Online Sampling and Decision Making with Low Entropy

Abstract

We consider a spatial voting model where both candidates and voters are positioned in the d-dimensional Euclidean space, and each voter ranks candidates based on their proximity to the voter's ideal point. We focus on the scenario where the given information about the locations of the voters' ideal points is incomplete; for each dimension, only an interval of possible values is known. In this context, we investigate the computational complexity of determining the possible winners under positional scoring rules. Our results show that the possible winner problem in one dimension is solvable in polynomial time for all k-truncated voting rules with constant k. Moreover, for some scoring rules for which the possible winner problem is NP-complete, such as approval voting for any dimension or k-approval for two or more dimensions, we give an FPT algorithm parameterized by the number of candidates. Finally, we classify tractable and intractable settings of the em weighted possible winner problem in one dimension, and resolve the computational complexity of the weighted case for all two-valued positional scoring rules when d=1.