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.
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.
By Noam Hazon, Leora Schmerler, Nicholas Teh
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.
By Muntaser Syed, Markus Zanker, Marius Silaghi
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...
By Ryuta Arisaka, Hirotaka Ono
The paper examines how Preference Inference (PI) models used in large-scale participatory democracy platforms can alter the perceived consensus and minority support by predicting missing votes. It introduces a collective‑centric evaluation framework that assesses whether inferred votes maintain key properties of the overall preference landscape, rather than focusing solely on individual prediction accuracy. Using the largest multilingual dataset to date—four consultations with over 90,000 participants, 1 million votes, and 22 languages—the study finds that models with similar predictive accuracy can differ markedly in how well they preserve the collective structure, underscoring that accuracy alone is insufficient for evaluating PI in democratic contexts.
By Pierre-Antoine Lequeu, Salim Hafid, Paul Lerner, Nazanin Shafiabadi, Laur\`ene Cave, David Mas, Jean-Philippe Cointet, Benjamin Piwowarski, Fran\c{c}ois Yvon
arXiv:2606. 23853v1 Announce Type: cross Abstract: This paper studies strict majority reasoning in finite electorates using so-called $\textit{social decision frames}$: finite sets of voters equipped with distinguished families of coalitions interpreted as those voting blocs evaluated to form a strict majority.
By Lawrence S. Moss, Arthur Paul Pedersen
This paper studies strict majority reasoning in finite electorates using so-called $\textit{social decision frames}$: finite sets of voters equipped with distinguished families of coalitions interpreted as those voting blocs evaluated to form a strict majority. A coherence criterion for qualitative majority judgments is identified and shown to give an exact characterization for representability of strict majorities by finitely additive measures.
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...
By Koustav De, Debajyoti Kar, Swagato Sanyal
arXiv:2608. 11500v1 Announce Type: cross Abstract: Full justified representation (FJR) is among the strongest known satisfiable proportionality axioms for approval-based committee elections.
By Nicholas Teh
arXiv:2606. 11692v1 Announce Type: cross Abstract: Deliberative polling promises to improve collective decision-making by exposing shareholders to a broad range of arguments before they vote.
By Rwaida Alssadi, Khulud Alawaji, Balaji Kasula, Muntaser Syed, Badria Alfurhood, Markus Zanker, Marius Silaghi
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.
By Julien Bastian (LabHC), Benjamin Leblanc (LabHC, UJM, MALICE), Pascal Germain (LabHC, UJM, MALICE), Amaury Habrard (LabHC, UJM, MALICE), Guillaume Metzler (ERIC), Emilie Morvant (LabHC), Paul Viallard (MALT)
arXiv:2606. 08098v1 Announce Type: new Abstract: Majority voting over sampled answers is the dominant unsupervised aggregator for multi-sample LLM inference.
By Yasushi Sakai, Allen Song, Kent Larson