Bi-layer voter model: modeling intolerant/tolerant positions and bots in opinion dynamics

Vega-Oliveros, Didier A.; Grande, Helder L. C.; Iannelli, Flavio; Vázquez, Federico

doi:10.1140/epjs/s11734-021-00151-8

Cited by 5 publications

(5 citation statements)

References 37 publications

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“…They have already been used in studies of polarization (e.g., [8,18,[32][33][34]). Despite their seeming conceptual simplicity (compared to traditional, complicated, data-intensive multi-variate regression models prevalent in social sciences) network models allow testing of hypotheses regarding such issues as the role of the media in social dynamics (e.g., [30]), or the use of bot agents to alter public opinion [35]. Network models can be used to explore polarization trends and find avenues for intervention, to avert some of its negative consequences.…”

Section: Introductionmentioning

confidence: 99%

An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study

Diep

Kaufman

2023

Entropy

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World-wide, political polarization continues unabated, undermining collective decision-making ability. In this issue, we have examined polarization dynamics using a (mean-field) model borrowed from statistical physics, assuming that each individual interacted with each of the others. We use the model to generate scenarios of polarization trends in time in the USA and explore ways to reduce it, as measured by a polarization index that we propose. Here, we extend our work using a more realistic assumption that individuals interact only with “neighbors” (short-range interactions). We use agent-based Monte Carlo simulations to generate polarization scenarios, considering again three USA political groups: Democrats, Republicans, and Independents. We find that mean-field and Monte Carlo simulation results are quite similar. The model can be applied to other political systems with similar polarization dynamics.

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Section: Introductionmentioning

confidence: 99%

An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study

Diep

Kaufman

2023

Entropy

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“…They have already been specifically used in studies of polarization e.g., [ 9 , 12 , 19 , 35 , 36 , 37 , 38 ]. Despite the seeming simplicity of concept (compared to the traditional, complicated, and multi-variate regression models prevalent in social sciences), such models allow for the testing of hypotheses regarding issues such as the role of the media in social dynamics e.g., [ 33 ] or the use of bot agents to alter public opinion [ 39 ].…”

Section: Introductionmentioning

confidence: 99%

Statistical Mechanics of Political Polarization

Kaufman

Diep

2022

Entropy

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Rapidly increasing political polarization threatens democracies around the world. Scholars from several disciplines are assessing and modeling polarization antecedents, processes, and consequences. Social systems are complex and networked. Their constant shifting hinders attempts to trace causes of observed trends, predict their consequences, or mitigate them. We propose an equivalent-neighbor model of polarization dynamics. Using statistical physics techniques, we generate anticipatory scenarios and examine whether leadership and/or external events alleviate or exacerbate polarization. We consider three highly polarized USA groups: Democrats, Republicans, and Independents. We assume that in each group, each individual has a political stance s ranging between left and right. We quantify the noise in this system as a “social temperature” T. Using energy E, we describe individuals’ interactions in time within their own group and with individuals of the other groups. It depends on the stance s as well as on three intra-group and six inter-group coupling parameters. We compute the probability distributions of stances at any time using the Boltzmann probability weight exp(−E/T). We generate average group-stance scenarios in time and explore whether concerted interventions or unexpected shocks can alter them. The results inform on the perils of continuing the current polarization trends, as well as on possibilities of changing course.

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“…Vega-Oliveros et al [15] propose a voter model to study the interplay between the dynamics of opinions and the tolerance of individuals to accept others' opinions. The proposed model incorporates bots and radical or intolerant individuals in the decision-making process.…”

Section: Theory and Methodsmentioning

confidence: 99%

“…The theoretical aspects considered here cover a broad spectrum of topics such as collective dynamics of excitable systems, cluster dynamics [1], interplay of noise and feedback [2], subdiffusive behavior [5], spreading phenomena on networks [8], coarsening [9], multipartite networks, partial synchrony [11], and time-delayed interactions [13,14]. In a series of works, complex networks are successfully employed as tools for detecting artificially inserted data [3], community detection [4,12], identification of time series patterns [6], and modeling a voter dynamics [15].…”

Section: Introductionmentioning

confidence: 99%

Dynamical phenomena in complex networks: fundamentals and applications

Yanchuk

Roque

Macau

et al. 2021

Eur. Phys. J. Spec. Top.

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This special issue presents a series of 33 contributions in the area of dynamical networks and their applications. Part of the contributions is devoted to theoretical and methodological aspects of dynamical networks, such as collective dynamics of excitable systems, spreading processes, coarsening, synchronization, delayed interactions, and others. A particular focus is placed on applications to neuroscience and Earth science, especially functional climate networks. Among the highlights, various methods for dealing with noise and stochastic processes in neuroscience are presented. A method for constructing weighted networks with arbitrary topologies from a single dynamical node with delayed feedback is introduced. Also, a generalization of the concept of geodesic distances, a path-integral formulation of network-based measures is developed, which provides fundamental insights into the dynamics of disease transmission. The contributions from the Earth science application field substantiate predictive power of climate networks to study challenging Earth processes and phenomena.

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Bi-layer voter model: modeling intolerant/tolerant positions and bots in opinion dynamics

Cited by 5 publications

References 37 publications

An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study

An Agent-Based Statistical Physics Model for Political Polarization: A Monte Carlo Study

Statistical Mechanics of Political Polarization

Dynamical phenomena in complex networks: fundamentals and applications

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