Approval-Based Committee Voting in Practice: A Case Study of (over-)Representation in the Polkadot Blockchain

SummaryHow large should a monetary policy committee be? Which voting rule should a monetary policy committee adopt? This paper builds on Condorcet's jury theorem to analyse the relationships between committee size and voting rules in a model where policy discussions are subject to a time constraint. It suggests that in large committees majority voting is likely to enhance policy outcomes. Under unanimity (consensus) it is preferable to limit the size of the committee. Finally, supermajority voting rules are social contrivances that contribute to policy performance in a more uncertain environment, when initial policy proposals are less likely to be correct, or when payoffs are asymmetric.

How large should a monetary policy committee be? Which voting rule should a monetary policy committee adopt? This paper builds on Condorcet's jury threorem to analyse the relationships between committee size and voting rules in a model where policy discussions are subject to a time constraint. It suggests that in large committees majority voting is likely to enhance policy outcomes. Under unanimity (consensus) it is preferable to limit the size of the committee. Finally, supermajority voting rules are social contrivances that contribute to policy performance in a more uncertain environment, when initial policy proposals are less likely to be correct, or when payoffs are asymmetric. JEL Classification: D71, D78, D81, E58

Quelle est la taille optimale d’un comité de politique monétaire ? Quelle est la meilleure règle de vote ? Dans l’esprit du théorème de Condorcet, cet article propose un modèle de prise de décision contenant des phases de délibération pendant lesquels les membres du comité peuvent changer de position. Ce processus de délibération est conclu par une décision qui peut être prise à l’unanimité, à la majorité simple ou à la majorité qualifiée. En termes de performance, la majorité simple apparaît plus efficace pour les comités composés d’un grand nombre de membres. En revanche, décider à l’unanimité n’est profitable que dans les comités de petite taille. D’une manière générale, les règles de super-majorité sont adaptées dans un environnement incertain, lorsque les propositions initiales ont moins de chance d’être correctes, ou dans des problèmes de décision asymétrique, quand la perte liée à l’adoption d’une mauvaise proposition est plus élevée que celle du rejet d’une bonne proposition. Classification JEL : D71, D78, D81, E58.

This paper presents a general framework for building classifiers that deal with short and sparse text & segments by making the most of hidden topics discovered from large-scale data collections. The main motivation of this work is that many classification tasks working with short segments of text & Web, such as search snippets, forum & chat messages, blog & news feeds, product reviews, and book & movie summaries, fail to achieve high accuracy due to the data sparseness. We, therefore, come up with an idea of gaining external knowledge to make the data more related as well as expand the coverage of classifiers to handle future data better. The underlying idea of the framework is that for each classification task, we collect a large-scale external data collection called universal dataset, and then build a classifier on both a (small) set of labeled training data and a rich set of hidden topics discovered from that data collection. The framework is general enough to be applied to different data domains and genres ranging from search results to medical text. We did a careful evaluation on several hundred megabytes of Wikipedia (30M words) and MEDLINE (18M words) with two tasks: Web search domain disambiguation and disease categorization for medical text, and achieved significant quality enhancement.

We study voting rules as a promising option for collective decision making in traffic applications. The aim of our work is to compare the suitability of several voting rules for different traffic applications and to tackle problems which arise when applying voting rules in traffic management. Here, we propose a multi-agent based voting architecture for evaluation of the suitability of voting rules. The design of the voting architecture is informed by the requirements from two applications we intend to study. The J-MADeM architecture is adapted for the development of our architecture. We describe the voting theory model we intend to incorporate in the architecture, the initial applications we plan to investigate and the features of the voting architecture. Furthermore, we outline the first simulation we intend to conduct using the voting architecture, focusing on the aspect of iterative winner determination for the committee voting rules Minisum and Minimax Approval.

Research into design rationale in the past has focused on argumentation-based design deliberations. These approaches cannot be used to support change impact analysis effectively because the dependency between design elements and decisions are not well represented and cannot be quantified. Without such knowledge, designers and architects cannot easily assess how changing requirements and design decisions may affect the system. We apply Bayesian Network Model (BNM), to capture the probabilistic causal relationships between design elements and decisions. We employ three different BNM-based reasoning methods to analyze the trade-off between the conflicting quality attributes. Markov blanket discovery algorithms can be used for quality assessment BNMs. Additionally, work will be done to determine how known optimization methods such as Tabu search may be applied in the context of the proposed framework. Ultimately, the goal is to create a possibility of automatic execution of steps involved in architectural optimization.

This study suggested combined filtering model to eliminate outlier travel time data in transportation information system, and it was based on Median Absolute Deviation and Voting Rule. This model applied Median Absolute Deviation (MAD) method to follow normal distribution as first filtering process. After that, Voting rule is applied to eliminate remaining outlier travel time data after Median Absolute Deviation. In Voting Rule, travel time samples are judged as outliers according to travel-time difference between sample data and mean data. Elimination or not of outliers are determined using a majority rule. In case study of national highway No. 3, combined filtering model selectively eliminated outliers only and could improve accuracy of estimated travel time.Keywords : Travel Time, Outlier Elimination, Filtering Model, Median Absolute Deviation, Voting Rule * 주저자 : 서울시립대학교 교통공학과 연구교수 ** 공저자 : 한국건설기술연구원 연구원 *** 공저자 : 한국건설기술연구원 연구위원**** 공저자 및 교신저자 : 서울시립대학교 교통공학과 교수†논문접수일 : 2013년 10월 21일†논문접수일 : 2013년 11월 11일†논문접수일 : 2013년 11월 22일

We present a unifying framework encompassing a plethora of social choice settings. Viewing each social choice setting as voting in a suitable metric space, we offer a general model of social choice over metric spaces, in which—similarly to the spatial model of elections—each voter specifies an ideal element of the metric space. The ideal element acts as a vote, where each voter prefers elements that are closer to her ideal element. But it also acts as a proposal, thus making all participants equal not only as voters but also as proposers. We consider Condorcet aggregation and a continuum of solution concepts, ranging from minimizing the sum of distances to minimizing the maximum distance. We study applications of our abstract model to various social choice settings, including single-winner elections, committee elections, participatory budgeting, and participatory legislation. For each setting, we compare each solution concept to known voting rules and study various properties of the resulting voting rules. Our framework provides expressive aggregation for a broad range of social choice settings while remaining simple for voters; and may enable a unified and integrated implementation for all these settings, as well as unified extensions such as sybil-resiliency, proxy voting, and deliberative decision making. We study applications of our abstract model to various social choice settings, including single-winner elections, committee elections, participatory budgeting, and participatory legislation. For each setting, we compare each solution concept to known voting rules and study various properties of the resulting voting rules. Our framework provides expressive aggregation for a broad range of social choice settings while remaining simple for voters; and may enable a unified and integrated implementation for all these settings, as well as unified extensions such as sybil-resiliency, proxy voting, and deliberative decision making.

Over the last several years, there has been an emerging interest in the development of wide-area data collection and analysis centers to help identify, track, and formulate responses to the ever-growing number of coordinated attacks and malware infections that plague computer networks worldwide. As large-scale network threats continue to evolve in sophistication and extend to widely deployed applications, we expect that interest in collaborative security monitoring infrastructures will continue to grow, because such attacks may not be easily diagnosed from a single point in the network. The intent of this position paper is not to argue the necessity of Internet-scale security data sharing infrastructures, as there is ample research [13, 48, 51, 54, 41, 47, 42] and operational examples [43, 17, 32, 53] that already make this case. Instead, we observe that these well-intended activities raise a unique set of risks and challenges.We outline some of the most salient issues faced by global network security centers, survey proposed defense mechanisms, and pose several research challenges to the computer security community. We hope that this position paper will serve as a stimulus to spur groundbreaking new research in protection and analysis technologies that can facilitate the collaborative sharing of network security data while keeping data contributors safe and secure.

We apply simple games with alternatives to describe voting mechanisms used in parliamentary organizations. As a matter of example, we analyze the Catalonia Parliament. Special attention is paid to abstention, which is allowed in most voting rules but is not taken into account when a voting rule is modelled as a simple classical game. To measure the power of each voter in each game, the extended Shapley–Shubik index is considered. (© 2008 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)

Ecological Momentary Assessment (EMA) enables the real-time capture of health-related behaviours, their situational contexts, and associated subjective experiences. This study aimed to evaluate the feasibility of an EMA targeting physical and eating behaviours, optimise its protocol, and provide recommendations for future large-scale EMA data collections. The study involved 52 participants (age 31±9 years, 56% females) from Czechia, France, Germany, and Ireland completing a 9-day free-living EMA protocol using the HealthReact platform connected to a Fitbit tracker. The EMA protocol included time-based (7/day), event-based (up to 10/day), and self-initiated surveys, each containing 8 to 17 items assessing physical and eating behaviours and related contextual factors such as affective states, location, and company. Qualitative insights were gathered from post-EMA feedback interviews. Compliance was low (median 49%), particularly for event-based surveys (median 34%), and declined over time. Many participants were unable or unwilling to complete surveys in certain contexts (e.g., when with family), faced interference with their daily schedules, and encountered occasional technical issues, suggesting the need for thorough initial training, an individualised protocol, and systematic compliance monitoring. The number of event-based surveys was less than desired for the study, with a median of 2.4/day for sedentary events, when 4 were targeted, and 0.9/day for walking events, when 3 were targeted. Conducting simulations using participants' Fitbit data allowed for optimising the triggering rules, achieving the desired median number of sedentary and walking surveys (3.9/day for both) in similar populations. Self-initiated reports of meals and drinks yielded more reports than those prompted in time-based and event-based EMA surveys, suggesting that self-initiated surveys might better reflect actual eating behaviours. This study highlights the importance of assessing feasibility and optimising EMA protocols to enhance subsequent compliance and data quality. Conducting pre-tests to refine protocols and procedures, including simulations using participants' activity data for optimal event-based triggering rules, is crucial for successful large-scale data collection in EMA studies of physical and eating behaviours.

Ecological Momentary Assessment (EMA) enables the real-time capture of health-related behaviours, their situational contexts, and associated subjective experiences. This study aimed to evaluate the feasibility of an EMA targeting physical and eating behaviours, optimise its protocol, and provide recommendations for future large-scale EMA data collections. The study involved 52 participants (age 31±9 years, 56% females) from Czechia, France, Germany, and Ireland completing a 9-day free-living EMA protocol using the HealthReact platform connected to a Fitbit tracker. The EMA protocol included time-based (7/day), event-based (up to 10/day), and self-initiated surveys, each containing 8 to 17 items assessing physical and eating behaviours and related contextual factors such as affective states, location, and company. Qualitative insights were gathered from post-EMA feedback interviews. Compliance was low (median 49%), particularly for event-based surveys (median 34%), and declined over time. Many participants were unable or unwilling to complete surveys in certain contexts (e.g., when with family), faced interference with their daily schedules, and encountered occasional technical issues, suggesting the need for thorough initial training, an individualised protocol, and systematic compliance monitoring. The number of event-based surveys was less than desired for the study, with a median of 2.4/day for sedentary events, when 4 were targeted, and 0.9/day for walking events, when 3 were targeted. Conducting simulations using participants’ Fitbit data allowed for optimising the triggering rules, achieving the desired median number of sedentary and walking surveys (3.9/day for both) in similar populations. Self-initiated reports of meals and drinks yielded more reports than those prompted in time-based and event-based EMA surveys, suggesting that self-initiated surveys might better reflect actual eating behaviours. This study highlights the importance of assessing feasibility and optimising EMA protocols to enhance subsequent compliance and data quality. Conducting pre-tests to refine protocols and procedures, including simulations using participants’ activity data for optimal event-based triggering rules, is crucial for successful large-scale data collection in EMA studies of physical and eating behaviours.

In an attempt to address the growing shortage of cybersecurity specialists in the country, the School of Electrical Engineering, University of Belgrade, started the course entitled Advanced Network and System Security (ANS) in the 2019/2020 school year. The ANS course covers the topics of computer system and network security, intrusion detection and prevention, and ethical hacking methodologies. This paper presents the course organization and associated laboratory environment and exercises and aims to prove that providing such a multidisciplinary laboratory leads to successful learning outcomes and directly improves gained knowledge in cybersecurity. The ANS course differs from all other related courses by covering various cybersecurity topics ranging from hardware through to network to web security. The analysis showed that 13 out of 19 Cyber Security Body of Knowledge classification Knowledge Areas are covered in the ANS course, unlike other related courses which cover up to 8 Knowledge Areas. Ultimately, the students’ practical skills improvement evaluation was performed through quantitative and qualitative analysis in order to prove that improving practical skills in the ANS laboratory resulted in the overall improvement of the gained knowledge.

We consider approval voting elections in which each voter votes for a (possibly empty) set of candidates and the outcome consists of a set of k candidates for some parameter k, e.g., committee elections. We are interested in the minimax approval voting rule in which the outcome represents a compromise among the voters, in the sense that the maximum distance between the preference of any voter and the outcome is as small as possible. This voting rule has two main drawbacks. First, computing an outcome that minimizes the maximum distance is computationally hard. Furthermore, any algorithm that always returns such an outcome provides incentives to voters to misreport their true preferences. In order to circumvent these drawbacks, we consider approximation algorithms, i.e., algorithms that produce an outcome that approximates the minimax distance for any given instance. Such algorithms can be considered as alternative voting rules. We present a polynomial-time 2-approximation algorithm that uses a natural linear programming relaxation for the underlying optimization problem and deterministically rounds the fractional solution in order to compute the outcome; this result improves upon the previously best known algorithm that has an approximation ratio of 3. We are furthermore interested in approximation algorithms that are resistant to manipulation by (coalitions of) voters, i.e., algorithms that do not motivate voters to misreport their true preferences in order to improve their distance from the outcome. We complement previous results in the literature with new upper and lower bounds on strategyproof and group-strategyproof algorithms.

Social media platforms make trade-offs in their design and policy decisions to attract users and stand out from other platforms. These decisions are influenced by a number of considerations, e.g. what kinds of content moderation to deploy or what kinds of resources a platform has access to. Their choices play into broader political tensions; social media platforms are situated within a social context that frames their impact, and they can have politics through their design that enforce power structures and serve existing authorities. We turn to Pillowfort, a small social media platform, to examine these political tensions as a case study. Using a discourse analysis, we examine public discussion posts between staff and users as they negotiate the site's development over a period of two years. Our findings illustrate the tensions in navigating the politics that users bring with them from previous platforms, the difficulty of building a site's unique identity and encouraging commitment, and examples of how design decisions can both foster and break trust with users. Drawing from these findings, we discuss how the success and failure of new social media platforms are impacted by political influences on design and policy decisions.