Multiwinner Voting

Multiwinner voting addresses the problem of selecting a group of winners from a larger set of candidates, aiming for fair and representative outcomes reflecting diverse voter preferences. Current research focuses on developing and analyzing voting rules that achieve proportionality and diversity, exploring computational complexity, and addressing challenges in temporal settings and biased data. These advancements have implications for various applications, including committee elections, resource allocation, and even machine learning instance selection, by improving the fairness and representativeness of the chosen subsets. The field is actively developing unified frameworks for analyzing temporal fairness and exploring the interplay between deliberation and voting outcomes.