Voter Model Research Articles

Approval-Based Voting with Mixed Goods

We consider a voting scenario in which the resource to be voted upon may consist of both indivisible and divisible goods. This generalizes both the well-studied model of multiwinner voting and the recently introduced model of cake sharing. Under approval votes, we propose two variants of the extended justified representation (EJR) notion from multiwinner voting, a stronger one called EJR for mixed goods (EJR-M) and a weaker one called EJR up to 1 (EJR-1). We extend three multiwinner voting rules to our setting—GreedyEJR, the method of equal shares (MES), and proportional approval voting (PAV)—and show that while all three generalizations satisfy EJR-1, only the first one provides EJR-M. In addition, we derive tight bounds on the proportionality degree implied by EJR-M and EJR-1, and investigate the proportionality degree of our proposed rules.

Maximizing the Probability of Fixation in the Positional Voter Model

Maximizing the Probability of Fixation in the Positional Voter Model

Come‐from‐behind victories under ranked‐choice voting and runoff: The impact on voter satisfaction

AbstractBoth ranked‐choice voting (RCV) and runoff seek to prevent the election of candidates with only minority support by enabling more broadly approved rivals to win through come‐from‐behind victories (CFBVs). Although CFBVs are intrinsic to RCV and runoff, they have received little scholarly attention. This study suggests that, amid voters' status quo bias, CFBVs provoke dissatisfaction. In a survey experiment fielded on U.S. voters, CFBVs under RCV significantly reduced satisfaction, while there was a weaker negative effect under runoff. Similarly, RCV was repealed or faced a visible repeal attempt in the vast majority of U.S. jurisdictions that experienced a CFBV in the first or second use of the rule. This was not the case for runoff. We encourage greater voter education, including regarding the rationale for and mechanics of CFBVs under RCV as well as consideration of runoff and other rules that encourage the election of candidates with majority support.Related ArticlesMcCarthy, Devin, and Jack Santucci. 2021. “Ranked Choice Voting as a Generational Issue in Modern American Politics.” Politics & Policy 49(1): 33–60. https://doi.org/10.1111/polp.12390.Nielson, Lindsay. 2017. “Ranked Choice Voting and Attitudes toward Democracy in the United States: Results from a Survey Experiment.” Politics & Policy 45(4): 535–70. https://doi.org/10.1111/polp.12212.Stiers, Dieter. 2019. “Static and Dynamic Models of Retrospective Voting: A Clarification and Application to the Individual Level.” Politics & Policy 47(5): 859–76. https://doi.org/10.1111/polp.12324.

Estonian experience of electronic voting: prospects of implementation in Ukraine

The paper examines the Estonian experience of electronic voting and reveals the prospects for the introduction of the Estonian model of Internet voting in Ukraine. The author investigated the procedure for conducting electronic voting in Estonia, the legal, organizational and technical aspects of its implementation, as well as the positive features of the implementation of electronic voting. On the basis of the conducted scientific research, the author came to the conclusion that when implementing electronic voting, it would be appropriate to use the Estonian experience, including regarding voting within a certain time before the voting date at the polling station, the possibility of changing the vote before the end of the time allocated for voting, priority of voting at the polling station over internet voting in case the voter voted using both forms of voting. It is also necessary to develop appropriate software that would use the performance of the i-Voting system, possibly based on the Diya mobile application and the Diya internet portal. For this purpose, it will be expedient to make appropriate changes to the Election Code of Ukraine, taking into account the provisions of Art. 116 of the Law of Ukraine "On the All-Ukrainian Referendum". The measures listed above will contribute to the implementation of e-democracy in Ukraine, especially in the context of the impossibility of creating polling stations in many regions of Ukraine, which are currently occupied or war zones, given the significant destruction of infrastructure, the temporary relocation of the majority of the population of the respective regions to other regions. The implementation of the electronic voting system would solve some of these problems that may arise during the next parliamentary and presidential elections in Ukraine.

Prediction of posttraumatic functional recovery in middle-aged and older patients through dynamic ensemble selection modeling.

Age-specific risk factors may delay posttraumatic functional recovery; complex interactions exist between these factors. In this study, we investigated the prediction ability of machine learning models for posttraumatic (6 months) functional recovery in middle-aged and older patients on the basis of their preexisting health conditions. Data obtained from injured patients aged ≥45 years were divided into training-validation (n = 368) and test (n = 159) data sets. The input features were the sociodemographic characteristics and baseline health conditions of the patients. The output feature was functional status 6 months after injury; this was assessed using the Barthel Index (BI). On the basis of their BI scores, the patients were categorized into functionally independent (BI >60) and functionally dependent (BI ≤60) groups. The permutation feature importance method was used for feature selection. Six algorithms were validated through cross-validation with hyperparameter optimization. The algorithms exhibiting satisfactory performance were subjected to bagging to construct stacking, voting, and dynamic ensemble selection models. The best model was evaluated on the test data set. Partial dependence (PD) and individual conditional expectation (ICE) plots were created. In total, nineteen of twenty-seven features were selected. Logistic regression, linear discrimination analysis, and Gaussian Naive Bayes algorithms exhibited satisfactory performances and were, therefore, used to construct ensemble models. The k-Nearest Oracle Elimination model outperformed the other models when evaluated on the training-validation data set (sensitivity: 0.732, 95% CI: 0.702-0.761; specificity: 0.813, 95% CI: 0.805-0.822); it exhibited compatible performance on the test data set (sensitivity: 0.779, 95% CI: 0.559-0.950; specificity: 0.859, 95% CI: 0.799-0.912). The PD and ICE plots showed consistent patterns with practical tendencies. Preexisting health conditions can predict long-term functional outcomes in injured middle-aged and older patients, thus predicting prognosis and facilitating clinical decision-making.

Individual bias and fluctuations in collective decision making: from algorithms to Hamiltonians

In this paper, we reconsider the spin model suggested recently to understand some features of collective decision making among higher organisms (Hartnett et al 2016 Phys. Rev. Lett. 116 038701). Within the model, the state of an agent i is described by the pair of variables corresponding to its opinion and a bias ω i toward any of the opposing values of S i . Collective decision making is interpreted as an approach to the equilibrium state within the nonlinear voter model subject to a social pressure and a probabilistic algorithm. Here, we push such a physical analogy further and give the statistical physics interpretation of the model, describing it in terms of the Hamiltonian of interaction and looking for the equilibrium state via explicit calculation of its partition function. We show that, depending on the assumptions about the nature of social interactions, two different Hamiltonians can be formulated, which can be solved using different methods. In such an interpretation the temperature serves as a measure of fluctuations, not considered before in the original model. We find exact solutions for the thermodynamics of the model on the complete graph. The general analytical predictions are confirmed using individual-based simulations. The simulations also allow us to study the impact of system size and initial conditions on the collective decision making in finite-sized systems, in particular, with respect to convergence to metastable states.

Individual Factors that Affect the Perceived Thermal Comfort Condition during Heating Seasons

This paper investigated the effect of individual differences and the type of heating system on the perceived thermal comfort condition, neutral temperature, and thermal sensitivity in cold outdoor air conditions. Three types of individual factors, such as gender, temperament, and thermal past experience, were studied and compared in this paper. According to the results, temperament and thermal past experience directly influenced the occupants’ thermal preference and indirectly influenced the thermal sensation by affecting their clothing level. However, the effect size of such variations was negligible. The type of heating system had the most significant impact on the neutral temperature, with a mean difference of 2.8°C. Individual factors did not have a direct impact on the neutral temperature. However, thermal sensitivity was influenced by an individual’s temperament. Occupants with a cold temperament were more sensitive to the variation in the running mean outdoor temperature (Trm) than those with a warm temperament. When the results were compared with the predictions by the adaptive thermal heat balance (ATHB) and the predicted mean vote (PMV) models, it indicated that if the control condition over the heating system became limited, there would be no (i.e., very low) behavioral, physiological, and psychological adaptation. However, by creating an opportunity to control the heating system, thermal adaptation could be reached at a lower temperature under cold outdoor air conditions.

An ensemble machine learning based bank loan approval predictions system with a smart application

Banks rely heavily on loans as a primary source of revenue; however, distinguishing deserving applicants who will reliably repay loans presents an ongoing challenge. Conventional selection processes often struggle to identify the most suitable candidates from a pool of loan applicants. In response to this challenge, we present an innovative machine learning (ML) based loan prediction system designed to identify qualified loan applicants autonomously. This comprehensive study encompasses data preprocessing, effective data balancing using SMOTE, and the implementation of diverse ML models, including Logistic Regression, Decision Tree, Random Forest, Extra Trees, Support Vector Machine, K-Nearest Neighbors, Gaussian Naive Bayes, AdaBoost, Gradient Boosting, and advanced deep learning models such as deep neural networks, recurrent neural networks, and long short-term memory models. The model's performance is rigorously assessed in terms of accuracy, recall, and F1_score. Our experimental analysis reveals that the Extra Trees outperforms its counterparts. Furthermore, we successfully predict bank loan defaulters through an ensemble voting model, which includes the top three ML models, achieving a remarkable 0.62% increase in accuracy compared to the Extra Trees. To facilitate user interaction, we have developed a user-friendly desktop-based application. Notably, our findings demonstrate that the voting-based ensemble model surpasses both individual ML models, including Extra Trees, and existing state-of-the-art approaches, achieving an impressive accuracy of 87.26%. This innovative system has the potential to significantly streamline and enhance the efficiency of bank loan approval processes, ultimately benefiting both financial institutions and loan applicants alike.

A comparison of zero- and minimal-intelligence agendas in majority-rule voting models

Emergent behavior in repeated collective decisions of minimally intelligent agents—who at each step in time invoke majority rule to choose between a status quo and a random challenge—can manifest through the long-term stationary probability distributions of a Markov chain. We use this known technique to compare two kinds of voting agendas: a zero-intelligence agenda that chooses the challenger uniformly at random and a minimally intelligent agenda that chooses the challenger from the union of the status quo and the set of winning challengers. We use Google Co-Lab’s GPU accelerated computing environment to compute stationary distributions for some simple examples from spatial-voting and budget-allocation scenarios. We find that the voting model using the zero-intelligence agenda converges more slowly, but in some cases to better outcomes.

Analysis of a continuous-time adaptive voter model.

In this paper, we study a variant of the voter model on adaptive networks in which nodes can flip their spin, create new connections, or break existing connections. We first perform an analysis based on the mean-field approximation to compute asymptotic values for macroscopic estimates of the system, namely, the total mass of present edges in the system and the average spin. However, numerical results show that this approximation is not very suitable for such a system, for which it does not capture key features such as the network breaking into two disjoint and opposing (in spin) communities. Therefore, we propose another approximation based on an alternate coordinate system to improve accuracy and validate this model through simulations. Finally, we state a conjecture dealing with the qualitative properties of the system, corroborated by numerous numerical simulations.

Voter-like Dynamics with Conflicting Preferences on Modular Networks.

Two of the main factors shaping an individual's opinion are social coordination and personal preferences, or personal biases. To understand the role of those and that of the topology of the network of interactions, we study an extension of the voter model proposed by Masuda and Redner (2011), where the agents are divided into two populations with opposite preferences. We consider a modular graph with two communities that reflect the bias assignment, modeling the phenomenon of epistemic bubbles. We analyze the models by approximate analytical methods and by simulations. Depending on the network and the biases' strengths, the system can either reach a consensus or a polarized state, in which the two populations stabilize to different average opinions. The modular structure generally has the effect of increasing both the degree of polarization and its range in the space of parameters. When the difference in the bias strengths between the populations is large, the success of the very committed group in imposing its preferred opinion onto the other one depends largely on the level of segregation of the latter population, while the dependency on the topological structure of the former is negligible. We compare the simple mean-field approach with the pair approximation and test the goodness of the mean-field predictions on a real network.

Enhancing Cardiovascular Disease Prediction Using Hard Voting Technique in Machine Learning

Cardio Vascular Disease (CVD) or Heart disease is one of the leading causes of death around the globe. Early identification of the disease can significantly save precious lives. But the identification of heart-related diseases is a challenging task as it relies on a wide range of factors. Machine Learning algorithms have strong potential in prediction-related domains. In this paper, we have used an Ensembled model called the Hard Vot-ing Ensemble Model to detect heart disease. A dataset containing 13 features is taken from the UCI repo using Kaggle. Seven different algorithms are used, tested, and trained, accuracy is measured and out of those, models with the best accuracy are picked and ensembled together. The ensemble model resulted in higher accuracy than all other individual models.

Mitigation of adversarial attacks on voter model dynamics by network heterogeneity

Voter model dynamics in complex networks are vulnerable to adversarial attacks. In particular, the voting outcome can be inverted by adding extremely small perturbations that are strategically generated in social networks, even when one opinion is dominant over the other. However, the mitigation of adversarial attacks on the voter model dynamics in complex networks has not been thoroughly investigated. Thus, we examined network structures that could mitigate adversarial attacks using model networks and real-world networks, considering that the network structure affects the voter model dynamics. Numerical simulations demonstrated that the heterogeneity of node degrees in the networks (degree heterogeneity) significantly mitigates adversarial attacks. In particular, for complex networks with a power-law degree distribution , the mitigation effect is significant for . However, the mitigation effect of the degree heterogeneity was relatively weak for large and dense networks. The degree correlation and clustering in the networks exhibited almost no mitigation effect. The results enhance our understanding of how opinion dynamics and collective decision-making are distorted in social networks and may be useful for considering defense strategies against adversarial attacks.

Solar Irradiation Forecasting Using Ensemble Voting Based on Machine Learning Algorithms

This paper proposes an ensemble voting model for solar radiation forecasting based on machine learning algorithms. Several ensemble models are assessed using a simple average and a weighted average, combining the following algorithms: random forest, extreme gradient boosting, categorical boosting, and adaptive boosting. A clustering algorithm is used to group data according to the weather, and feature selection is applied to choose the most-related inputs and their past observation values. Prediction performance is evaluated by several metrics using a real-world Brazilian database, considering different prediction time horizons of up to 12 h ahead. Numerical results show the weighted average voting approach based on random forest and categorical boosting has superior performance, with an average reduction of 6% for MAE, 3% for RMSE, 16% for MAPE, and 1% for R2 when predicting one hour in advance, outperforming individual machine learning algorithms and other ensemble models.

Polarization and Consensus in a Voter Model under Time-Fluctuating Influences

We study the effect of time-fluctuating social influences on the formation of polarization and consensus in a three-party community consisting of two types of voters (“leftists” and “rightists”) holding extreme opinions, and moderate agents acting as “centrists”. The former are incompatible and do not interact, while centrists hold an intermediate opinion and can interact with extreme voters. When a centrist and a leftist/rightist interact, they can become either both centrists or both leftists/rightists. The population eventually either reaches consensus with one of the three opinions, or a polarization state consisting of a frozen mixture of leftists and rightists. As a main novelty, here agents interact subject to time-fluctuating external influences favouring in turn the spread of leftist and rightist opinions, or the rise of centrism. The fate of the population is determined under various scenarios, and it is shown how the rate of change of external influences can drastically affect the polarization and consensus probabilities, as well as the mean time to reach the final state.

CT-based radiomics can identify physiological modifications of bone structure related to subjects' age and sex.

Radiomics of vertebral bone structure is a promising technique for identification of osteoporosis. We aimed at assessing the accuracy of machine learning in identifying physiological changes related to subjects' sex and age through analysis of radiomics features from CT images of lumbar vertebrae, and define its generalizability across different scanners. We annotated spherical volumes-of-interest (VOIs) in the center of the vertebral body for each lumbar vertebra in 233 subjects who had undergone lumbar CT for back pain on 3 different scanners, and we evaluated radiomics features from each VOI. Subjects with history of bone metabolism disorders, cancer, and vertebral fractures were excluded. We performed machine learning classification and regression models to identify subjects' sex and age respectively, and we computed a voting model which combined predictions. The model was trained on 173 subjects and tested on an internal validation dataset of 60. Radiomics was able to identify subjects' sex within single CT scanner (ROC AUC: up to 0.9714), with lower performance on the combined dataset of the 3 scanners (ROC AUC: 0.5545). Higher consistency among different scanners was found in identification of subjects' age (R2 0.568 on all scanners, MAD 7.232years), with highest results on a single CT scanner (R2 0.667, MAD 3.296years). Radiomics features are able to extract biometric data from lumbar trabecular bone, and determine bone modifications related to subjects' sex and age with great accuracy. However, acquisition from different CT scanners reduces the accuracy of the analysis.

Opinion formation models with extreme switches and disorder: Critical behavior and dynamics.

In a three-state kinetic exchange opinion formation model, the effect of extreme switches was considered in a recent paper. In the present work, we study the same model with disorder. Here disorder implies that negative interactions may occur with a probability p. In the absence of extreme switches, the known critical point is at p_{c}=1/4 in the mean-field model. With a nonzero value of q that denotes the probability of such switches, the critical point is found to occur at p=1-q/4 where the order parameter vanishes with a universal value of the exponent β=1/2. Stability analysis of initially ordered states near the phase boundary reveals the exponential growth (decay) of the order parameter in the ordered (disordered) phase with a timescale diverging with exponent 1. The fully ordered state also relaxes exponentially to its equilibrium value with a similar behavior of the associated timescale. Exactly at the critical points, the order parameter shows a power-law decay with time with exponent 1/2. Although the critical behavior remains mean-field-like, the system behaves more like a two-state model as q→1. At q=1 the model behaves like a binary voter model with random flipping occurring with probability p.

Adaptive thermal sensation evaluation model in tents for Western Sichuan Plateau of China: A field study

The Western Sichuan Plateau (WSP), which is located in the eastern margin of the Qinghai–Tibet Plateau, exhibits a unique climate featuring high altitude, thin air, low air temperature, and strong solar radiation. The local residents have developed a unique thermal sensation as a result of climatic adaptation, which cannot be accurately described using existing thermal sensation evaluation models. Therefore, we conducted a field study of the thermal sensation of the herdsmen living in tents in two typical areas located in the WSP. We measured the thermal environment parameters (air temperature, relative humidity, air speed, and black-globe temperature) on site and simultaneously conducted a questionnaire regarding the activity level, clothing insulation, and thermal sensation of the residents. A statistical regression analysis was performed, and the results showed a difference of 5.7 °C and 3.2 °C between the actual and predicted thermal neutral temperatures of the residents in the WSP during winter and summer, respectively. To improve the prediction accuracy, an adaptive predicted mean vote (aPMV) model incorporated with an adaptive coefficient was established for the WSP. The aPMV model can accurately predicted the thermal sensation levels of the local herdsmen living in tents.

Stability and Regime Change: The Evolution of Accounting Standards

ABSTRACT We examine the evolution of accounting regulation by linking disclosure policies and investments in a dynamic voting model. The disclosure policies are the outcome of voting by entrepreneurs, whose preferences are influenced by their investments. The investments are in turn endogenously determined by current and future disclosure policies. Absent external influences, accounting regimes are stable. A disclosure regime of high (low) quality and a strong (weak) economy coexist and reinforce each other. However, regulatory interventions can result in regime changes by changing the entrepreneurs’ expectations, even without direct enforcement. Unexpected shocks could also result in regime changes by impacting economic conditions and hence voter composition. Our analysis provides a framework to study the interaction between accounting regulation and firms’ economic decisions.

The Model of Voting in Cicero’s Best State

Abstract In the proposed law-code in De legibus there is a law that votes are to be known by the best citizens (the optimates) but free to the common people (the plebs) (3.10). This law, Cicero claims, grants ‘the appearance of liberty’ (libertatis species), preserves the authority (auctoritas) of the optimates, and promotes harmony between the classes (3.39). The law and the precise meaning of libertatis species remain opaque even with the lengthy commentary (3.33–39), and much scholarly debate and discussion has arisen as a result – most of it very critical of Cicero’s proposal and the arguments supporting it. This paper offers a fresh analysis of the voting law that is more charitable to Cicero. It unpacks the full details of the voting system that is developed in De legibus and sheds new light on developments in Cicero’s thinking about the best state.