Voter Model Research Articles

Rainfall, population density and voter turnout

We create a dataset linking rainfall amounts to constituency-level election data for Irish general elections over the period 1989–2016. Rainfall is shown to significantly reduce voter turnout. The marginal effect of rainfall on turnout is greater in densely populated constituencies, where a rainy day decreases turnout by as much as three percentage points (or five percent). Using a theoretical framework based on a rational voting model, we propose two possible explanations for this effect. Firstly, if rural voters have higher civic duty than urban voters, they may be immune to rain on election day. Secondly, mode of transport may play a role. Urban voters are more likely to travel on foot or bicycle, whereas rural voters typically travel by car. Therefore, the cost to voting associated with rainfall may be higher in urban areas. Constituency-level data on mode of transport from 1997 to 2016 provides some empirical support for this hypothesis.

Adaptive voter model on simplicial complexes.

Collective decision making processes lie at the heart of many social, political, and economic challenges. The classical voter model is a well-established conceptual model to study such processes. In this work, we define a form of adaptive (or coevolutionary) voter model posed on a simplicial complex, i.e., on a certain class of hypernetworks or hypergraphs. We use the persuasion rule along edges of the classical voter model and the recently studied rewiring rule of edges towards like-minded nodes, and introduce a peer-pressure rule applied to three nodes connected via a 2-simplex. This simplicial adaptive voter model is studied via numerical simulation. We show that adding the effect of peer pressure to an adaptive voter model leaves its fragmentation transition, i.e., the transition upon varying the rewiring rate from a single majority state into a fragmented state of two different opinion subgraphs, intact. Yet, above and below the fragmentation transition, we observe that the peer pressure has substantial quantitative effects. It accelerates the transition to a single-opinion state below the transition and also speeds up the system dynamics towards fragmentation above the transition. Furthermore, we quantify that there is a multiscale hierarchy in the model leading to the depletion of 2-simplices, before the depletion of active edges. This leads to the conjecture that many other dynamic network models on simplicial complexes may show a similar behavior with respect to the sequential evolution of simplices of different dimensions.

Deadlock on the Board

AbstractWe develop a dynamic model of board decision-making akin to dynamic voting models in the political economy literature. We show a board could retain a policy all directors agree is worse than an available alternative. Thus, directors may retain a CEO they agree is bad—deadlocked boards lead to entrenched CEOs. We explore how to compose boards and appoint directors to mitigate deadlock. We find board diversity and long director tenure can exacerbate deadlock. We rationalize why CEOs and incumbent directors have power to appoint new directors: to avoid deadlock. Our model speaks to short-termism, staggered boards, and proxy access.

Legal aspects of formation and development of electoral systems in Ukraine

Introduction. The history of the Ukrainian Parliament functioning counts many electoral systems. The constant change of one kind or another was supposed to eliminate the shortcomings that were revealed during the preparation and carrying out the last elections. For a long time elections were holding under a mixed electoral system, but the combination of proportional and majoritarian components in it did not allow to provide the real expression of the citizens’ will and the effective parliament functioning. Therefore the legislator has been tasked with introducing a more optimal voting model. The result in this area was the adoption the Electoral Code of Ukraine in 2019. The purpose of the paper is to identify the main disadvantages of a mixed electoral system. to carry out the comparative analysis of the activity of deputies, which elected by different components of the mixed electoral system, to investigate the key points of the Electoral Code of Ukraine that will form the basis of legal regulation of the preparation and holding the elections to the Ukrainian Parliament and to compare them with the previous electoral system features. Results. Despite the combination of proportional and majoritarian electoral systems, theirinherent negative features remained relevant. We are talking about the lack of opportunity to select specific candidates from the electoral lists of parties, whose votes were not supported by another candidate in the single-member district etc. At the same time there was a pronounced preference for the proportional system over the majority one, because thanks to the popularity of the political party candidates could obtain a mandate without “special” effort. It is determined, that the points of the new Electoral Code of Ukraine provide the introduction of a new electoral system that will allow citizens to independently determine the priority of a candidate on the list during the distribution of mandates. At the same time the analysis of the Electoral Code shows that in some cases it will still be possible to keep "closed" lists. Conclusion. The Electoral Code of Ukraine, approved by the People's Deputies, is another testimony to the prudence of their political will and readiness to take into account the demands of society. During the analysis of the codified act it was found out that it will promote the political will of the citizens in the elections and ensure the efficiency of the deputies themselves.

Analysis of earlier times and flux of entropy on the majority voter model with diffusion

We study the properties of nonequilibrium systems modelled as spin models without defined Hamiltonian as the majority voter model. This model has transition probabilities that do not satisfy the condition of detailed balance. The lack of detailed balance leads to entropy production phenomena, which are a hallmark of the irreversibility. By considering that voters can diffuse on the lattice we analyze how the entropy production and how the critical properties are affected by this diffusion. We also explore two important aspects of the diffusion effects on the majority voter model by studying entropy production and entropy flux via time-dependent and steady-state simulations. This study is completed by calculating some critical exponents as function of the diffusion probability.

Study on clothing insulation distribution between half-bodies and its effects on thermal comfort in cold environments

Clothing is important for keeping the human body warm in cold environments. In Fanger's predicted mean vote (PMV) model, clothing insulation is a single input parameter that describes the dressing status of the whole body. The objective of this study was to examine the effects of the clothing insulation distribution between half-bodies. The distribution index (DI) was defined to describe the uneven level of the clothing insulation distribution. An experiment with 20 subjects and 9 different clothing conditions was conducted in a climate chamber. Subjects were asked to be sedentary and wear specified sets of garments in a 20 °C environment. The thermal insulation values of the garment sets were measured by using a thermal manikin. The nine clothing conditions were divided into four groups. Although their thermal insulations were very close, the DI values were quite different in each group. Subjective evaluations of thermal comfort were collected, and seven-point skin temperatures were measured. To investigate this topic with a wider temperature range and greater clothing diversity, a one-year continuous field study was conducted with 18 subjects. The results indicated that the subjective thermal evaluations were different when the clothing insulation levels were close and the DI values were different. Sets of garments with a lower DI were more likely to lead to lower thermal sensation vote (TSV) and more subjects preferring a warmer environment. Clothes with an uneven DI (<0.75 or >1.25) and less clothes on the lower body (DI < 1) made people more sensitive to cold environments.

Recall on Village Heads Election: An Election Law Reform

This study aims to analyze the model of holding village head elections between times with a case study in Reban Village, Reban District, Batang Regency. This study uses a qualitative method with a sociological juridical approach(Dawkins et al., 2019). The results of this study indicate that: 1). Inter-village head election procedures in Reban Village, Reban District, Batang Regency are in accordance with the mandate of Article 47 of Law Number 6 of 2014 concerning Villages. However, referring to Article 45 of Government Regulation No. 43 of 2014 there are stages of selection that are not yet in accordance with technical rules: a). The holding of the election exceeds the specified time limit. Limitations of technical guidelines become the main obstacle b). Funding for the election is still fully burdened to the candidates for village heads. 2). The mechanism for electing village heads over time through a voting model with a representative system. Voters who have the right to vote are every family head who lives in Reban Village, proven by a Family Card. Elections are democratic with a turnout of 94.9%.

Comparing machine learning algorithms in predicting thermal sensation using ASHRAE Comfort Database II

Predicting building occupants’ thermal comfort via machine learning (ML) is a hot research topic. Many algorithms and data processing methods have been applied to predict thermal comfort indices in different contexts. But few studies have systematically investigated how different algorithms and data processing methods can influence the prediction accuracy. In this study, we first summarized the recent literature from perspectives of predicted comfort indices, algorithms applied, input features, data sources, sample size, training proportion, predicting accuracy, etc. Then, we applied nine ML algorithms and three data sampling methods to predict the 3-point and 7-point thermal sensation vote (TSV) in ASHRAE Comfort Database II. The results show that with an accuracy of 66.3% and 61.1% for 3-point and 7-point TSV respectively, Random Forest (RF) has the best performance among the tested algorithms. Compared to the Predicted Mean Vote (PMV) model, ML TSV models generally have higher accuracy in TSV prediction. Based on feature importance analysis, the air temperature, humidity, clothing, air velocity, age, and metabolic rate are the top six important features for TSV prediction. The RF algorithm can achieve 63.6% overall accuracy in TSV prediction with the top three features, which is only 2.6% lower than involving 12 input features. Further, this paper addressed other common considerations in ML comfort model establishment such as tuning hyperparameters, splitting of training and testing data, and encoding methods. We also provided Python and R programming codes and packages as appendixes, which can be a good reference for future studies.

Learning, proximity and voting: theory and empirical evidence from nuclear referenda

This paper presents novel evidence on the pattern of voting in referenda and develops a spatial learning model that helps explain such behavior. In particular, we shed light on the determinants of voters’ choices over nuclear power using data on two Italian referenda. Exploiting the panel structure of the data, we document that voting against nuclear power increases, whenever the distance from the closest nuclear plant decreases. However, we detect a different voting behavior between municipalities close to existing reactors and those close to proposed ones. A possible explanation is that many citizens hold more precise information on nuclear safety because they have experienced the presence of a reactor in their vicinity for many years. Therefore, we propose a model of voting with endogenous information acquisition interacting both proximity and learning effects, whose results are compatible with the empirical findings. Citizens receive public and private signals and revise their beliefs on the risk of living close to a plant. Such revision process is nested into a spatial voting model establishing conditions for a similar or different voting behavior of the electorate based on the proximity from the reactor.

The Neglected Role and Variability of Party Intercepts in the Spatial Valence Approach

Empirical applications of the spatial theory of elections typically rely on the discrete choice framework to arrive at probabilistic voting models. Whereas in the classic model voter choice is solely a function of spatial proximity, neo-Downsian models also incorporate voter-specific nonpolicy attributes, which are represented by sociodemographic characteristics. One prominent line of such probabilistic models, Schofield’s Valence Model, additionally includes party valences into voter utility functions. The model rests on the estimated party intercepts to measure the valence advantages empirically. The party intercepts are ordered based on their values, and then this valence ranking is used further to predict equilibrium locations. The paper demonstrates that this measurement strategy does not provide unique results in fully specified models due to central properties of discrete choice models and the specific nature of party intercepts in these models. Drawing on a simple example based on mass election surveys from Germany, it is shown that the valence ranking, the crucial factor to investigate how valence differences affect the nature of spatial competition, is highly sensitive to arbitrary coding decisions. As a consequence, it is impossible to represent valence with the constants and to infer something substantial from the resulting valence ranking.

Invariant measures of interacting particle systems: Algebraic aspects

Consider a continuous time particle system ηt = (ηt(k), k ∈ 𝕃), indexed by a lattice 𝕃 which will be either ℤ, ℤ∕nℤ, a segment {1, ⋯ , n}, or ℤd, and taking its values in the set Eκ𝕃 where Eκ = {0, ⋯ , κ − 1} for some fixed κ ∈{∞, 2, 3, ⋯ }. Assume that the Markovian evolution of the particle system (PS) is driven by some translation invariant local dynamics with bounded range, encoded by a jump rate matrix ⊤. These are standard settings, satisfied by the TASEP, the voter models, the contact processes. The aim of this paper is to provide some sufficient and/or necessary conditions on the matrix ⊤ so that this Markov process admits some simple invariant distribution, as a product measure (if 𝕃 is any of the spaces mentioned above), the law of a Markov process indexed by ℤ or [1, n] ∩ ℤ (if 𝕃 = ℤ or {1, …, n}), or a Gibbs measure if 𝕃 = ℤ/nℤ. Multiple applications follow: efficient ways to find invariant Markov laws for a given jump rate matrix or to prove that none exists. The voter models and the contact processes are shown not to possess any Markov laws as invariant distribution (for any memory m). (As usual, a random process X indexed by ℤ or ℕ is said to be a Markov chain with memory m ∈ {0, 1, 2, ⋯ } if ℙ(Xk ∈ A | Xk−i, i ≥ 1) = ℙ(Xk ∈ A | Xk−i, 1 ≤ i ≤ m), for any k.) We also prove that some models close to these models do. We exhibit PS admitting hidden Markov chains as invariant distribution and design many PS on ℤ2, with jump rates indexed by 2 × 2 squares, admitting product invariant measures.

Leyes de escalamiento y criticalidad en modelos de votantes y dinámica neuronal

&lt;p class="p1"&gt;Una propiedad importante de muchos sistemas complejos es la presencia de leyes de escalamiento, las cuales se caracterizan por el hecho de que las variables que describen el comportamiento del sistema se relacionan a través de leyes de potencias. Las leyes de escalamiento generalmente tienen dos implicaciones importantes: (i) la auto–similitud del sistema en un gran rango de escalas (cada parte del sistema se parece al sistema completo), y, (ii) los “eventos raros” (es decir, eventos poco probables) no son tan raros y ocurren mucho más frecuentemente de lo que se esperaría. En este trabajo discutimos la criticalidad dinámica como uno de los principales mecanismos que generan leyes de escalamiento en sistemas complejos, enfocándonos principalmente en modelos de formación de opiniones y en redes neuronales. Veremos que dos sistemas aparentemente muy distintos (redes de votantes y redes neuronales) pueden describirse prácticamente con las mismas herramientas conceptuales y metodológicas, lo cual ilustra la universalidad de los fenómenos críticos y las correspondientes leyes de escalamiento.&lt;span class="Apple-converted-space"&gt; &lt;/span&gt;&lt;/p&gt;

Nonlinear q-voter model from the quenched perspective

We compare two versions of the nonlinear q-voter model: the original one, with annealed randomness, and the modified one, with quenched randomness. In the original model, each voter changes its opinion with a certain probability ϵ if the group of influence is not unanimous. In contrast, the modified version introduces two types of voters that act in a deterministic way in the case of disagreement in the influence group: the fraction ϵ of voters always change their current opinion, whereas the rest of them always maintain it. Although both concepts of randomness lead to the same average number of opinion changes in the system on the microscopic level, they cause qualitatively distinct results on the macroscopic level. We focus on the mean-field description of these models. Our approach relies on the stability analysis by the linearization technique developed within dynamical system theory. This approach allows us to derive complete, exact phase diagrams for both models. The results obtained in this paper indicate that quenched randomness promotes continuous phase transitions to a greater extent, whereas annealed randomness favors discontinuous ones. The quenched model also creates combinations of continuous and discontinuous phase transitions unobserved in the annealed model, in which the up–down symmetry may be spontaneously broken inside or outside the hysteresis loop. The analytical results are confirmed by Monte Carlo simulations carried out on a complete graph.

An Analysis of Parallel Ensemble Diabetes Decision support system based on Voting Classifier for Classification Problem

Diabetes mellitus is one of the prominent health challenges in the world. Diabetes is a dangerous, metabolic disease that caused by human blood sugar level and progresses throughout life. In supervised learning-based systems have been proposed that incorporate ensemble learning techniques for diabetes prediction depends upon the diagnostic measurement of the diabetes patient. In this paper, voting classifier were used for combining the various ensemble and base classifiers for designing diabetes disease prediction. Voting mechanism helps to build the multiple ensemble and base classifier model. The accuracy of ensemble of ensemble classifiers has resulted in high rate of accuracy (79%) when compared to the ensemble of base classifiers (77%) with majority rule voting (MRV) and weighted majority voting (WMV) models. Hence, ensemble of ensemble classifier was chosen as the best model for diabetes healthcare prediction. This system has been experimented with Pima Indian diabetes UCI dataset and its implemented in python language.

Local Symmetry and Global Structure in Adaptive Voter Models

Adaptive voter models (AVMs) are simple mechanistic systems that model the emergence of mesoscopic structure from local networked processes driven by conflict and homophily. AVMs display rich behavior, including a phase transition from a fully-fragmented regime of "echo-chambers" to a regime of persistent disagreement governed by low-dimensional quasistable manifolds. Many extant methods for approximating the behavior of AVMs are either restricted in scope, expensive in computation, or inaccurate in predicting important statistics. In this work, we develop a novel, second-order moment closure approximation method for binary-state rewire-to-random and rewire-to-same model variants. We incorporate a small amount of noise via a random mutation term, which renders the system ergodic. Using ergodicity, we then approximate the voting process, which is non-Markovian in the second moments of the system, with a Markovian term near the phase transition. This approximation exploits an asymmetry between different classes of voting events. The resulting scheme enables us to predict the location of the phase transition and the active edge density in the regime of persistent disagreement, across the entire space of parameters and opinion densities. Numerically, our results are nearly exact for the rewire-to-random model, and competitive with other current approaches for the rewire-to-same model. Moreover, our computations display constant scaling in the mean degree, enabling approximations for denser systems than previously possible. We conclude with suggestions for model refinements and extensions.

Identification of complex network topologies through delayed mutual information

The definitions of delayed mutual information and multi-information are recalled. It is shown how the delayed mutual information may be used to reconstruct the interaction topology resulting from some unknown scale-free graph with its associated local dynamics. Delayed mutual information is also used to solve the community detection problem. A probabilistic voter model defined on a scale-free graph is used throughout the paper as an illustrative example.

Polarization and consensus by opposing external sources

We introduce a socially motivated extension of the voter model in which individual voters are also influenced by two opposing, fixed-opinion news sources. These sources forestall consensus and instead drive the population to a politically polarized state, with roughly half the population in each opinion state. Two types of social networks for the voters are studied: (a) the complete graph of N voters and, more realistically, (b) the two-clique graph with N voters in each clique. For the complete graph, many dynamical properties are soluble within an annealed-link approximation, in which a link between a news source and a voter is replaced by an average link density. In this approximation, we show that the average consensus time grows as , with . Here p is the probability that a voter consults a news source rather than a neighboring voter, and is the link density between a news source and voters, so that can be greater than 1. The polarization time, namely, the time to reach a politically polarized state from an initial strong majority state, is typically much less than the consensus time. For voters on the two-clique graph, either reducing the density of interclique links or enhancing the influence of news sources again promotes polarization.

Information Disclosure in Elections with Sequential Costly Participation

Electoral legislation varies across countries and within countries over time, and across different types of elections in terms of how it allows publication of intermediate election results including turnout and candidates’ vote shares during an election day. Using a pivotal costly voting model of elections in which voters have privately observed preferences between two candidates and act sequentially, I study how different rules for disclosing information about the actions of early voters affect the actions of later voters, and how they ultimately impact voter and candidate welfare. Comparing three rules observed in real life elections (no disclosure, turnout disclosure and vote count disclosure), I find that vote count disclosure dominates the other two rules in terms of voter welfare. I further show that each of the rules can provide a candidate with either the greatest or the least chance to win, depending on the candidate’s ex-ante support.

Identification of COPD From Multi-View Snapshots of 3D Lung Airway Tree via Deep CNN

Chronic obstructive pulmonary disease (COPD) is associated with morphologic abnormalities of airways with various patterns and severities. However, the way of effectively representing these abnormalities is lacking and whether these abnormalities enable to distinguish COPD from healthy controls is unknown. We propose to use deep convolutional neural network (CNN) to assess 3D lung airway tree from the perspective of computer vision, thereby constructing models of identifying COPD. After extracting airway trees from CT images, snapshots of their 3D visualizations are obtained from ventral, dorsal and isometric views. Using snapshots of each view, one deep CNN model is constructed and further optimized by Bayesian optimization algorithm to indentify COPD. The majority voting of three views presents the final prediction. Finally, the class-discriminative localization maps have been drawn to visually explain the CNNs’ decisions. The models trained with single view (ventral, dorsal and isometric) of colorful snapshots present the similar accuracy (ACC) (86.8%, 87.5% and 86.7%) and the model after voting achieves the ACC of 88.2%. The ACC of the final voting model using gray and binary snapshots achieves 88.6% and 86.4%, respectively. Our specially designed CNNs outperform the typical off-the-shelf CNNs and the pre-trained CNNs with fine tuning. The class-discriminative regions of COPD are mainly located at central airways; however, regions in HC are scattering and located at peripheral airways. It is feasible to identify COPD using snapshots of 3D lung airway tree extracted from CT images via deep CNN. The CNNs can represent the abnormalities of airway tree in COPD and make accurate CT-based diagnosis of COPD.

Should You Want An Educated Mayor? Evidence from Close Elections in Italy

This paper studies the impact of politicians' education on policy choices and public finance. A probabilistic voting model with candidates' education level and public expenditure decomposition is used to explain how politician's education matters for policy decisions over public budget allocation. Empirically, I use Italian municipality data on electoral results, balance sheets and mayor candidates' educational attainment from 2000 to 2015. To estimate the causal effect of mayors' education on public finance I rely on regression discontinuity design focusing on close elections. Overall, I find that educated mayors boost public investment, especially in the education sector, without compromising the fiscal stability of the municipalities.