Journal of Computer and System Sciences
When are elections with few candidates hard to manipulate?
Journal of the ACM (JACM)
Average-case tractability of manipulation in voting via the fraction of manipulators
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Generalized scoring rules and the frequency of coalitional manipulability
Proceedings of the 9th ACM conference on Electronic commerce
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Algorithms for the coalitional manipulation problem
Artificial Intelligence
Nonexistence of voting rules that are usually hard to manipulate
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Junta distributions and the average-case complexity of manipulating elections
Journal of Artificial Intelligence Research
Universal voting protocol tweaks to make manipulation hard
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Compiling the votes of a subelectorate
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Where are the really hard manipulation problems? the phase transition in manipulating the veto rule
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Complexity of unweighted coalitional manipulation under some common voting rules
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Hybrid voting protocols and hardness of manipulation
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
Preference functions that score rankings and maximum likelihood estimation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
A scheduling approach to coalitional manipulation
Proceedings of the 11th ACM conference on Electronic commerce
Computing the margin of victory for various voting rules
Proceedings of the 13th ACM Conference on Electronic Commerce
Designing social choice mechanisms using machine learning
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Generalized scoring rules: a framework that reconciles Borda and Condorcet
ACM SIGecom Exchanges
Smart pricing scheme: A multi-layered scoring rule application
Expert Systems with Applications: An International Journal
A smooth transition from powerlessness to absolute power
Journal of Artificial Intelligence Research
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An important problem in computational social choice concerns whether it is possible to prevent manipulation of voting rules by making it computationally intractable. To answer this, a key question is how frequently voting rules are manipulable. We [Xia and Conitzer, 2008] recently defined the class of generalized scoring rules (GSRs) and characterized the frequency of manipulability for such rules. We showed, by examples, that most common rules seem to fall into this class. However, no natural axiomatic characterization of the class was given, leaving the possibility that there are natural rules to which these results do not apply. In this paper, we characterize the class of GSRs based on two natural properties: it is equal to the class of rules that are anonymous and finitely locally consistent. Generalized scoring rules also have other uses in computational social choice. For these uses, the order of the GSR (the dimension of its score vector) is important. Our characterization result implies that the order of a GSR is related to the minimum number of locally consistent components of the rule. We proceed to bound the minimum number of locally consistent components for some common rules.