavior.
Consider two strategic players, one more informed about the state of the world and the other less informed. How should the more informed side select what data to communicate to the other side, in order to inspire actions that benefit goals like social welfare? Can this be done under constraints such as privacy, limited communication, limited attention span, fairness, etc.?
