The paper investigates the Reverse Sequential Proportional Approval Voting Rule (RevSeqPAV) used in approval-based committee elections. It examines the rule’s performance in terms of proportional representation—specifically Extended Justified Representation and related metrics—and its ability to approximate the maximum PAV score. The authors first present strong negative results for general election instances, then identify specific settings where RevSeqPAV offers meaningful fairness and optimization guarantees.
arXiv:2503. 01985v2 Announce Type: replace-cross Abstract: In the classic committee election setting each voter approves a subset of candidates and the goal is to select $k$ winners based on these preferences.
The paper investigates proportional representation in a temporal voting setting where a single candidate is chosen each round and voters submit ranked preferences that may evolve over time. It extends classic proportionality axioms—justified representation (JR), proportional JR (PJR), extended JR (EJR), and proportionality for solid coalitions (PSC)—to accommodate various ways of determining which top-ranked candidates are considered approved, ranging from a fixed common cutoff to individual, round‑specific cutoffs. The authors analyze which axioms can be guaranteed under different informational assumptions about future rounds, showing that while EJR is unattainable, JR, PJR, and PSC can be achieved with a fixed cutoff if all preferences are known in advance; varying cutoffs reduce guarantees, yet PJR can still be achieved efficiently for groups that agree in every round, and PSC can be satisfied without future knowledge. They also demonstrate that checking these axioms is often coNP‑complete, though some stronger axioms may be easier to verify.
arXiv:2608. 11500v1 Announce Type: cross Abstract: Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approval-based committee elections.
The paper proposes a transparent, user‑configurable rule for selecting arguments in deliberative polls, replacing opaque learned rankers. It formalises argument selection over bipolar justification sets, introduces seven civic recommender criteria, and presents a one‑hop reversed endorsement flow rule that meets them. Experiments on 17,000 simulated runs show the rule performs comparably to random on coverage but outperforms other methods on endorsement mass and robustness under adversarial pressure.
The paper studies how to safely delegate action approval to multiple AI reviewers when the reviewers themselves may be misaligned. It introduces a weaker condition—k‑robust coalitional alignment—under which a threshold rule that tolerates up to k disapprovals guarantees that the principal’s expected utility is at least as good as a baseline policy. The authors extend this characterization to sequential decision‑making in discounted MDPs and show that full‑panel coverage of reward functions ensures safety in Nash equilibria, while more permissive thresholds can lead to unsafe outcomes. Experiments demonstrate that collective review can remain sound even when individual reviewers are not fully aligned, provided some disapprovals are allowed.
The paper introduces a derandomization framework for stochastic majority vote classifiers, converting PAC‑Bayesian guarantees into deterministic majority vote guarantees. By applying disintegrated PAC‑Bayesian theory to the space of vote weight vectors, the authors derive two families of high‑probability generalization bounds for both data‑independent and data‑dependent ensembles. These bounds naturally lead to a self‑bounding learning algorithm that optimizes deterministic majority vote performance.
The paper examines how an agent’s probability report is evaluated twice—once by a strictly proper scoring rule and again by an approval rule that determines a decision. It shows that when the approval rule is welfare‑maximizing, it cannot be affine, yet the resulting distortion is predictable and can be mitigated by a reserve report that neutralizes the cost of pretending to be the marginal type. A Lipschitz rule with a single kink achieves first‑best welfare, while smooth rules cannot, and the key constraint is the steepness of the rule rather than its smoothness.
arXiv:2608.23247v1 Announce Type: cross Abstract: We introduce multi-winner voting with argumentative ballots (MVArg) and investigate theoretical properties. As our conceptual contribution, we genera...
arXiv:2608.29308v1 Announce Type: cross Abstract: In metric social choice, each voter ranks a set of $m$ candidates by her distance to them in an unknown metric space. The cost of a candidate is its...
arXiv:2610.00577v1 Announce Type: cross Abstract: In a district-based election, N voters are partitioned into k districts, and each voter votes for one of m candidates. Each district elects a winner...
StepCOPS is a new method for selecting a language‑model policy from many checkpoints, prompts, and decoding rules by providing closed‑testing lower‑tail certificates. It uses an independent proposal split to nominate a lower‑tail floor for each candidate, applies exact binomial tests on a fresh certification split, and employs Holm’s step‑down procedure to certify a set of floors. In experiments across 24 configurations and 11 benchmarks, StepCOPS achieves 96.4% selected‑policy coverage, raises the certified floor by 1.5 points over prior methods, stays 0.6 points below a large‑reference jury oracle, and abstains in 2.4% of trials.
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.