arXiv AI
Improving Randomized Metric Distortion to 2.3282
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arXiv AI
Sep 10
Improving Randomized Metric Distortion to 2.1441
The paper announces an improved upper bound on the distortion of randomized voting rules in metric social choice, reducing it from the previous range of $[2.1126,2.5]$ to $2.1441$. It introduces the concept of random-size stable lotteries, proves their existence, and derives the new bound via a potential argument. The proofs were generated with GPT-5.6-Sol and subsequently verified and simplified by the author.
arXiv AI
Oct 15
MILP the Gap: An Automated Reasoning Approach to Condorcet's Paradox
arXiv:2604.19851v2 Announce Type: replace-cross Abstract: Computational social choice studies how conflicting individual preferences are aggregated into a collective decision, with applications to el...
arXiv Machine Learning
6d ago
Sharp Bounds on the Winner's Bias under Independent and Nonnegatively Correlated Noise
arXiv:2610.31948v1 Announce Type: new Abstract: Reporting the best of $M$ noisy, unbiased scores overstates the winner's value by up to $c\sqrt{V}$, where $V$ is the total noise variance and the shar...
arXiv AI
Oct 16
Replicable Dueling Bandits
The paper introduces the first algorithms for replicable dueling bandits, ensuring that two runs with shared internal randomness and independent comparison outcomes select the same ordered pair in every round with high probability. Under relaxed stochastic transitivity, the authors achieve expected regret ×O(γ^8 K^4 H_p/ρ^2), while under strong stochastic transitivity they improve this to ×O(K^2 H_p/ρ^2). For linear dueling bandits, a regret bound of ×O(d^3√T/(c_μρ)) is presented, and experiments confirm both replicability and sublinear regret.
arXiv AI
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Learning Lotteries with Minimal Stakeholder Violations from Membership Queries
arXiv:2610. 37828v1 Announce Type: cross Abstract: An AI developer often has several candidate versions of a system and can deploy a mixture of them, sending each request to a randomly chosen version, as in A/B testing.
arXiv AI
Oct 2
Query-efficient winner prediction in district-based elections
The paper studies how to predict the winner of a district‑based election using as few queries to individual votes as possible. It introduces an adaptive randomized algorithm that, for any number of candidates and any error tolerance, correctly predicts the overall winner with probability at least 1‑δ using ≥O(1/ε² log m/δ log 1/δ) queries. This improves previous results for two candidates and balanced districts, and nearly matches a proven lower bound for constant‑size candidate sets.