Biased Voter Research Articles

Partisanship in a Pandemic: Biased Voter Assessments of Past and Present Government Performance

AbstractAccountability relies on voters accurately evaluating government performance in addressing the important issues of the day. This requirement arguably applies to an even greater extent when addressing fundamental societal crises. However, partisanship can bias evaluations, with government partisans perceiving outcomes more favorably, or attributing less responsibility for bad outcomes. We examine partisan motivated reasoning in the context of the Covid-19 pandemic crisis, using panel data and a survey experiment of over 6000 respondents in which vignettes prime respondents about the UK government’s successes and failures in tackling the pandemic. We also propose a novel extension of the partisan bias thesis: partisans arrive at biased judgements of government competence by recalling the past performance of the government differently, according to whether or not their favored party held power at that time. We find that even in the relatively consensual partisan context of the UK’s response to Covid-19, where both major parties endorsed both lockdown and vaccination programs, there is evidence of both current and recall partisan biases: Opposition partisans are more likely to blame the government for negative outcomes and less likely to recall positive aspects of the government’s recent and past performance unless prompted to do so. Our findings have implications for understanding the limits of democratic accountability under crisis conditions.

Racial Bias and U.S. Presidential Candidate Preference

Abstract Much research has suggested that Republican candidates in U.S. presidential elections benefit from voter bias against non-White groups. The present study supplements this research by including in the analysis bias against Whites. Estimates from the American National Election Studies 2020 Time Series Study indicated that a nontrivial percentage of the U.S. population has a bias that disfavors Whites, with this bias more prevalent among Democrats than Republicans. Further analyses estimated the extent to which the type of voter racial bias that favors Republican presidential candidates offsets the type of voter racial bias that favors Democratic presidential candidates. Estimates for the 2020 U.S. presidential election indicated that the two-party vote share for Donald Trump was higher among voters who rated Whites, Blacks, Hispanics, and Asians/Asian-Americans equal to each other on 0-to-100 feeling thermometers than among the full population of two-party voters, suggesting that Donald Trump was disadvantaged on net by the electorate including racially biased voters. These results call into question research that has used an unrepresentative set of racial attitudes to suggest that, in recent U.S. presidential elections, only Republican candidates have benefitted from racial bias among the electorate.

Spread of premalignant mutant clones and cancer initiation in multilayered tissue

Over 80% of human cancers originate from the epithelium, which covers the outer and inner surfaces of organs and blood vessels. In stratified epithelium, the bottom layers are occupied by stem and stem-like cells that continually divide and replenish the upper layers. In this work, we study the spread of premalignant mutant clones and cancer initiation in stratified epithelium using the biased voter model on stacked two-dimensional lattices. Our main result is an estimate of the propagation speed of a premalignant mutant clone, which is asymptotically precise in the cancer-relevant weak-selection limit. We use our main result to study cancer initiation under a two-step mutational model of cancer, which includes computing the distributions of the time of cancer initiation and the size of the premalignant clone giving rise to cancer. Our work quantifies the effect of epithelial tissue thickness on the process of carcinogenesis, thereby contributing to an emerging understanding of the spatial evolutionary dynamics of cancer.

Are Passive Investors Biased Voters?

Are Passive Investors Biased Voters?

Coalescing and branching simple symmetric exclusion process

Motivated by kinetically constrained interacting particle systems (KCM), we consider a reversible coalescing and branching simple exclusion process on a general finite graph G=(V,E) dual to the biased voter model on G. Our main goal is tight bounds on its logarithmic Sobolev constant and relaxation time, with particular focus on the delicate slightly supercritical regime in which the equilibrium density of particles tends to zero as |V|→∞. Our results allow us to recover very directly and improve to ℓp-mixing, p≥2, and to more general graphs, the mixing time results of Pillai and Smith for the Fredrickson–Andersen one spin facilitated (FA-1f) KCM on the discrete d-dimensional torus. In view of applications to the more complex FA-jf KCM, j>1, we also extend part of the analysis to an analogous process with a more general product state space.

Biased-voter model: How persuasive a small group can be?

We study the voter model dynamics in the presence of confidence and bias. We assume two types of voters. Unbiased voters whose confidence is indifferent to the state of the voter and biased voters whose confidence is biased towards a common fixed preferred state. We study the problem analytically on the complete graph using mean field theory and on an Erdős-Rényi random network topology using the pair approximation, where we assume that the network of interactions topology is independent of the type of voters. We find that for the case of a random initial setup, and for sufficiently large number of voters N, the time to consensus increases proportionally to log(N)/γv, with γ the fraction of biased voters and v the parameter quantifying the bias of the voters (v = 0 no bias). We verify our analytical results through numerical simulations. We study this model on a topology of the network of interactions depending on the bias, and examine two distinct, global average-degree preserving strategies (model I and model II) to obtain such random topologies starting from the random topology independent of bias case as the initial setup. Keeping all other parameters constant, in model I, μBU, the average number of links among biased (B) and unbiased (U) voters is varied at the expense of μUU and μBB, i.e. the average number of links among only unbiased and biased voters respectively. In model II, μBU is kept constant, while μBB is varied at the expense of μUU. We find that if the agents follow the strategy described by model II, they can achieve a significant reduction in the time to reach consensus as well as an increment in the probability to reach consensus to the preferred state. Hence, persuasiveness of the biased group depends on how well its members are connected among each other, compared to how well the members of the unbiased group are connected among each other.

How Twitter data sampling biases U.S. voter behavior characterizations.

Online social media are key platforms for the public to discuss political issues. As a result, researchers have used data from these platforms to analyze public opinions and forecast election results. The literature has shown that due to inauthentic actors such as malicious social bots and trolls, not every message is a genuine expression from a legitimate user. However, the prevalence of inauthentic activities in social data streams is still unclear, making it difficult to gauge biases of analyses based on such data. In this article, we aim to close this gap using Twitter data from the 2018 U.S. midterm elections. We propose an efficient and low-cost method to identify voters on Twitter and systematically compare their behaviors with different random samples of accounts. We find that some accounts flood the public data stream with political content, drowning the voice of the majority of voters. As a result, these hyperactive accounts are over-represented in volume samples. Hyperactive accounts are more likely to exhibit various suspicious behaviors and to share low-credibility information compared to likely voters. Our work provides insights into biased voter characterizations when using social media data to analyze political issues.

A New Model for Bias-Generating Agent-Based Simulation and Its Application to Election Systems: Allowing Agents to Make Mistakes for a Reason

Several studies have proposed that vote tampering based on heuristic algorithms can manipulate voters’ votes. It can be found from the analysis of the poll results of the 2016 US election that the frequency of “Trump won,” which is generally considered a black swan phenomenon, is not low and even reached 16.8%. However, many models are unable to restore the generation of such a high frequency of black swan phenomena. In this study, the black swan phenomenon is successfully reproduced using a bias-generating agent-based election system model. By adjusting the tampering method, the frequency of the black swan phenomenon will change from 5% to 15%. From the simulation results, it can be observed that one of the possible causes of the black swan phenomenon is the tampering of the voting results, which leads to more biased voters, thus increasing the frequency of the winning elections. This study proposes that to obtain more realistic simulation results, it is necessary to introduce more realistic perceptual models for agents, rather than relying solely on random functions. Allowing agents to make mistakes for a reason should be an integral part of multi-agent-based simulation in the field of pairwise human simulation.

Stochastic PDEs on graphs as scaling limits of discrete interacting systems

Stochastic partial differential equations (SPDE) on graphs were recently introduced by Cerrai and Freidlin (Ann. Inst. Henri Poincaré Probab. Stat. 53 (2017) 865–899). This class of stochastic equations in infinite dimensions provides a minimal framework for the study of the effective dynamics of much more complex systems. However, how they emerge from microscopic individual-based models is still poorly understood, partly due to complications near vertex singularities. In this work, motivated by the study of the dynamics and the genealogies of expanding populations in spatially structured environments, we obtain a new class of SPDE on graphs of Wright–Fisher type which have nontrivial boundary conditions on the vertex set. We show that these SPDE arise as scaling limits of suitably defined biased voter models (BVM), which extends the scaling limits of Durrett and Fan (Ann. Appl. Probab. 26 (2016) 3456–3490). We further obtain a convergent simulation scheme for each of these SPDE in terms of a system of Itô SDEs, which is useful when the size of the BVM is too large for stochastic simulations. These give the first rigorous connection between SPDE on graphs and more discrete models, specifically, interacting particle systems and interacting SDEs. Uniform heat kernel estimates for symmetric random walks approximating diffusions on graphs are the keys to our proofs. Some open problems are provided as further motivations of our study.

An invariance principle for biased voter model interfaces

We consider one-dimensional biased voter models, where 1’s replace 0’s at a faster rate than the other way round, started in a Heaviside initial state describing the interface between two infinite populations of 0’s and 1’s. In the limit of weak bias, for a diffusively rescaled process, we consider a measure-valued process describing the local fraction of type 1 sites as a function of time. Under a finite second moment condition on the rates, we show that in the diffusive scaling limit there is a drifted Brownian path with the property that all but a vanishingly small fraction of the sites on the left (resp. right) of this path are of type 0 (resp. 1). This extends known results for unbiased voter models. Our proofs depend crucially on recent results about interface tightness for biased voter models.

The zealot voter model

Inspired by the spread of discontent as in the 2016 presidential election, we consider a voter model in which 0’s are ordinary voters and 1’s are zealots. Thinking of a social network, but desiring the simplicity of an infinite object that can have a nontrivial stationary distribution, space is represented by a tree. The dynamics are a variant of the biased voter: if $x$ has degree $d(x)$ then at rate $d(x)p_{k}$ the individual at $x$ consults $k\ge 1$ neighbors. If at least one neighbor is 1, they adopt state 1, otherwise they become 0. In addition at rate $p_{0}$ individuals with opinion 1 change to 0. As in the contact process on trees, we are interested in determining when the zealots survive and when they will survive locally.

Equilibrium interfaces of biased voter models

A one-dimensional interacting particle system is said to exhibit interface tightness if starting in an initial condition describing the interface between two constant configurations of different types, the process modulo translations is positive recurrent. In a biological setting, this describes two populations that do not mix, and it is believed to be a common phenomenon in one-dimensional particle systems. Interface tightness has been proved for voter models satisfying a finite second moment condition on the rates. We extend this to biased voter models. Furthermore, we show that the distribution of the equilibrium interface for the biased voter model converges to that of the voter model when the bias parameter tends to zero. A key ingredient is an identity for the expected number of boundaries in the equilibrium voter model interface, which is of independent interest.

The dark side of the vote: Biased voters, social information, and information aggregation through majority voting

We experimentally investigate information aggregation through majority voting when voters are biased. In such situations, majority voting can have a “dark side,” that is, result in groups making choices inferior to those made by individuals acting alone. In line with theoretical predictions, information on the popularity of policy choices is beneficial when a minority of voters is biased, but harmful when a majority is biased. In theory, information on the success of policy choices elsewhere de-biases voters and alleviates the inefficiency. However, in the experiment, providing social information on success is ineffective and does not sufficiently de-bias voters.

A spatial model for selection and cooperation

Abstract We study the evolution of cooperation in an interacting particle system with two types. The model we investigate is an extension of a two-type biased voter model. One type (called defector) has a (positive) bias α with respect to the other type (called cooperator). However, a cooperator helps a neighbor (either defector or cooperator) to reproduce at rate γ. We prove that the one-dimensional nearest-neighbor interacting dynamical system exhibits a phase transition at α = γ. A special choice of interaction kernels yield that for α > γ cooperators always die out, but if γ > α, cooperation is the winning strategy.

New approach of financial volatility duration dynamics by stochastic finite-range interacting voter system.

We make an approach on investigating the fluctuation behaviors of financial volatility duration dynamics. A new concept of volatility two-component range intensity (VTRI) is developed, which constitutes the maximal variation range of volatility intensity and shortest passage time of duration, and can quantify the investment risk in financial markets. In an attempt to study and describe the nonlinear complex properties of VTRI, a random agent-based financial price model is developed by the finite-range interacting biased voter system. The autocorrelation behaviors and the power-law scaling behaviors of return time series and VTRI series are investigated. Then, the complexity of VTRI series of the real markets and the proposed model is analyzed by Fuzzy entropy (FuzzyEn) and Lempel-Ziv complexity. In this process, we apply the cross-Fuzzy entropy (C-FuzzyEn) to study the asynchrony of pairs of VTRI series. The empirical results reveal that the proposed model has the similar complex behaviors with the actual markets and indicate that the proposed stock VTRI series analysis and the financial model are meaningful and feasible to some extent.

Genealogies in expanding populations

The goal of this paper is to prove rigorous results for the behavior of genealogies in a one-dimensional long range biased voter model introduced by Hallatschek and Nelson [Theor. Pop. Biol. 73 (2008) 158–170]. The first step, which is easily accomplished using results of Mueller and Tribe [Probab. Theory Related Fields 102 (1995) 519–545], is to show that when space and time are rescaled correctly, our biased voter model converges to a Wright–Fisher SPDE. A simple extension of a result of Durrett and Restrepo [Ann. Appl. Probab. 18 (2008) 334–358] then shows that the dual branching coalescing random walk converges to a branching Brownian motion in which particles coalesce after an exponentially distributed amount of intersection local time. Brunet et al. [Phys. Rev. E (3) 76 (2007) 041104, 20] have conjectured that genealogies in models of this type are described by the Bolthausen–Sznitman coalescent, see [Proc. Natl. Acad. Sci. USA 110 (2013) 437–442]. However, in the model we study there are no simultaneous coalescences. Our third and most significant result concerns “tracer dynamics” in which some of the initial particles in the biased voter model are labeled. We show that the joint distribution of the labeled and unlabeled particles converges to the solution of a system of stochastic partial differential equations. A new duality equation that generalizes the one Shiga [In Stochastic Processes in Physics and Engineering (Bielefeld, 1986) (1988) 345–355 Reidel] developed for the Wright–Fisher SPDE is the key to the proof of that result.

Genealogies in expanding populations

The goal of this paper is to prove rigorous results for the behavior of genealogies in a one-dimensional long range biased voter model introduced by Hallatschek and Nelson [25]. The first step, which is easily accomplished using results of Mueller and Tribe [38], is to show that when space and time are rescaled correctly, our biased voter model converges to a Wright-Fisher SPDE. A simple extension of a result of Durrett and Restrepo [18] then shows that the dual branching coalescing random walk converges to a branching Brownian motion in which particles coalesce after an exponentially distributed amount of intersection local time. Brunet et al. [8] have conjectured that genealogies in models of this type are described by the Bolthausen-Sznitman coalescent, see [39]. However, in the model we study there are no simultaneous coalescences. Our third and most significant result concerns "tracer dynamics" in which some of the initial particles in the biased voter model are labeled. We show that the joint distribution of the labeled and unlabeled particles converges to the solution of a system of stochastic partial differential equations. A new duality equation that generalizes the one Shiga [44] developed for the Wright-Fisher SPDE is the key to the proof of that result.

PLEASE DON'T REGULATE MY INTERNALITIES.

PLEASE DON'T REGULATE MY INTERNALITIES.

Electoral Competition with Ideologically Biased Voters

This paper studies a model of redistributive policies with deterministic voting, where two parties compete for an electorate consisting of groups which have different ideological preferences. In equilibrium, party electoral promises decrease with ideological biases, and a swing voter outcome emerges. Groups with extreme ideological preferences moreover are excluded from redistribution, and within moderate groups there is always a share of voters which, after the elections, receives a nil transfer. A problem of tyranny of the majority thus arises which depends on ideology distribution. In this context, a decrease in the level of polarization causes a reduction in the degree of tyranny of the majority and in party electoral promises.

The Williams–Bjerknes model on regular trees

We consider the Williams–Bjerknes model, also known as the biased voter model on the $d$-regular tree $\mathbb{T} ^{d}$, where $d\geq3$. Starting from an initial configuration of “healthy” and “infected” vertices, infected vertices infect their neighbors at Poisson rate $\lambda\geq1$, while healthy vertices heal their neighbors at Poisson rate $1$. All vertices act independently. It is well known that starting from a configuration with a positive but finite number of infected vertices, infected vertices will continue to exist at all time with positive probability if and only if $\lambda>1$. We show that there exists a threshold $\lambda_{c}\in(1,\infty)$ such that if $\lambda>\lambda_{c}$ then in the above setting with positive probability, all vertices will become eventually infected forever, while if $\lambda<\lambda_{c}$, all vertices will become eventually healthy with probability $1$. In particular, this yields a complete convergence theorem for the model and its dual, a certain branching coalescing random walk on $\mathbb{T} ^{d}$—above $\lambda_{c}$. We also treat the case of initial configurations chosen according to a distribution which is invariant or ergodic with respect to the group of automorphisms of $\mathbb{T} ^{d}$.