Complexity of manipulating elections with few candidates
Eighteenth national conference on Artificial intelligence
Vote elicitation: complexity and strategy-proofness
Eighteenth national conference on Artificial intelligence
How many candidates are needed to make elections hard to manipulate?
Proceedings of the 9th conference on Theoretical aspects of rationality and knowledge
When are elections with few candidates hard to manipulate?
Journal of the ACM (JACM)
Computational aspects of preference aggregation
Computational aspects of preference aggregation
Eliciting single-peaked preferences using comparison queries
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
Nonexistence of voting rules that are usually hard to manipulate
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Llull and copeland voting broadly resist bribery and control
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Junta distributions and the average-case complexity of manipulating elections
Journal of Artificial Intelligence Research
Incompleteness and incomparability in preference aggregation
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Universal voting protocol tweaks to make manipulation hard
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Hybrid voting protocols and hardness of manipulation
ISAAC'05 Proceedings of the 16th international conference on Algorithms and Computation
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
Is computational complexity a barrier to manipulation?
CLIMA'10 Proceedings of the 11th international conference on Computational logic in multi-agent systems
Practical voting rules with partial information
Autonomous Agents and Multi-Agent Systems
On the modelling and optimization of preferences in constraint-based temporal reasoning
Artificial Intelligence
Incompleteness and incomparability in preference aggregation: Complexity results
Artificial Intelligence
Possible and necessary winners in voting trees: majority graphs vs. profiles
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Is computational complexity a barrier to manipulation?
Annals of Mathematics and Artificial Intelligence
Winner determination in voting trees with incomplete preferences and weighted votes
Autonomous Agents and Multi-Agent Systems
Where are the hard manipulation problems?
Journal of Artificial Intelligence Research
Robust approximation and incremental elicitation in voting protocols
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
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Complexity theory is a useful tool to study computational issues surrounding the elicitation of preferences, as well as the strategic manipulation of elections aggregating together preferences of multiple agents. We study here the complexity of determining when we can terminate eliciting preferences, and prove that the complexity depends on the elicitation strategy. We show, for instance, that it may be better from a computational perspective to elicit all preferences from one agent at a time than to elicit individual preferences from multiple agents. We also study the connection between the strategic manipulation of an election and preference elicitation. We show that what we can manipulate affects the computational complexity of manipulation. In particular, we prove that there are voting rules which are easy to manipulate if we can change all of an agent's vote, but computationally intractable if we can change only some of their preferences. This suggests that, as with preference elicitation, a fine-grained view of manipulation may be informative. Finally, we study the connection between predicting the winner of an election and preference elicitation. Based on this connection, we identify a voting rule where it is computationally difficult to decide the probability of a candidate winning given a probability distribution over the votes.