Artificial Intelligence
The constrainedness knife-edge
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Beyond NP: the QSAT phase transition
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Morphing: combining structure and randomness
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
The phase transition in 1-in-k SAT and NAE 3-SAT
SODA '01 Proceedings of the twelfth annual ACM-SIAM symposium on Discrete algorithms
A physicist's approach to number partitioning
Theoretical Computer Science - Phase transitions in combinatorial problems
Upper bounds on the satisfiability threshold
Theoretical Computer Science - Phase transitions in combinatorial problems
Scaling Effects in the CSP Phase Transition
CP '95 Proceedings of the First International Conference on Principles and Practice of Constraint Programming
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
The interface between P and NP: COL, XOR, NAE, 1-in-k, and Horn SAT
Eighteenth national conference on Artificial intelligence
Threshold phenomena in random graph colouring and satisfiability
Threshold phenomena in random graph colouring and satisfiability
Phase Transitions in Combinatorial Optimization Problems - Basics, Algorithms and Statistical Mechanics
When are elections with few candidates hard to manipulate?
Journal of the ACM (JACM)
On the complexity of manipulating elections
CATS '07 Proceedings of the thirteenth Australasian symposium on Theory of computing - Volume 65
Eliciting single-peaked preferences using comparison queries
Proceedings of the 6th international joint conference on Autonomous agents and multiagent systems
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
A sufficient condition for voting rules to be frequently manipulable
Proceedings of the 9th ACM conference on Electronic commerce
Generalized scoring rules and the frequency of coalitional manipulability
Proceedings of the 9th ACM conference on Electronic commerce
Complexity of terminating preference elicitation
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Elections Can be Manipulated Often
FOCS '08 Proceedings of the 2008 49th Annual IEEE Symposium on Foundations of Computer Science
Proceedings of the 12th Conference on Theoretical Aspects of Rationality and Knowledge
Note: Generalized juntas and NP-hard sets
Theoretical Computer Science
Nonexistence of voting rules that are usually hard to manipulate
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Uncertainty in preference elicitation and aggregation
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
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Winner determination in sequential majority voting
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
From approximate to optimal solutions: a case study of number partitioning
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
Where the really hard problems are
IJCAI'91 Proceedings of the 12th international joint conference on Artificial intelligence - Volume 1
Eliciting single-peaked preferences using comparison queries
Journal of Artificial Intelligence Research
Phase transitions of PP-complete satisfiability problems
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
IJCAI'01 Proceedings of the 17th international joint conference on Artificial intelligence - Volume 1
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
Using complexity to protect elections
Communications of the ACM
An Empirical Study of the Manipulability of Single Transferable Voting
Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence
Hard and easy distributions of SAT problems
AAAI'92 Proceedings of the tenth national conference on Artificial intelligence
The Geometry of Manipulation: A Quantitative Proof of the Gibbard-Satterthwaite Theorem
FOCS '10 Proceedings of the 2010 IEEE 51st Annual Symposium on Foundations of Computer Science
Unweighted coalitional manipulation under the Borda rule Is NP-hard
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Is computational complexity a barrier to manipulation?
Annals of Mathematics and Artificial Intelligence
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Complexity of judgment aggregation
Journal of Artificial Intelligence Research
Achieving fully proportional representation is easy in practice
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
Search strategies for optimal multi-way number partitioning
IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence
Normalized Range Voting Broadly Resists Control
Theory of Computing Systems
A smooth transition from powerlessness to absolute power
Journal of Artificial Intelligence Research
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Voting is a simple mechanism to combine together the preferences of multiple agents. Unfortunately, agents may try to manipulate the result by mis-reporting their preferences. One barrier that might exist to such manipulation is computational complexity. In particular, it has been shown that it is NP-hard to compute how to manipulate a number of different voting rules. However, NP-hardness only bounds the worst-case complexity. Recent theoretical results suggest that manipulation may often be easy in practice. In this paper, we show that empirical studies are useful in improving our understanding of this issue. We consider two settings which represent the two types of complexity results that have been identified in this area: manipulation with unweighted votes by a single agent, and manipulation with weighted votes by a coalition of agents. In the first case, we consider Single Transferable Voting (STV), and in the second case, we consider veto voting. STV is one of the few voting rules used in practice where it is NP-hard to compute how a single agent can manipulate the result when votes are unweighted. It also appears one of the harder voting rules to manipulate since it involves multiple rounds. On the other hand, veto voting is one of the simplest representatives of voting rules where it is NP-hard to compute how a coalition of weighted agents can manipulate the result. In our experiments, we sample a number of distributions of votes including uniform, correlated and real world elections. In many of the elections in our experiments, it was easy to compute how to manipulate the result or to prove that manipulation was impossible. Even when we were able to identify a situation in which manipulation was hard to compute (e.g. when votes are highly correlated and the election is "hung"), we found that the computational difficulty of computing manipulations was somewhat precarious (e.g. with such "hung" elections, even a single uncorrelated voter was enough to make manipulation easy to compute).