Distributive and Temporal Fairness in Algorithmic Collective Decision−Making

Abstract

From dividing parliamentary seats after a national election, to scheduling conference activities for an international AI conference, or deciding how to split public budget for city-wide projects, numerous real-life scenarios necessitates a group of individuals collectively reaching a desirable outcome through a preference aggregation process. In recent years, algorithms have been deployed in many scenarios to aid humans in such collective decision-making processes, with the goal of achieving fair outcomes efficiently. My work looks at the design and analysis of algorithms for various collective decision-making settings, including (i) indivisible resource allocation in the presence of strategic agents with different entitlements, (ii) multiwinner elections with temporal considerations, and (iii) the division of time and money when agents have cardinal preferences.