Abstract This article examines the adoption of voting methods designed to support individuals with intellectual disabilities in elections. It focuses on two widely used approaches, frequently explored in scholarly discourse: assisted voting and proxy voting. Both of these voting methods rely on third-party involvement and therefore require the consideration of the prohibition of plural voting in the Australian Constitution. The article concludes that while assisted voting and a limited form of proxy voting-where the proxy must strictly follow the elector’s explicit instructions-are constitutional, proxy voting becomes unconstitutional if the elector is unable to communicate their electoral judgment. Assisted voting therefore emerges as the most practical and constitutionally compliant option.

Abstract This paper investigates the effects of campaign finance rules on electoral outcomes. In French local elections, candidates competing in districts above 9,000 inhabitants face spending limits and are eligible for public reimbursement. Using an RDD around the population threshold, we find that these rules increase competitiveness and benefit the runner-up of the previous race as well as new candidates in departmental elections, while leaving the polarization of results and winners’ representativeness and quality unaffected. Incumbents are less likely to get reelected because they are less likely to run and obtain a lower vote share, conditional on running. These results appear to be driven by the reimbursement of campaign expenditures, not spending limits. We do not find such effects in municipal elections, which we attribute to higher spending, decreasing marginal returns of campaign money, and the use of a proportional list system instead of plurality voting.

Abstract Simple games in partition function form are used to model voting situations where a coalition being winning or losing might depend on the way players outside that coalition organize themselves. Such a game is called a plurality voting game if in every partition there is at least one winning coalition. In the present paper, we introduce an equal impact power index for this class of voting games and provide an axiomatic characterization. This power index is based on equal weight for every partition, equal weight for every winning coalition in a partition, and equal weight for each player in a winning coalition. Since some of the axioms we develop are conditioned on the power impact of losing coalitions becoming winning in a partition, our characterization heavily depends on a new result showing the existence of such elementary transitions between plurality voting games in terms of single embedded winning coalitions. The axioms restrict then the impact of such elementary transitions on the power of different types of players.

Abstract We propose a new electoral system, Mixed Local-Proportional (MLP), that reconciles reforms beneficial for the polity with existing political interests. The MLP system maintains the current distribution of seats among parties while addressing problematic issues with the existing Open-List Proportional Representation (OLPR) system by engineering OLPR’s intra-party properties. Key features include dividing districts into subdistricts with plurality voting, allocating proportional seats using a restricted Jefferson-D’Hondt method, and reducing the number of candidates per party. We explain the MLP system’s mechanics, simulate election results based on recent election data, and discuss variants. Expected political consequences include improved territorial representation, reduced intra-party competition, reduced voter cognitive overload, and stronger ties between voters and MPs. The Polish Parliamentary Committee on Electoral Reform accepted the proposal for further processing.

Multilabel learning deals with datasets where each sample is associated with multiple labels. It is commonly assumed that label correlations should be well exploited to build an effective multilabel classifier. Moreover, the class imbalance problem occurs in many multilabel datasets and should be tackled to reduce the classification bias. While many multilabel learning methods have been proposed, research on imbalanced multilabel learning (IMLL) is relatively deficient. To address these issues, we exploit the value of meta-learned confidences, i.e., the prediction confidences iteratively updated over the out-of-bag samples, for IMLL. First, such meta-confidences can be fused to the original feature space to learn high-order label correlations. Second, meta-confidences can be used to calibrate the prediction results to alleviate class imbalance. Motivated by these, we propose an ensemble learning method named meta-confidence ensemble (MCE) for IMLL. Specifically, MCE iteratively makes bootstrap replicates of the multilabel training set, leverages the out-of-bag samples to generate meta-confidences, and fuses them to the original feature space to learn label correlations. A sparse projection method is presented to avoid overfitting and improve the ensemble diversity. Finally, the prediction result of an unseen sample is determined by the calibrated plurality vote of MCE's base classifiers. Extensive experiments demonstrated the effectiveness and superiority of MCE for IMLL. Codes have been made publicly available at https://github.com/ CUHKSZ-NING/MCEClassifier.

The inclusive classification optimization model (ICOM) is an advanced fusion model specifically designed for large-scale land use and land cover classification. It automatically derives training samples from the intersection areas of five available top-tier classification products, and validation samples from their union areas. The reconciliation index is designed to refine the samples and aid in selecting the most suitable composite approach of the Landsat images for classification. ICOM further employs six classifiers from Google Earth Engine to comprehensively explore classification details of the customized Landsat images, and utilizes an accuracy-weighted plurality voting approach to integrate all the 11 classification results into a final optimized outcome. These accuracy indices comprises traditional metrics including producer’s accuracy and user’s accuracy, alongside a newly designed index of matching accuracy, which specifically assesses the capability of each land-cover class in accurately reflecting real-world conditions. Results demonstrate that that ICOM effectively capitalizes on the strengths of the incorporated high-quality classification products and classifiers, with the integrated classification output significantly enhancing both overall accuracy and localized classification performance.

This study presents a comprehensive evaluation of multilingual sentiment categorization performance using locally deployed large language models (LLMs) on consumer-grade hardware, focusing on GDPR-compliant implementation scenarios. Through extensive Monte Carlo validation involving 947,700 classifications over 702 iterations, we demonstrate significant performance capabilities across English, Italian, and Japanese languages while operating within consumer hardware constraints. Using lightblue/suzume-llama-3-8B-multilingual-orpo-borda-half on a Python-based llama-cpp framework on consumer NVIDIA GPU hardware, English achieved 96.3% accuracy (95% CI: 0.963–0.964), with Italian and Japanese showing strong performance at 92.2% (95% CI: 0.921–0.922) and 90.7% (95% CI: 0.906–0.908) respectively. Notably, our analysis demonstrates that plurality voting can achieve extremely high confidence levels across all languages, suggesting an efficient approach to improving classification reliability without requiring extensive computational resources.

ABSTRACT “History doesn’t repeat itself, but it often rhymes,” said humorist and social critic Mark Twain. Simply put, history often follows recurring cycles, enabling us to identify patterns that will likely repeat. Such supposed steadiness should bode well for prediction. Nevertheless, regarding structural election forecasts, most projections rely on short-term political fundamentals measuring macroeconomic performance or government or leader popularity. In this contribution, we take a structural approach but eschew any macroeconomic or popularity measure and instead rely on historical and structural patterns to predict the 2025 German Federal Election. Using seemingly unrelated regression, our model predicts the vote share of Germany’s two largest blocs—the Union and the SPD—and All Others combined across 19 elections between 1953 and 2021 with solid accuracy (correctly predicting the winning bloc three out of four times), creating circumstances to assume that political history may be a helpful guide as to how the 2025 contest may pan out. Our ex ante central projection for the 2025 German federal election foresees a cliffhanger race, with point estimates suggesting that the Union and the SPD will win 26% of the vote each and All Others 48%, departing from the dominant narrative of the opinion polls of a clear CDU/CSU plurality vote victory and substantial losses for the SPD. The political history model suggests that the formation of another grand coalition is possible.

ABSTRACT: Electoral systems in ethnically divided democracies can accentuate or moderate political disagreement. In Northern Ireland, the Westminster electoral system is non-proportional, whereas local elections use a proportional system. Parties and voters must think strategically in Westminster elections to achieve their desired result. Prudent party withdrawals, strategic voting and differential bloc-identity turnout had significant impacts on the 2024 Westminster election result compared to the 2023 local elections. Notional Westminster constituency results were estimated for the 2023 local elections (which used different electoral contest areas) to enable comparison. Turnout increases occurred where there were close unionist–Alliance or intra-unionist battles. Republican-dominated constituencies saw turnout decrease. Most constituency results were impacted by an idiosyncratic blend of external political issues, prudent party withdrawal and strategic voting. The implications of unionist electoral reinvigoration and republican electoral stasis are discussed, in the context of likely increasing input by the next Irish government into the reunification debate.

Abstract DNA is considered as a prospective candidate for the next‐generation data storage medium, due to its high coding density, long cold‐storage lifespan, and low energy consumption. Despite these advantages, challenges remain in achieving high‐fidelity, fully integrated, and cost‐efficient DNA storage system. In this study, a homemade digital microfluidic (DMF)‐based compact DNA data storing pipeline is orchestrated to complete the entire process from the synthesis to the sequencing. The synthetic half employs phosphoramidite chemistry on 200 nm magnetic beads (MBs), where the dimethyltrityl protecting group is removed by droplet manipulation of trichloroacetic acid. The sequencing counterpart relies on pyrophosphate releasing originated from polymerase‐catalyzed primer extension, which leads to photon‐countable chemiluminescence (CL) signal in 2.5‐μL drops of trienzyme cascading reactions. Further by DNA denaturation, repeated pyrosequencing plus plurality voting can improve the nucleobase accuracy beyond 95 %. As a proof‐of‐concept trial, semantic information is saved in DNA via the Huffman coding algorithm plus the Reed‐Solomon error‐correction, and then robustly retrieved from this streamlined platform. As a result, it took a net total of approximately 6.5 h to writing and reading 8 bytes of data, that equal to a storaging speed of 49 min/byte, much quicker than the previously reported 2.8–4.2 h/byte. This bead‐based miniaturized device promises an unattended protocol for achieving high‐throughput, full‐packaged, and above all, neatly precision DNA storage.

DNA is considered as a prospective candidate for the next-generation data storage medium, due to its high coding density, long cold-storage lifespan, and low energy consumption. Despite these advantages, challenges remain in achieving high-fidelity, fully integrated, and cost-efficient DNA storage system. In this study, a homemade digital microfluidic (DMF)-based compact DNA data storing pipeline is orchestrated to complete the entire process from the synthesis to the sequencing. The synthetic half employs phosphoramidite chemistry on 200 nm magnetic beads (MBs), where the dimethyltrityl protecting group is removed by droplet manipulation of trichloroacetic acid. The sequencing counterpart relies on pyrophosphate releasing originated from polymerase-catalyzed primer extension, which leads to photon-countable chemiluminescence (CL) signal in 2.5-μL drops of trienzyme cascading reactions. Further by DNA denaturation, repeated pyrosequencing plus plurality voting can improve the nucleobase accuracy beyond 95 %. As a proof-of-concept trial, semantic information is saved in DNA via the Huffman coding algorithm plus the Reed-Solomon error-correction, and then robustly retrieved from this streamlined platform. As a result, it took a net total of approximately 6.5 h to writing and reading 8 bytes of data, that equal to a storaging speed of 49 min/byte, much quicker than the previously reported 2.8-4.2 h/byte. This bead-based miniaturized device promises an unattended protocol for achieving high-throughput, full-packaged, and above all, neatly precision DNA storage.

State-of-the-art tools for classifying metagenomic sequencing reads provide both rapid and accurate options, although the combination of both in a single tool is a constantly improving area of research. The machine learning-based Naïve Bayes Classifier (NBC) approach provides a theoretical basis for accurate classification of all reads in a sample. We developed the multithreaded Minimizer-based Naïve Bayes Classifier (MNBC) tool to improve the NBC approach by applying minimizers, as well as plurality voting for closely related classification scores. A standard reference- and test-sequence framework using simulated variable-length reads benchmarked MNBC with six other state-of-the-art tools: MetaMaps, Ganon, Kraken2, KrakenUniq, CLARK, and Centrifuge. We also applied MNBC to the "marine" and "strain-madness" short-read metagenomic datasets in the Critical Assessment of Metagenome Interpretation (CAMI) II challenge using a corresponding database from the time. MNBC efficiently identified reads from unknown microorganisms, and exhibited the highest species- and genus-level precision and recall on short reads, as well as the highest species-level precision on long reads. It also achieved the highest accuracy on the "strain-madness" dataset. MNBC is freely available at: https://github.com/ComputationalPathogens/MNBC.

There are multiple models of party formation in the political economy literature. However, most of these works consider individuals as parties and do not model parties as a group of candidates. In this paper, we follow the latter approach and assume that parties form as a result of mutually agreeable links between candidates. We consider a citizen-candidate model of electoral competition where candidates decide whether they want to participate in election alone or offer links to adjacently placed candidates on a one-dimensional policy space. We characterize one-party and two-party equilibrium and show that no multi-party equilibrium exists with three or more parties. We provide conditions on the rents of winning with respect to the cost of participating in election which also depend on the number of candidates. We provide new insights which explain party formation from the perspective of group formation. Our results confirm the Duverger’s law and are consistent with empirical evidence on plurality voting systems.

An extensive study of security games with strategic informants

Federated learning allows collaborative clients to solve a machine-learning problem while preserving data privacy. Recent studies have tackled various challenges in federated learning, but the joint optimization of communication overhead, learning reliability, and deployment efficiency is still an open problem. To this end, we propose a new scheme named federated learning via plurality vote (FedVote). In each communication round of FedVote, clients transmit binary or ternary weights to the server with low communication overhead. The model parameters are aggregated via weighted voting to enhance the resilience against Byzantine attacks. When deployed for inference, the model with binary or ternary weights is resource-friendly to edge devices. Our results demonstrate that the proposed method can reduce quantization error and converges faster compared to the methods directly quantizing the model updates.

Abstract Are there group decision methods which (i) give everyone, including minorities, an equal share of effective decision power even when voters act strategically, (ii) promote consensus and equality, rather than polarization and inequality, and (iii) do not favour the status quo or rely too much on chance? We describe two non-deterministic group decision methods that meet these criteria, one based on automatic bargaining over lotteries, the other on conditional commitments to approve compromise options. Using theoretical analysis, agent-based simulations and a behavioral experiment, we show that these methods prevent majorities from consistently suppressing minorities, which can happen in deterministic methods, and keeps proponents of the status quo from blocking decisions, as in other consensus-based approaches. Our simulations show that these methods achieve aggregate welfare comparable to common voting methods, while employing chance judiciously, and that the welfare costs of fairness and consensus are small compared to the inequality costs of majoritarianism. In an incentivized experiment with naive participants, we find that a sizable fraction of participants prefers to use a non-deterministic voting method over Plurality Voting to allocate monetary resources. However, this depends critically on their position within the group. Those in the majority show a strong preference for majoritarian voting methods.

Abstract Mill advocates plural voting on instrumentalist grounds: the more competent are to have more votes. At the same time, he regards it as a ‘personal injustice’ to withhold from anyone ‘the ordinary privilege of having his voice reckoned in the disposal of affairs in which he has the same interest as other people’ (Mill 1861a, p. 469). But if electoral voice is justified by its contribution to good governance, why would it be an injustice to deny the vote to those whose use of it would disserve this end? I propose the dual justification view to resolve this tension. Mill holds that electoral voice is to be justified in two complementary ways: both as communicating a person’s interests and perspective in order that they be accommodated in policy deliberations, and as advancing a vision of the common good and influencing the policy of the legislature.

This paper examines an important argument that has received little attention despite its wide implications. This is the claim that judicial review can be equated with plural weighted voting (PwV) because both are justified as instruments to achieve better outcomes, and both violate political equality. We take this argument to be a reductio: given that plural voting is unacceptable, judicial review must be rejected. If correct, this claim threatens to undermine much recent liberal democratic theorising. We argue that none of the obvious routes to distinguish judicial review from PwV offer a convincing way to distinguish these two schemes. Furthermore, this has important implications for how we should understand judicial review. The result is thus significant not only for the particular issues mentioned, but also for our understanding of the role instrumental justifications play in democratic theory.

Instant runoff voting (IRV) has recently gained popularity as an alternative to plurality voting for political elections, with advocates claiming a range of advantages, including that it produces more moderate winners than plurality and could thus help address polarization. However, there is little theoretical backing for this claim, with existing evidence focused on case studies and simulations. In this work, we prove that IRV has a moderating effect relative to plurality voting in a precise sense, developed in a 1-dimensional Euclidean model of voter preferences. We develop a theory of exclusion zones, derived from properties of the voter distribution, which serve to show how moderate and extreme candidates interact during IRV vote tabulation. The theory allows us to prove that if voters are symmetrically distributed and not too concentrated at the extremes, IRV cannot elect an extreme candidate over a moderate. In contrast, we show plurality can and validate our results computationally. Our methods provide new frameworks for the analysis of voting systems, deriving exact winner distributions geometrically and establishing a connection between plurality voting and stick-breaking processes.

This study analyses the voting patterns among the Sarawakian Chinese and their pro-opposition inclination in the 2021 Sarawak State Election (SSE21). It begins by recounting how the national opposition alliances, former Pakatan Harapan (PH) alliances—Barisan Alternatif (BA) and Pakatan Rakyat (PR)—gained popularity among the Chinese voters in Sarawak since the 2006 state elections. The voting trends reveal changes in Chinese voting patterns, from a voting bloc to fragmented votes, and their openness to local opposition parties in the recent state elections. In order to examine the plurality voting pattern and the extent of fragmentation, this research studies two Chinese-majority urban constituencies, Batu Lintang and Bukit Assek in Sarawak. It contends that the Chinese political leanings, including their tendency to embrace regionalism, are profoundly shaped by local and national political developments. In the past two decades, ethnopatronage politics and prevalent corruption practices partly explain the Chinese’s quest for regime change. The Sarawak government’s emphasis on regional identity and multiculturalism in its official narrative nurtures a sense of belonging, which also impacts Chinese identity politics. This article argues that the decision to support minor parties advocating for separatism can be read as strategic voting of the regional minority who feel deprived of their equal rights. It also contends that political fragmentation among the Chinese might persist if PH fails to consolidate its previous support. Otherwise, it spells the decline of Parti Sarawak Bersatu (PSB) and Parti Bumi Kenyalang (PBK). The sustainability of local opposition parties depends on the internal reform of parties. The parties should reduce the influences of personal politics as it tends to breed party-hopping, which places parties’ future or institutional development at risk of uncertainty.