Voting for movies: the anatomy of a recommender system
Proceedings of the third annual conference on Autonomous Agents
Rank aggregation methods for the Web
Proceedings of the 10th international conference on World Wide Web
Aggregating inconsistent information: ranking and clustering
Proceedings of the thirty-seventh annual ACM symposium on Theory of computing
Generalized scoring rules and the frequency of coalitional manipulability
Proceedings of the 9th ACM conference on Electronic commerce
The learnability of voting rules
Artificial Intelligence
Improved bounds for computing Kemeny rankings
AAAI'06 Proceedings of the 21st national conference on Artificial intelligence - Volume 1
The strategy-proofness landscape of merging
Journal of Artificial Intelligence Research
Fixed-parameter algorithms for Kemeny rankings
Theoretical Computer Science
Finite local consistency characterizes generalized scoring rules
IJCAI'09 Proceedings of the 21st international jont conference on Artifical intelligence
Using complexity to protect elections
Communications of the ACM
The Clarke tax as a consensus mechanism among automated agents
AAAI'91 Proceedings of the ninth National conference on Artificial intelligence - Volume 1
A maximum likelihood approach towards aggregating partial orders
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
| Hi-index | 0.00 |
Social choice studies ordinal preference and information aggregation with applications in high-stakes political elections as well as low-stakes movie rating websites. Recently, computational aspects of classical social choice mechanisms have been extensively investigated, yet not much has been done in designing new mechanisms with the help of computational techniques. In this paper, we outline a workflow to formalize a principled approach towards designing novel social choice mechanisms using machine learning. In the workflow, we clearly separate the following two goals of social choice (1) reaching a compromise among agents' subjective preferences, and (2) revealing the ground truth. For each of the two goals, we discuss criteria for evaluation, main challenges, possible solutions, and future directions.