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Found 7 papers, 0 papers with code
The Complexity of Learning Approval-Based Multiwinner Voting Rules
Despite the existence of exponentially many outcomes compared to single-winner elections, we show that the sample complexity is still low: a polynomial number of samples carries enough information for learning the target rule with high confidence and accuracy.
On Interim Envy-Free Allocation Lotteries
iEF is a natural generalization of envy-freeness to random allocations in the sense that a deterministic envy-free allocation is iEF (when viewed as a degenerate lottery).
Fairness
Computer Science and Game Theory
Evaluating approval-based multiwinner voting in terms of robustness to noise
These models take as input a ground truth committee and return random approval votes to be thought of as noisy estimates of the ground truth.
Optimizing positional scoring rules for rank aggregation
Then, the important optimization problem to be solved is to compute the positional scoring rule whose outcome, when applied to the profile of individual rankings, is as close as possible to the part of the underlying true ranking we know.
How effective can simple ordinal peer grading be?
When statistical information about the grading behaviour of students is available (in terms of a noise matrix that characterizes the grading behaviour of the average student from a student population), the framework can be used to compute the optimal rule from this class with respect to a series of performance objectives that compare the ranking returned by the aggregation rule to the underlying ground truth ranking.
Efficiency and complexity of price competition among single-product vendors
Motivated by recent progress on pricing in the AI literature, we study marketplaces that contain multiple vendors offering identical or similar products and unit-demand buyers with different valuations on these vendors.
Aggregating partial rankings with applications to peer grading in massive online open courses
This is a novel application area for social choice concepts and methods where the important problem to be solved is as follows: how should the assignments be distributed so that the collected individual rankings can be easily merged into a global one that is as close as possible to the ranking that represents the relative performance of the students in the assignment?
