Generalized scoring rules and the frequency of coalitional manipulability
Proceedings of the 9th ACM conference on Electronic commerce
Aggregating Partially Ordered Preferences
Journal of Logic and Computation
mCP nets: representing and reasoning with preferences of multiple agents
AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
Uncertainty in preference elicitation and aggregation
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Determining possible and necessary winners under common voting rules given partial orders
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 1
Journal of Artificial Intelligence Research
A multivariate complexity analysis of determining possible winners given incomplete votes
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Preference functions that score rankings and maximum likelihood estimation
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
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
Towards a dichotomy for the Possible Winner problem in elections based on scoring rules
Journal of Computer and System Sciences
Campaigns for lazy voters: truncated ballots
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
When do noisy votes reveal the truth?
Proceedings of the fourteenth 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
Hi-index | 0.00 |
In many of the possible applications as well as the theoretical models of computational social choice, the agents' preferences are represented as partial orders. In this paper, we extend the maximum likelihood approach for defining "optimal" voting rules to this setting. We consider distributions in which the pairwise comparisons/incomparabilities between alternatives are drawn i.i.d. We call such models pairwise-independent models and show that they correspond to a class of voting rules that we call pairwise scoring rules. This generalizes rules such as Kemeny and Borda. Moreover, we show that Borda is the only pairwise scoring rule that satisfies neutrality, when the outcome space is the set of all alternatives. We then study which voting rules defined for linear orders can be extended to partial orders via our MLE model. We show that any weakly neutral outcome scoring rule (including any ranking/candidate scoring rule) based on the weighted majority graph can be represented as the MLE of a weakly neutral pairwise-independent model. Therefore, all such rules admit natural extensions to profiles of partial orders. Finally, we propose a specific MLE model πk for generating a set of k winning alternatives, and study the computational complexity of winner determination for the MLE of πk.