On distance rationalizability of some voting rules
Proceedings of the 12th Conference on Theoretical Aspects of Rationality and Knowledge
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Strongly decomposable voting rules on multiattribute domains
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Voting on multiattribute domains with cyclic preferential dependencies
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
Preference functions that score rankings and maximum likelihood estimation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Strategy-proof voting rules over multi-issue domains with restricted preferences
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Homogeneity and monotonicity of distance-rationalizable voting rules
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
A maximum likelihood approach towards aggregating partial orders
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
The complexity of online manipulation of sequential elections
Journal of Computer and System Sciences
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In this paper, we study a maximum likelihood estimation (MLE) approach to voting when the set of alternatives has a multi-issue structure, and the voters' preferences are represented by CP-nets. We first consider general multi-issue domains, and study whether and how issue-by-issue voting rules and sequential voting rules can be represented by MLEs. We first show that issue-by-issue voting rules in which each local rule is itself an MLE (resp. a candidate scoring rule) can be represented by MLEs with a weak (resp. strong) decomposability property. Then, we prove two theorems that state that if the noise model satisfies a very weak decomposability property, then no sequential voting rule that satisfies unanimity can be represented by an MLE, unless the number of voters is bounded. We then consider multi-issue domains in which each issue is binary; for these, we propose a general family of distance-based noise models, of which give an axiomatic characterization. We then propose a more specific family of natural distance-based noise models that are parameterized by a threshold. We identify the complexity of winner determination for the corresponding MLE voting rule in the two most important subcases of this framework.