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Title: Most Expected Winner: An Interpretation of Winners over Uncertain Voter Preferences
It remains an open question how to determine the winner of an election given incomplete or uncertain voter preferences. One solution is to assume some probability space for the voting profile and declare that the candidates having the best chance of winning are the (co-)winners. We refer to this interpretation as the Most Probable Winner (MPW). In this paper, we focus on elections that use positional scoring rules, and propose an alternative winner interpretation, the Most Expected Winner (MEW), according to the expected performance of the candidates. We separate the uncertainty in voter preferences into the generation step and the observation step, which gives rise to a unified voting profile combining both incomplete and probabilistic voting profiles. We use this framework to establish the theoretical hardness of MEW over incomplete voter preferences, and then identify a collection of tractable cases for a variety of voting profiles, including those based on the popular Repeated Insertion Model (RIM) and its special case, the Mallows model. We develop solvers customized for various voter preference types to quantify the candidate performance for the individual voters, and propose a pruning strategy that optimizes computation. The performance of the proposed solvers and pruning strategy is evaluated extensively on real and synthetic benchmarks, showing that our methods are practical.  more » « less
Award ID(s):
1916647 1916505 1934464
NSF-PAR ID:
10377586
Author(s) / Creator(s):
Date Published:
Journal Name:
Proceedings of the '23 International Conference on the Management of Data (ACM SIGMOD 2023)
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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