Voting Rules Research Articles

Breaking the Metric Voting Distortion Barrier

We consider the following well-studied problem of metric distortion in social choice. Suppose that we have an election with n voters and m candidates located in a shared metric space. We would like to design a voting rule that chooses a candidate whose average distance to the voters is small. However, instead of having direct access to the distances in the metric space, the voting rule obtains, from each voter, a ranked list of the candidates in order of distance. Can we design a rule that, regardless of the election instance and underlying metric space, chooses a candidate whose cost differs from the true optimum by only a small factor (known as the distortion )? A long line of work culminated in finding optimal deterministic voting rules with metric distortion 3. However, for randomized voting rules, there is still a significant gap in our understanding: even though the best lower bound is substantially lower at 2.112, the best upper bound is still 3, which is attained even by simple rules such as Random Dictatorship. Finding a randomized rule that guarantees distortion 3 - ɛ for some constant ɛ has been a major challenge in computational social choice, as prevalent approaches to designing voting rules are known to be insufficient. In particular, such a voting rule must use information beyond aggregate comparisons between pairs of candidates, and cannot only assign positive probability to candidates that are voters’ top choices. In this work, we give a rule that guarantees distortion less than 2.753. To do so, we study a handful of voting rules that are new to the problem. One is Maximal Lotteries , a rule based on the Nash equilibrium of a natural zero-sum game that dates back to the 1960s. The others are novel rules that can be thought of as hybrids of Random Dictatorship and the Copeland rule. None of these rules can beat distortion 3 alone; however, a careful randomization between Maximal Lotteries and any of the novel rules can.

Individual representation in approval-based committee voting

AbstractWhen selecting multiple candidates based on approval preferences of voters, the proportional representation of voters’ opinions is an important and well-studied desideratum. Existing criteria for evaluating the representativeness of outcomes focus on groups of voters and demand that sufficiently large and cohesive groups are “represented” in the sense that candidates approved by some group members are selected. Crucially, these criteria say nothing about the representation of individual voters, even if these voters are members of groups that deserve representation. In this paper, we formalize the concept of individual representation (IR) and explore to which extent, and under which circumstances, it can be achieved. We show that checking whether an IR outcome exists is computationally intractable, and we verify that all common approval-based voting rules may fail to provide IR even in cases where this is possible. We then focus on domain restrictions and establish an interesting contrast between “voter interval” and “candidate interval” preferences. This contrast can also be observed in our experimental results, where we analyze the attainability of IR for realistic preference profiles.

Internal Self-Determination in Ethiopia

Abstract This paper discusses cluster statehood and its implications on minority ethnic groups. Using the three statehood exercises (South-Western Ethiopian Peoples, Southern Ethiopia and Central Ethiopian regional state) as a case study, this paper highlights the main issues and challenges minority ethnic groups face in the process of statehood exercise. These include the application of the simple majority vote rule in a statehood referendum, the implication of cluster statehood on minority ethnic groups’ rights, the challenge of clustering ethnic minorities without their wishes and restructuring minorities’ ethnic administrative boundaries without a referendum. It concludes that the simple majority followed in statehood referendum makes the role of minority ethnic groups rubber-stamp rather than reflect their real interests. Creating the Central Ethiopian State without a referendum is a flagrant disregard of the constitutional procedure and hinders the full political authority of minority nnp s to decide on their affairs.

Cellular automaton model of self-healing

We propose a simple cellular automaton model of a self-healing system and investigate its properties. In the model, the substrate is a two-dimensional checkerboard configuration which can be damaged by changing values of a finite number of sites. The cellular automaton we consider is a checkerboard voting rule, a binary rule with Moore neighborhood which is topologically conjugate to the majority voting rule. For a single-color damage (when only cells in the same state are modified), the rule always fixes the damage. For a general damage, when it is localized inside a 3 × 3 square, the rule also fixes it always. When the damage is inside of a larger n × n square, the efficiency of the rule in fixing the damage becomes smaller than 100 % , but it remains better than 98 % for n ≤ 5 and better than 75 % for n ≤ 7 . We show that in the limit of infinite n the efficiency tends to zero.

Equitable, neutral, and efficient voting rules

In the context of voting, Moulin (1980) establishes that anonymity, neutrality, and efficiency are often incompatible unless one accepts indecision (i.e. ties). We show that versions of this incompatibility continue to hold for a natural weakening of anonymity proposed by Bartholdi et al. (2021) called equity. As equity is a relatively weak fairness requirement, the tension between fairness and efficiency in voting is deeper than previously established.

Empowered minority shareholders and corporate cash dynamics: Empirical evidence from China's online voting reform

This study examines whether enhanced minority shareholder voting influences corporate liquidity management. Using China's Rules for the Implementation of Online Voting (Rules) as a natural experiment, we find that increased minority shareholder participation leads to a significant reduction in corporate cash hoarding. By enabling their active participation, the Rules curtails tunneling activities, especially in firms with excess cash reserves, thereby mitigating expropriation by controlling shareholders. This effect is notably stronger in firms with weaker external monitoring mechanisms. Our findings also reveal that empowered minority shareholders contribute to more efficient cash allocation, reflected in higher market valuations of cash holdings and a quicker adjustment toward optimal cash levels. Overall, this study highlights the economic benefits of strengthened minority voting rights in mitigating agency problems and promoting value-enhancing liquidity management strategies.

Brake fault diagnosis using a voting ensemble of machine learning classifiers

Brake fault diagnosis is a crucial aspect of enhancing driving safety, as various faults such as air in brake fluid, brake oil spill, reservoir leak, mechanical fade and distinct types of brake pad wear can compromise vehicle safety. This study presents a method for timely fault detection by analyzing vibration signals. Vibration signals for normal and faulty conditions were captured using a hydraulic brake test setup equipped with a piezoelectric transducer and data acquisition system. Feature extraction was performed using an auto-regressive moving average (ARMA) model. The performance of five different classifiers namely, random forest (RF), Naive Bayes (NB), instance-based k-nearest neighbours (IBk), logistic model trees (LMT) and J48 decision tree was evaluated. The LMT classifier achieved the highest accuracy at 95.00 % followed by IBk, RF, J48 and NB with accuracies of 92.00 %, 90.00 %, 90.00 % and 87.00 %. To further improve the diagnosis accuracy, a voting-based ensemble approach was employed by combining two, three, four and five classifiers with the application of five different voting strategies. The results obtained showcase that a combination of three classifiers LMT, IBk and NB utilizing the majority voting rule yielded an enhanced classification accuracy of 98.00 % highlighting the effectiveness of this ensemble method in brake fault diagnosis. A similar classification accuracy of 98.00 % was achieved by four (LMT-IBk-RF-J48) and five (IBk-RF-MLP-J48-NB) classifier ensembles; however, considering the computational complexity three classifier ensembles were selected. Whilst two classifier ensembles (IBk-J48) achieved a maximum classification accuracy of 96.00 %.

Empirical insights into enabling and impeding factors for increasing citizen investments in renewable energy communities

This study empirically investigates factors that alternatively enable or impede citizen investments in renewable energy communities (RECs). These factors include key ownership and governance features of REC business models following a strong market logic. Analyzing data from a survey of 1022 adults in France through a discrete choice experiment and a contingent valuation experiment on stated investments in RECs, we find that participants value the opportunity to delegate their voting rights to a trustee but dislike a capital-based voting rule and co-investments by firms. While a capital-based voting rule weakens the stated propensity to invest, it increases the invested amount for participants who are willing to invest some amount. Higher rates of return and higher shares of self-consumption of electricity spur stated investments. These findings offer insights that can inform the transformation of business models to facilitate the scaling-up of RECs. Unless a REC business model with the ownership and governance features considered in this study generates higher rates of return, it appears to face a trade-off between lower citizen participation rates and the leveraging of higher investment amounts from firms, municipalities, and potentially also citizens.

Some further results on random OBIC rules

We study the structure of probabilistic voting rules that are ordinal Bayesian incentive compatible (OBIC) with respect to independently distributed prior beliefs that can be considered generic (Majumdar and Sen (2004)). We first identify a class of priors, such that for each prior in that class there exists a probabilistic voting rule that puts a positive probability weight on “compromise” candidates. The class of priors include generic priors. Next, we consider a class of randomized voting rules that have a “finite range”. For this class of rules, we identify an appropriate generic condition on priors such that, any rule in this class is OBIC with respect to a prior satisfying the generic condition if and only if the rule is a random dictatorship.

On the (robust) ex post stability of constitutions

AbstractThis paper explores the concept of ex post stability in voting systems, shifting the focus from the traditional ex ante perspective, where preferences are uncertain, to a setting where agents' preferences are fully known. We offer a comprehensive characterization of ex post stable constitutions within deterministic voting rule environments. We bridge the theoretical gap between ex post and ex ante stability, shedding light on their intricate relationship and implications for the design and evolution of voting systems. Additionally, our research further evaluates the resilience of these constitutions to changes in the voting environment, particularly their flexibility in response to varying alternative rule sets. This study not only contributes to a deeper understanding of constitutional stability but also provides insights into the dynamics of collective decision‐making processes.

VOTING PROTOCOLS AS INFORMAL JUDICIAL INSTITUTIONS: THE POLITICS OF ENFORCEABILITY AND STRATEGIC BREACHING

AbstractConstitutional courts operate under a framework of formal and informal rules. While formal rules have been extensively studied, our understanding of informal rules remains limited. Courts often rely on internal practices, traditions and unwritten customs developed over time, posing a significant challenge due to their hidden nature. Numerous constitutional courts lack detailed voting protocols in their statutes and internal regulations, leaving essential aspects to the court's discretion, such as, inter alia, the voting order, deliberation style, outcome versus issue voting and tie-breaking protocols. By employing a case study of strategic breaching of informal voting protocols in the Mexican Supreme Court, this article highlights the complexity of enforcing informal voting rules given that external actors may be unaware of them, along with other factors. Even when informal rules are broadly known, certain circumstances may diminish the efficacy of informal sanctions addressing their breach. Thus, key judicial players, such as chief justices or judge-rapporteurs, may take advantage of the informal rules of voting protocols to advance their policy preferences.

How Would Preferential Voting Have Changed Taiwan’s 2024 Presidential Election?

Would the results of Taiwan’s 2024 presidential election have differed under preferential voting rules? Observers assume that the split in the opposition virtually guaranteed Lai Ching-te’s victory, but no analysis has aimed to measure ranked preferences across the three presidential candidates. This analysis combines original pre-election survey data that includes preference ordering of candidates with simulation analysis of the actual election results under the alternative vote and Borda count systems. The original survey finds Ko Wen-je would have won under both preferential systems, while simulating preference ordering using the 2024 election results suggests Lai would have won under alternative vote but lost to Hou You-ih under Borda count.

Information manipulation and majority rule

This paper studies endogenous institution in the presence of information manipulation. We show majority rule discourages information manipulation, reducing Type II errors in collective decision-making. In equilibrium, the optimal majority rule strategically balances the reduction in Type II errors associated with discouraging information manipulation with the potential increase in Type I errors. When it comes to green environmental issues, member countries often adopt a majority rule voting system. This is because these issues typically involve high inherent risks that affect all countries on a global scale, and low heterogeneity among members in terms of the desire to address these challenges.

Does consensus contour improve robustness and accuracy in 18F-FDG PET radiomic features?

PurposeThe purpose of our study is to validate the robustness and accuracy of consensus contour in 2-deoxy-2-[18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F]fluoro-D-glucose (18\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$^{18}$$\\end{document}F-FDG) PET radiomic features.Methods225 nasopharyngeal carcinoma (NPC) and 13 extended cardio-torso (XCAT) simulated data were enrolled. All segmentation were performed with four segmentation methods under two different initial masks, respectively. Consensus contour (ConSeg) was then developed using the majority vote rule. 107 radiomic features were extracted by Pyradiomics based on segmentation and the intraclass correlation coefficient (ICC) was calculated for each feature between masks or among segmentation, respectively. In XCAT ICC between segmentation and simulated ground truth were also calculated to access the accuracy.ResultsICC varied with the dataset, segmentation method, initial mask and feature type. ConSeg presented higher ICC for radiomic features in robustness tests and similar ICC in accuracy tests, compared with the average of four segmentation results. Higher ICC were also generally observed in irregular initial masks compared with rectangular masks in both robustness and accuracy tests. Furthermore, 19 features (17.76%) had ICC ≥ 0.75 in both robustness and accuracy tests for any of the segmentation methods or initial masks. The dataset was observed to have a large impact on the correlation relationships between radiomic features, but not the segmentation method or initial mask.ConclusionsThe consensus contour combined with irregular initial mask could improve the robustness and accuracy in radiomic analysis to some extent. The correlation relationships between radiomic features and feature clusters largely depended on the dataset, but not segmentation method or initial mask.

Invalid Votes as Protest: Compulsory Voting and the Democratic Context in Ecuador

AbstractInvalid votes are pervasive in many democracies and can threaten the legitimacy of elections. While often associated with factors such as choice complexity and disengagement, especially under compulsory voting rules, intentional ballot spoiling can also signal a political protest. We examine the argument that spoiling ballots as protest should occur under compulsory voting where voters perceive limited legitimacy to the electoral process and a lack of viable options. Using municipal elections in Ecuador, a context featuring both compulsory voting and a period of democratic erosion, we examine subnational variation in both spoiled and blank ballots. While factors related to sophistication and choice complexity explain variation in both forms of invalid voting, our results also reveal patterns consistent with the expectation that spoiled ballots reflect a protest of a lack of competition in the political system. Specifically, we find that areas opposed to Rafael Correa’s dominant party had a higher rate of spoiled ballots in the 2014 mayoral elections, a period when party dominance was consolidated and competition weak. This pattern declined in 2019 when the system’s competitiveness increased. A separate analysis of the 2019 elections for the Correa-era participatory governance body suggests continued use of spoiled ballots as protest by opposition voters, but primarily in higher connectivity areas where the online coordination on use of this tactic may have had greater influence. Overall, our study sheds light on factors shaping invalid vote patterns in compulsory systems and highlights spoiled ballots’ potential as a means to signal protest under conditions where some voters would perceive a decline in the legitimacy of democratic institutions.

Designing Digital Voting Systems for Citizens: Achieving Fairness and Legitimacy in Participatory Budgeting

Participatory Budgeting (PB) has evolved into a key democratic instrument for resource allocation in cities. Enabled by digital platforms, cities now have the opportunity to let citizens directly propose and vote on urban projects, using different voting input and aggregation rules. However, the choices cities make in terms of the rules of their PB have often not been informed by academic studies on voter behaviour and preferences. Therefore, this work presents the results of behavioural experiments where participants were asked to vote in a fictional PB setting. We identified approaches to designing PB voting that minimise cognitive load and enhance the perceived fairness and legitimacy of the digital process from the citizens’ perspective. In our study, participants preferred voting input formats that are more expressive (like rankings and distributing points) over simpler formats (like approval voting). Participants also indicated a desire for the budget to be fairly distributed across city districts and project categories. Participants found the Method of Equal Shares voting rule to be fairer than the conventional Greedy voting rule. These findings offer actionable insights for digital governance, contributing to the development of fairer and more transparent digital systems and collective decision-making processes for citizens.

Air leaks fault detection in maintenance using machine learning

PurposeThe objective of this paper is to develop a condition-based maintenance (CBM) scheme for pneumatic cylinders. The CBM scheme will detect two common types of air leaking failure modes and identify the leaky/faulty cylinder. The successful implementation of the proposed scheme will reduce energy consumption, scrap and rework, and time to repair.Design/methodology/approachEffective implementation of maintenance is important to reduce operation cost, improve productivity and enhance quality performance at the same time. Condition-based monitoring is an effective maintenance scheme where maintenance is triggered based on the condition of the equipment monitored either real time or at certain intervals. Pneumatic air systems are commonly used in many industries for packaging, sorting and powering air tools among others. A common failure mode of pneumatic cylinders is air leaks which is difficult to detect for complex systems with many connections. The proposed method consists of monitoring the stroke speed profile of the piston inside the pneumatic cylinder using hall effect sensors. Statistical features are extracted from the speed profiles and used to develop a fault detection machine learning model. The proposed method is demonstrated using a real-life case of tea packaging machines.FindingsBased on the limited data collected, the ensemble machine learning algorithm resulted in 88.4% accuracy. The algorithm can detect failures as soon as they occur based on majority vote rule of three machine learning models.Practical implicationsEarly air leak detection will improve quality of packaged tea bags and provide annual savings due to time to repair and energy waste reduction. The average annual estimated savings due to the implementation of the new CBM method is $229,200 with a payback period of less than two years.Originality/valueTo the best of the authors’ knowledge, this paper is the first in terms of proposing a CBM for pneumatic systems air leaks using piston speed. Majority, if not all, current detection methods rely on expensive equipment such as infrared or ultrasonic sensors. This paper also contributes to the research gap of economic justification of using CBM.

SUSTAINABILITY TRANSPARENCY IN SOVEREIGN WEALTH FUNDS: BENCHMARK AND CASE STUDY APPROACH

The COVID-19 pandemic and the current food, energy, and safety crisis caused by the war in Ukraine strongly affected the existing gap in Sustainable Development Goals funding. Restoring global sustainability progress and the 2030 Agenda requires reshaping responsible investment architecture and rethinking the role of sovereign wealth funds as the largest investors in this process.The current study explores the ways in which the most transparent sovereign wealth funds act towards sustainability values and incorporate environmental, social, governance, and ethical criteria (ESGE) into their investment strategy, portfolio, and reporting. Also, 16 high-ranked funds by the Sustainability Transparency Index (Cavagnetto et al., 2022) were investigated by using the case study and benchmark analysis. Study results demonstrate the similarity of all funds disclosure within some questionnaire criteria like sustainability policy, scoring (voting) rules for decision-making, ESGE criteria, stakeholder engagements, and assurance of the financial information disclosed. Nevertheless, qualitative differences were identified between the sustainability transparency criteria of A and B-ranked funds. There is also huge variation in the implementation of funds’ ethical criteria, sustainability coherence, relevance of sustainability goals, disclosure of specific climate-related goals, and foundation membership criteria.Policy implications derived from the differences in sovereign funds' benchmarks are aimed at enhancing the sustainability context in funds’ operating frameworks, developing impact measurement metrics, prioritizing the most material stakeholder requests, sustainability verification procedures, and standardizing funds’ sustainability reporting. Specific channels, instruments, and regulatory measures towards greater funds’ sustainability transparency and their involvement in bridging the sustainable development funding gap may be the subject of further investigation.

Voting Rights, Agenda Control and Information Aggregation

Abstract This paper examines the comparative properties of voting rules based on the richness of their ballot spaces, assuming a given distribution of voting rights. We focus on how well voting rules aggregate the information dispersed among voters. We consider how different voting rules affect both voters’ decisions at the voting stage and the incentives of the agenda-setter, who decides whether to put the proposal to a vote. Without agenda-setter, the voting efficiency of rules is higher when their ballot space is richer. Moreover, full-information efficiency requires full divisibility of the votes. In the presence of an agenda-setter, we uncover a novel trade-off: in some cases, rules with high voting efficiency provide worse incentives to the agenda-setter to select good proposals. This negative effect can be large enough to wash out the higher voting efficiency of even the most efficient rules.

Defending dominant cooperative probabilistic attack in CRNs by JS-divergence-based improved reputation algorithm

Rapid advances in wireless communication services has made limited spectrum resources increasingly scarce. One promising solution for enhancing spectrum utilization is cooperative spectrum sensing (CSS) in cognitive radio networks (CRNs). However CSS is vulnerable to Byzantine attack. Current researches show that Byzantine attack is easily defended for their fixed attack probability. In this context, we propose an improved attack model called the dominated cooperative probabilistic attack (DCPA) model in the actual situation, building upon the generalized collaborative probabilistic Byzantine attack model. This DCPA model contains auxiliary cooperative attackers (ACAs) who launch attacks and a dominant attacker (DA) who determines ACAs’ attack probability intervals based on their respective credibility. The DCPA model allows ACAs to flexibly launch attacks, without being identified by the traditional reputation defense algorithm, significantly compromising the sensing performance of CSS. To successfully resist the threat posed by the DCPA model to CSS, we propose a JS-divergence-based improved reputation algorithm that can distinguish honest users (HUs) from attackers. This algorithm analyzes two consecutive sensing reports to identify differences in sensing behavior between HUs and attackers. Through Python simulation analysis, we demonstrate that, compared to the generalized cooperative probabilistic attack (CPA) model and the attack-free CSS (AFC) model, the proposed DCPA model is more concealed and significantly more disruptive to the performance of traditional reputation defense algorithms. Furthermore, our approach greatly enhances the performance of CSS by promoting the participation of HUs and suppressing attackers during the final data fusion. And also compared with the PAM2 algorithm, the conventional voting rule (CVR) algorithm and the traditional reputation defense algorithm, our proposed algorithm improves the detection performance by at least 7%, 15% and 50%.