Cranking: Combining Rankings Using Conditional Probability Models on Permutations
ICML '02 Proceedings of the Nineteenth International Conference on Machine Learning
The complexity of Kemeny elections
Theoretical Computer Science
Generalized scoring rules and the frequency of coalitional manipulability
Proceedings of the 9th ACM conference on Electronic commerce
Improved bounds for computing Kemeny rankings
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
Finite local consistency characterizes generalized scoring rules
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Consistency without neutrality in voting rules: When is a vote an average?
Mathematical and Computer Modelling: An International Journal
On distance rationalizability of some voting rules
Proceedings of the 12th Conference on Theoretical Aspects of Rationality and Knowledge
Fixed-parameter algorithms for Kemeny rankings
Theoretical Computer Science
The unavailable candidate model: a decision-theoretic view of social choice
Proceedings of the 11th ACM conference on Electronic commerce
On the role of distances in defining voting rules
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Aggregating preferences in multi-issue domains by using maximum likelihood estimators
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Joint process games: from ratings to wikis
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
Comparing multiagent systems research in combinatorial auctions and voting
Annals of Mathematics and Artificial Intelligence
Homogeneity and monotonicity of distance-rationalizable voting rules
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Possible winners when new alternatives join: new results coming up!
The 10th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
Determining possible and necessary winners under common voting rules given partial orders
Journal of Artificial Intelligence Research
Cloning in elections: finding the possible winners
Journal of Artificial Intelligence Research
Optimal social choice functions: a utilitarian view
Proceedings of the 13th ACM Conference on Electronic Commerce
Computing the margin of victory for various voting rules
Proceedings of the 13th ACM Conference on Electronic Commerce
On the complexity of voting manipulation under randomized tie-breaking
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
A maximum likelihood approach towards aggregating partial orders
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
Studies in computational aspects of voting: open problems of downey and fellows
The Multivariate Algorithmic Revolution and Beyond
On swap-distance geometry of voting rules
Proceedings of the 2013 international conference on Autonomous agents and multi-agent systems
AT'13 Proceedings of the Second international conference on Agreement Technologies
From blurry numbers to clear preferences: A mechanism to extract reputation in social networks
Expert Systems with Applications: An International Journal
Annals of Mathematics and Artificial Intelligence
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In social choice, a preference function (PF) takes a set of votes (linear orders over a set of alternatives) as input, and produces one or more rankings (also linear orders over the alternatives) as output. Such functions have many applications, for example, aggregating the preferences of multiple agents, or merging rankings (of, say, webpages) into a single ranking. The key issue is choosing a PF to use. One natural and previously studied approach is to assume that there is an unobserved "correct" ranking, and the votes are noisy estimates of this. Then, we can use the PF that always chooses the maximum likelihood estimate (MLE) of the correct ranking. In this paper, we define simple ranking scoring functions (SRSFs) and show that the class of neutral SRSFs is exactly the class of neutral PFs that are MLEs for some noise model. We also define composite ranking scoring functions (CRSFs) and show a condition under which these coincide with SRSFs. We study key properties such as consistency and continuity, and consider some example PFs. In particular, we study Single Transferable Vote (STV), a commonly used PF, showing that it is a CRSF but not an SRSF, thereby clarifying the extent to which it is an MLE function. This also gives a new perspective on how ties should be broken under STV. We leave some open questions.