Plurality Voting Research Articles

MNBC: a multithreaded Minimizer-based Naïve Bayes Classifier for improved metagenomic sequence classification.

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.

A citizen-candidate model of party formation

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

Over the past years, game-theoretic modeling for security and public safety issues (also known as security games) have attracted intensive research attention and have been successfully deployed in many real-world applications for fighting, e.g., illegal poaching, fishing and urban crimes. However, few existing works consider how information from local communities would affect the structure of these games. In this paper, we systematically investigate how a new type of players – strategic informants who are from local communities and may observe and report upcoming attacks – affects the classic defender-attacker security interactions. Characterized by a private type, each informant has a utility structure that drives their strategic behaviors.For situations with a single informant, we capture the problem as a 3-player extensive-form game and develop a novel solution concept, Strong Stackelberg-perfect Bayesian equilibrium, for the game. To find an optimal defender strategy, we establish that though the informant can have infinitely many types in general, there always exists an optimal defense plan using only a linear number of patrol strategies; this succinct characterization then enables us to efficiently solve the game via linear programming. For situations with multiple informants, we show that there is also an optimal defense plan with only a linear number of patrol strategies that admits a simple structure based on plurality voting among multiple informants.Finally, we conduct extensive experiments to study the effect of the strategic informants and demonstrate the efficiency of our algorithm. Our experiments show that the existence of such informants significantly increases the defender's utility. Even though the informants exhibit strategic behaviors, the information they supply holds great value as defensive resources. Compared to existing works, our study leads to a deeper understanding on the role of informants in such defender-attacker interactions.

Federated Learning via Plurality Vote.

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.

Fair group decisions via non-deterministic proportional consensus

AbstractAre 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.

What Justifies Electoral Voice? J. S. Mill on Voting

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.

Judicial Review, Plural Weighted Voting, and Democracy

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.

The Moderating Effect of Instant Runoff Voting

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.

From Voting Bloc to Fragmentation: The Chinese Opposition Votes in the 2021 Sarawak State Election

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.

An Adaptation of the Input Doubling Method for Solving Classification Tasks in Case of Small Data Processing

In the era of big data processing, numerous techniques prove valuable for analyzing large-scale datasets. However, the efficient processing of small data is equally crucial, particularly in the context of intelligent mobile systems. This importance stems from resource constraints, real-time requirements, privacy concerns, and the emergence of edge computing paradigms. In this paper, we adapt an existing input doubling method to address classification tasks when facing limited training datasets. The proposed solution is implemented by modifying the procedure for aggregating predicted values, based on the principles of majority voting. This approach differs significantly from the existing method, which relies on averaging prediction results. A flowchart of the adapted method is presented, detailing the procedures for training and application based on the use of a plurality voting scheme. Modeling is conducted on a short, imbalanced dataset for solving express diagnostic tasks in the field of transplantology. The results demonstrate the high effectiveness of employing the adapted input doubling method for classification tasks in transplantology, where acquiring a substantial training dataset for analysis is challenging.

Radar Signal Recognition Based on Bagging SVM

Radar signal recognition under low signal-to-noise ratio (SNR) conditions is a critical issue in modern electronic reconnaissance systems, which face significant challenges in recognition accuracy due to signal diversity. A novel method for radar signal detection based on the bagging support vector machine (SVM) is proposed in this paper.This method firstly utilizes the Choi–Williams distribution (CWD) and the smooth pseudo Wigner-Ville distribution (SPWVD) to obtain different time–frequency images of radar signals, which effectively leverages CWD’s strong time–frequency aggregation and SPWVD’s robust cross-interference resistance. Moreover, histograms of oriented gradient (HOG) features are extracted from time–frequency images to train multiple SVM classifiers by bootstrap sampling. Finally, the performance of each SVM classifier is aggregated using plurality voting to reduce the risk of model overfitting and improve recognition accuracy. We evaluate the effectiveness of the proposed method using 12 different types of radar signals. The experimental results demonstrate that its overall identification rate reaches around 79% at an SNR of −10 dB, and it improves the recognition rate by 5% compared with a single classifier.

ANALYSIS OF ELECTROHYSTEROGRAM SIGNALS AND PREDICTION OF PRETERM BIRTHS USING MACHINE LEARNING

The World Health Organization (WHO) estimates that over 15 million infants are born before the entire period of pregnancy. Over a million neonatal deaths occurred in 2015 as a result of preterm delivery, which is the prime cause for most deaths among children under the age of five. Although preterm birth has no hereditary link, only 10% of preterm deliveries in high-income settings result in deaths, compared to a mortality rate of up to 90% in low-income nations. When preterm cases are discovered, in addition to enhanced care provided at home for the expecting mother, the growing foetus may also benefit from medication, hospitalization for the duration of the pregnancy, or both. Low-income countries also struggle with a lack of access to a comprehensive healthcare system, which makes it difficult to proactively detect premature births. Machine learning techniques have a great potential to improve this situation by providing an intelligent framework for the detection of critical situations and consequently alerting the individual in cases of anomalies. This will require further follow-up with medical specialists. In this work, we investigate the application of deep learning and machine learning techniques for the analysis of electrohysterogram (EHG) data, which are uterine electrical impulses used to detect preterm deliveries. The TPEHG dataset is used to train a variety of machine learning classifiers, such as Support Vector Machines, Logistic Regression, and Decision Trees, as well as Deep Neural Networks like Convolutional Neural Networks and LSTMs. Additionally, we use plurality voting to build an ensemble of various neural networks and binary classifiers that are trained to classify EHG signals. The ensemble machine learning classifier with five base classifiers produced the best results overall, with an accuracy of 98.99%, sensitivity of 98.3%, and specificity of 97.9% outperforming several state-of-the-art algorithms for preterm birth detection.

Causes and Consequences of Post-Election Violence in Kenya

The election of leaders by the people is the aim of democratic procedures. An electoral system is intended to ensure that the outcomes reflect the preferences of the majority of voters, providing elected officials’ legitimacy, and that the results are unchallenged in any form. The aim of this study was to identify the causes of the post-election violence. Voting theory served as the foundation for this work. A descriptive study approach using both qualitative and quantitative methods was used by the researcher. The research was carried out from July 2022 to December 2022. Political parties, political analysts, IEBC members, legislators, and voters made up the study's target population. The researcher employed the stratified random sampling technique. The target population established the strata, and a random sample was taken from each stratum. The researcher used interview guide techniques to collect data, using items that were developed from the study objectives and research questions. Questionnaires and interviews were the two main ways that data were gathered. Voters were handed questionnaires, and interview guidelines were provided for political experts, electoral experts, and IEBC officials. The information gathered through the use of questionnaires was statistically analyzed using descriptive methods. According to the findings, disputed election results, tribalism, inadequate electoral conflict resolution mechanisms, the "winner-takes-it-all" election system, perceived bias of the electoral management body, and voter incitement by politicians are among the main causes of post-election violence in Kenya since 1963. Casualties, internally displaced people, the damage to private and public property, economic instability, and emotional and psychological suffering are only a few of the key effects of the post-election violence in Kenya. The report recommends, among other things, looking at alternative choices, including mixed-member and proportional representation models, since they are more inclusive and produce fair representation, as a remedy for the existing polarizing plurality voting system.

Incentivized choice in large-scale voting experiments

AbstractSurvey experiments that investigate how voting procedures affect voting behavior and election outcomes use hypothetical questions and non-representative samples. We present here the results of a novel survey experiment that addresses both concerns. First, the winning party in our experiment receives a donation to its campaign funds inducing real consequences for voting. Second, we run an online experiment with a Dutch national representative sample (N = 1240). Our results validate previous findings using a representative sample, in particular that approval voting leads to a higher concentration in votes for smaller parties and strengthens centrist parties in comparison to plurality voting. Importantly, our results suggest that voting behavior is not affected by voting incentives and can be equally reliably elicited with hypothetical questions.

Democratic governance and modernity in 21st century co-operatives in Portugal: Frontiers and balances

The legal framework applying to cooperatives is not harmonised in the European Union. Diverging from other legal systems, the Portuguese legal system made the co-operative principles part of the Portuguese Constitution, giving them a mandatory nature, protecting the co-operative identity.As evidenced in the PECOL project, demutualisation, digital transition, diversity, the professionalisation of management, transparency, prevention of conflicts of interest and accountability, investor members and plural voting are the main challenges addressing the principle of democratic member control in the 21st century. The article concludes that these challenges must be addressed in the framework of the principle of democratic management of members. Plural voting can be a response to the heterogeneity of members and that the admission of investor members can contribute both to the professionalisation of co-operative management and co-operatives' sustainability. Portuguese regulation preserves the essential core of the principle of democratic member control.The article's main contribution is to disseminate internationally a legislative experience of hard law regarding the nature of the co-operative principles. It conveys the message that this political-legislative option has not slowed down the adaptation of co-operatives to new challenges.

Ballot Length in Instant Runoff Voting

Instant runoff voting (IRV) is an increasingly-popular alternative to traditional plurality voting in which voters submit rankings over the candidates rather than single votes. In practice, elections using IRV often restrict the ballot length, the number of candidates a voter is allowed to rank on their ballot. We theoretically and empirically analyze how ballot length can influence the outcome of an election, given fixed voter preferences. We show that there exist preference profiles over k candidates such that up to k-1 different candidates win at different ballot lengths. We derive exact lower bounds on the number of voters required for such profiles and provide a construction matching the lower bound for unrestricted voter preferences. Additionally, we characterize which sequences of winners are possible over ballot lengths and provide explicit profile constructions achieving any feasible winner sequence. We also examine how classic preference restrictions influence our results—for instance, single-peakedness makes k-1 different winners impossible but still allows at least Ω(√k). Finally, we analyze a collection of 168 real-world elections, where we truncate rankings to simulate shorter ballots. We find that shorter ballots could have changed the outcome in one quarter of these elections. Our results highlight ballot length as a consequential degree of freedom in the design of IRV elections.

California’s 2021 Gubernatorial Recall: Field Notes

In 2021, the second attempt in California history to recall a governor ended in a resounding victory for the Democratic incumbent. Unlike the recall of Governor Gray Davis in 2003, auspicious political conditions helped enable Gavin Newsom’s survival; these included a large Democratic advantage in voter registration and an all-mail-ballot election that reduced participation costs. Turning points in the process included an extension of the signature-gathering deadline and the emergence of a polemical candidate who helped energize Democratic voters. These developments re-exposed flaws in the state’s recall process and have prompted calls to reform the qualification procedures and the dual ballot format, which permits the election of a potentially anti-majoritarian replacement who receives a small fraction of the plurality vote.

Ultra-low complexity random forest for optical fiber communications.

In this paper, we present an efficient equalizer based on random forest for channel equalization in optical fiber communication systems. The results are experimentally demonstrated in a 120 Gb/s, 375 km, dual-polarization 64-quadrature magnitude modulation (QAM) optical fiber communication platform. Based on the optimal parameters, we choose a series of deep learning algorithms for comparison. We find that random forest has the same level of equalization performance as deep neural networks as well as lower computational complexity. Moreover, we propose a two-step classification mechanism. We first divide the constellation points into two regions and then use different random forest equalizers to compensate the points in different regions. Based on this strategy, the system complexity and performance can be further reduced and improved. Furthermore, due to the plurality voting mechanism and two-stage classification strategy, the random forest-based equalizer can be applied to actual optical fiber communication systems.

STAR Voting, equality of voice, and voter satisfaction: considerations for voting method reform

The vote itself forms the foundation of our political discourse, and how we vote may be the single most important decision in a representative democracy. In the pursuit of more fair and representative elections, we explore common electoral pathologies like vote-splitting, Favorite Betrayal, and the center-squeeze Spoiler Effect, and discuss criteria and methodology for evaluating voting method quality. We introduce STAR Voting for consideration in place of Plurality and Instant Runoff Voting for governmental elections, and compare it to other leading voting method proposals. To evaluate voting method accuracy and strategy resilience, we present the metrics Voter Satisfaction Efficiency (VSE) and Pivotal Voter Strategic Incentive (PVSI). We find that compared to Plurality Voting, all methods tested substantially reduce the need for voters to consider candidate electability but that STAR Voting, Smith/Minimax, the Condorcet method included, and Approval Top Two go further to ensure an equally weighted vote. We conclude that STAR Voting in particular offers a viable, competitive, and compelling proposal for more representative elections.

The case for Instant Runoff Voting

Instant runoff voting (IRV) is an electoral system that produces better elections and more representative outcomes in races with three or more candidates. Used by over 50 American jurisdictions, IRV addresses the challenges of plurality voting in crowded candidate fields. It upholds majority rule, promotes voter engagement, discourages personal attacks, and minimizes strategic voting. IRV is preferable to runoff systems and other proposed single-winner methods. In comparison to runoffs, IRV produces higher, more equitable turnout and reduces costs and demands on voters. In contrast to methods based on rating candidates, voters in IRV elections have incentives to indicate their honest preferences. IRV demonstrates greater political viability and more intuitively fair outcomes than Condorcet methods. For legislative elections, IRV complements the single transferable vote, which is the best way to address the U.S.’s governance and polarization crises, end gerrymandering, and secure fairer representation for all.