Seminar

First, Jannik Peters (TU Berlin) will give an overview talk, in which he will give an overview over core notions and algorithms in the proportional clustering literature and discuss their connections to each other and to related notions and algorithms from social choice.

Second, Evi Micha (Harvard) will give a spotlight talk about her work on proportional clustering and about applying the same principles to the random selection of representatives (sortition):

Sortition is based on the idea of choosing randomly selected representatives for decision making. The main properties that make sortition particularly appealing are fairness—all the citizens can be selected with the same probability—and  proportional representation—a randomly selected panel  probably  reflects the composition of the whole population. When a population lies on a representation metric, we formally define proportional representation by using a notion called the core. A panel is in the core if no group of individuals is underrepresented proportional to its size. While uniform selection is fair, it does not always return panels that are in the core. Thus, we ask if we can design a selection algorithm that satisfies fairness and ex post core simultaneously. We answer this question affirmatively and present an efficient selection algorithm that is fair and provides a constant-factor approximation to the optimal ex post core.