Which Voting Rules Are More Resilient to Coalitional Manipulation?
2026-07-01 • Computer Science and Game Theory
Computer Science and Game Theory
AI summaryⓘ
The authors studied how easy it is to manipulate different voting methods by changing how much one specific preference ranking is favored compared to others. They found that each voting rule has a clear cutoff point where manipulation suddenly becomes unlikely, grouping similar voting methods based on their behavior near these points. Testing their simple model on real data showed it can predict which voting rules are more vulnerable and how this changes with the number of candidates. Their work helps explain why some voting rules behave similarly against manipulation, which depends mostly on the number of candidates.
voting rulescoalitional manipulationPerturbed Culture modelCondorcet winnerMaximinRanked PairsSchulze methodphase transitionordinal votingmanipulation vulnerability
Authors
François Durand
Abstract
Which voting rules are more resilient to coalitional manipulation? We find that a deliberately minimal model, capturing only the degree of advantage of one preference ranking over the others, can predict their relative vulnerability remarkably well. Extending prior work on three rules, we systematically analyze all standard ordinal voting rules under the Perturbed Culture model, a variant of Impartial Culture parameterized by the extra weight assigned to one ranking. Each rule exhibits a sharp phase transition: manipulation succeeds with high probability below a critical concentration threshold, and fails above it. This structure reveals natural families of rules: seemingly distinct methods such as Maximin, Ranked Pairs, Schulze, and Young share identical thresholds, while Baldwin, Nanson, Kemeny, and Dodgson form another. These groupings are driven by new, strengthened notions of Condorcet winners. In addition, we identify a third family based on a previously introduced Condorcet notion: Black, Slater, and Copeland. Empirically, the model displays strong predictive power. Tested on real-world datasets (Netflix and FairVote), it accurately ranks rules by vulnerability, predicts how this ranking evolves with the number of candidates, and explains why empirically similar clusters persist despite large absolute differences in manipulation rates, with a more nuanced picture for Bucklin and veto-based rules. Thus, an extremely parsimonious model with no tuning captures the comparative vulnerability of voting rules: which rules to prefer depends largely on the number of candidates alone.