Computing the Extremal Possible Ranks with Incomplete Preferences
Aviram Imber, Benny Kimelfeld

TL;DR
This paper studies the computational complexity of determining the extremal possible ranks of candidates in voting rules with incomplete preferences, showing NP-hardness results for most cases and identifying some tractable scenarios.
Contribution
It establishes NP-hardness for computing extremal candidate positions under various voting rules with incomplete preferences, extending previous winner determination results.
Findings
Finding minimal and maximal positions is NP-hard for all considered rules.
Tractability remains for fixed top-$k$ positions, except for Maximin.
Deciding if the maximal rank is 1 (necessary winner) is tractable for Maximin.
Abstract
Various voting rules are based on ranking the candidates by scores induced by aggregating voter preferences. A winner (respectively, unique winner) is a candidate who receives a score not smaller than (respectively, strictly greater than) the remaining candidates. Examples of such rules include the positional scoring rules and the Bucklin, Copeland, and Maximin rules. When voter preferences are known in an incomplete manner as partial orders, a candidate can be a possible/necessary winner based on the possibilities of completing the partial votes. Past research has studied in depth the computational problems of determining the possible and necessary winners and unique winners. These problems are all special cases of reasoning about the range of possible positions of a candidate under different tiebreakers. We investigate the complexity of determining this range, and particularly the…
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Taxonomy
TopicsGame Theory and Voting Systems · Constraint Satisfaction and Optimization · Logic, Reasoning, and Knowledge
