Graduiertenkolleg: Methods for Discrete Structures

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Monday Lecture and Colloquium


Monday, November 27, 2017

Technische Universität Berlin
Institut für Mathematik
Straße des 17. Juni 136
10623 Berlin
room MA 041



Lecture - 14:15

Piotr Skowron- Technische Universität Berlin


Multiwinner Elections: Applications, Axioms, and Algorithms

Abstract:
The goal of multiwinner elections is to select a group of k individuals (the committee) based on the preferences of a number of agents (voters). While the most prominent example of multiwinner elections comes from the world of politics (parliamentary elections held in almost all modern democracies), they are present nearly everywhere and include choosing other representative bodies (e.g., working groups to deal with particular issues), making various business decisions (e.g., choosing products for an Internet store to put on its homepage, choosing movies to put on a transatlantic flight), or shortlisting tasks (e.g., shortlisting applicants for a given academic position).

We will present a number of applications of multiwinner voting (including the ones mentioned above), a number of multiwinner voting rules, and a number of algorithms for computing them (most of the rules are NP-hard to compute). Based on the axiomatic properties of the rules and on simulation results, we will argue which rules are best-suited for particular applications.


Colloquium - 16:00

Piotr Skowron - Technische Universität Berlin


Proportional Rankings

Abstract:
We extend the principle of proportional representation to rankings: given approval preferences, we aim to generate aggregate rankings so that cohesive groups of voters are represented proportionally in each initial segment of the ranking. Such rankings are desirable in situations where initial segments of different lengths may be relevant, e.g., in recommendation systems, for hiring decisions, or for the presentation of competing proposals on a liquid democracy platform. We define what it means for rankings to be proportional, provide bounds for well-known aggregation rules, and experimentally evaluate the performance of these rules.



Letzte Aktualisierung: 21.11.2017