Social Welfare Maximization in Approval-Based Committee Voting under Uncertainty
For computational social choice researchers, it extends social welfare maximization to uncertain preferences, offering algorithmic foundations for robust committee selection.
The paper studies approval-based multi-winner voting under voter preference uncertainty, providing algorithms to compute social welfare probability distributions, the probability that a given outcome is optimal, and the most likely optimal outcome.
Approval voting is widely used for making multi-winner voting decisions. The canonical rule (also called Approval Voting) used in the setting aims to maximize social welfare by selecting candidates with the highest number of approvals. We revisit approval-based multi-winner voting in scenarios where the information regarding the voters' preferences is uncertain. We present several algorithmic results for problems related to social welfare maximization under uncertainty, including computing the social welfare probability distribution of a given outcome, computing the probability that a given outcome is social welfare maximizing, computing an outcome that is social welfare maximizing with the highest probability, and understanding how robust an outcome is with respect to social welfare maximization.