The Trust in Voting Systems (TVS) Measure

It is essential to democracy that voters trust voting systems enough to participate in elections and use these systems. Unfortunately, voter trust has been found to be low in many situations, which could detrimentally impact human-computer interactions in voting. Therefore, it is important to understand the degree to which voters trust any specific voting method. Voting researchers have developed and used measures of overall trust in technology; yet researchers have long argued that trust in systems is domain-specific, implying that system-specific measures should be used instead. To address this latter point, this paper describes the development of a psychometrically reliable and validated instrument called the Trust in Voting Systems (TVS) measure. The TVS not only allows researchers to understand group mean differences in trust across voting systems; it also allows researchers to understand individual differences in trust within systems—all of which collectively serves to inform and improve voting systems.

The Impact of the D21 Method and Its Modification on Citizens’ Participation in Participatory Budgeting. The Case of the Czech Republic

Participatory budgeting (PB) is often discussed as a tool to support active participation of citizens in the decision-making in the matters of the distribution of public resources. However, little was said about the possibility that the choice of a voting method used in the voting phase of the PB process could affect the participation in PB. In the Czech Republic, the Democracy 2.1 (D21) voting method is often used in municipalities implementing PB and additionally, it is promoted as a method to encourage more people to vote. This article aims to determine Czech municipalities with the D21 method and its modification, and to evaluate the impact of choosing these voting methods on participation rate in PB. The study suggests that the choice of a voting method is relevant for citizens´ engagement in voting. Based on the empirical findings, the D21 method and its modification are associated with a higher voter turnout in PB than another voting method. Further, the results underline the influence of external organizations offering online platforms for voting.

Methods and Software Complex Development for Remote Electronic Voting Based on Ethereum Blockchain Platform

The most promising way to increase voters’ confidence in the remote electronic voting (REV) procedure is a voting method based on Ethereum blockchain platform. However, the existing solutions using this method faced a range of problems: ensuring the secrecy of the vote and openness of the procedure for society, pressure on the voter and a guarantee of the reliability of the whole system. In this work, a method for constructing a REV is proposed that solves these problems. It is similar in structure to the traditional voting method, using the same principle and processes. The Ethereum blockchain based REV process is described in detail. It was shown that received votes are securely stored in the Ethereum blockchain network, and the correctness of the vote addressing to the selected candidate can always be checked in real time. The description of smart contract algorithm that implements the transfer of vote from voter to candidate using transactions and determines the winner who received the highest number of votes was provided. It was demonstrated that keccak256 hashing algorithm and secp256k1 elliptic curve signatures ensure transactions’ maximum protection, reliability, and non-rollability. The developed REV technique based on Ethereum blockchain platform increases the efficiency of data security and confidentiality, transparency and anonymity of the voting procedure, and solves the problem of coercion. The results of the work have been implemented programmatically and can be used not only in the electoral system, but also wherever there is need of remote voting.

Do people want smarter ballots?

We ascertain whether citizens want to have smart ballots, that is, whether they appreciate having the possibility to express some support for more than one option (expression across options) and to indicate different levels of support for these options (expression within options). We conducted two independent yet complementary survey experiments at the time of the Super Tuesday Democratic primaries to examine which voting method citizens prefer, one with the real candidates in the states holding Democratic primaries and one with fictitious candidates in the whole country. In both surveys, respondents were asked to vote using four different voting rules: single, approval, rank, and point (score). After they cast their vote, respondents were asked how satisfied they were using each voting method. The findings are consistent in both studies: the single vote is the most preferred voting method. We show that this is a reflection of a status quo bias, as citizens’ views are strongly correlated with age.

A hybrid model with novel feature selection method and enhanced voting method for credit scoring

With the in-depth application of artificial intelligence technology in the financial field, credit scoring models constructed by machine learning algorithms have become mainstream. However, the high-dimensional and complex attribute features of the borrower pose challenges to the predictive competence of the model. This paper proposes a hybrid model with a novel feature selection method and an enhanced voting method for credit scoring. First, a novel feature selection combined method based on a genetic algorithm (FSCM-GA) is proposed, in which different classifiers are used to select features in combination with a genetic algorithm and combine them to generate an optimal feature subset. Furthermore, an enhanced voting method (EVM) is proposed to integrate classifiers, with the aim of improving the classification results in which the prediction probability values are close to the threshold. Finally, the predictive competence of the proposed model was validated on three public datasets and five evaluation metrics (accuracy, AUC, F-score, Log loss and Brier score). The comparative experiment and significance test results confirmed the good performance and robustness of the proposed model.