Van Kampen's expansion approach in an opinion formation model

Lama, M. S. de la; Szendro, Ivan G.; Iglesias, J. R.; Wio, Horacio S.

doi:10.1140/epjb/e2006-00232-8

Cited by 45 publications

(40 citation statements)

References 21 publications

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“…Bounded confidence allows for an extension of the Sznajd model to real-valued opinions (Fortunato, 2005b). Other studies focused on the dynamics of clusters of agents with regular opinion patterns, ferromagnetic and/or antiferromagnetic (Schneider and Hirtreiter, 2005b), damage spreading (Klietsch, 2005;Roehner et al, 2004), the combination of Sznajd with other convincing strategies (Sousa and Sánchez, 2006), contrarian behavior (de la Lama et al, 2005;Wio et al, 2006), the effect on the dynamics of agents biased towards the global majority and/or minority opinion (Schneider, 2004;Schneider and Hirtreiter, 2005a). Recently extensions of Sznajd B to higher dimensions have been considered as well (Kondrat and Sznajd-Weron, 2008).…”

Section: E Sznajd Modelmentioning

confidence: 99%

Statistical physics of social dynamics

2009

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Statistical physics has proven to be a very fruitful framework to describe phenomena outside the realm of traditional physics. The last years have witnessed the attempt by physicists to study collective phenomena emerging from the interactions of individuals as elementary units in social structures. Here we review the state of the art by focusing on a wide list of topics ranging from opinion, cultural and language dynamics to crowd behavior, hierarchy formation, human dynamics, social spreading. We highlight the connections between these problems and other, more traditional, topics of statistical physics. We also emphasize the comparison of model results with empirical data from social systems.

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Section: E Sznajd Modelmentioning

confidence: 99%

Statistical physics of social dynamics

2009

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“…We finally examine the dynamics when only two opinions are allowed. The modeling of such systems is of great interest and has been extensively studied [1,15,[17][18][19] since there are many situations in real life where binary choices have to be made [1]. Furthermore, the above results, associated with Figs.…”

Section: Resultsmentioning

confidence: 85%

“…A great deal of effort has been devoted to developing models for describing the properties of systems made up of agents with competing opinions [1,[4][5][6][7][14][15][16][17][18][19][20]. This is of great relevance as human conflicts very often arise from the simultaneous existence of incompatible opinions in populations.…”

Section: Introductionmentioning

confidence: 99%

Dynamical model for competing opinions

Souza

Gonçalves

2012

Phys. Rev. E

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We propose an opinion model based on agents located at the vertices of a regular lattice. Each agent has an independent opinion (among an arbitrary, but fixed, number of choices) and its own degree of conviction. The latter changes every time two agents which have different opinions interact with each other. The dynamics leads to size distributions of clusters (made up of agents which have the same opinion and are located at contiguous spatial positions) which follow a power law, as long as the range of the interaction between the agents is not too short; i.e., the system self-organizes into a critical state. Short range interactions lead to an exponential cutoff in the size distribution and to spatial correlations which cause agents which have the same opinion to be closely grouped. When the diversity of opinions is restricted to two, a nonconsensus dynamic is observed, with unequal population fractions, whereas consensus is reached if the agents are also allowed to interact with those located far from them. The individual agents' convictions, the preestablished interaction range, and the locality of the interaction between a pair of agents (their neighborhood has no effect on the interaction) are the main characteristics which distinguish our model from previous ones.

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“…Consequently, the structure of electoral network is very complex. Nonetheless, some features of electoral dynamics can be simulated on networks with relatively simple structures or even square lattices [11][12][13][14][15][16][17][18][19][20]. This enables us to study how the factors, which have been chosen as the important ones for a phenomenon under study, influence the outcome, while avoiding the possible effects of other attributes.…”

Section: Model and Details Of Numerical Simulationsmentioning

confidence: 99%

“…[6][7][8][9][10]. In this regard, the introduction of a neutral state can also have a considerable impact on the spin dynamics [11][12][13][14][15]. Furthermore, heterogeneities in the initial spatial distributions of non-zero spins also affect the system dynamics [16].…”

Section: Introductionmentioning

confidence: 99%

Effect of Heterogeneity in Initial Geographic Distribution on Opinions’ Competitiveness

Balankin

Cruz

Martínez

et al. 2015

Entropy

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Spin dynamics on networks allows us to understand how a global consensus emerges out of individual opinions. Here, we are interested in the effect of heterogeneity in the initial geographic distribution of a competing opinion on the competitiveness of its own opinion. Accordingly, in this work, we studied the effect of spatial heterogeneity on the majority rule dynamics using a three-state spin model, in which one state is neutral. Monte Carlo simulations were performed on square lattices divided into square blocks (cells). Accordingly, one competing opinion was distributed uniformly among cells, whereas the spatial distribution of the rival opinion was varied from the uniform to heterogeneous, with the median-to-mean ratio in the range from 1 to 0. When the size of discussion group is odd, the uncommitted agents disappear completely after = tr T 3.30 ± 0.05 update cycles, and then the system evolves in a two-state regime with complementary spatial distributions of two competing opinions. Even so, the initial heterogeneity in the spatial distribution of one of the competing opinions causes a decrease of this opinion competitiveness. That is, the opinion with initially heterogeneous spatial distribution has less probability to win, than the opinion with the initially uniform spatial distribution, even when the initial concentrations of both opinions are equal. We found that although the time to consensus OPEN ACCESSEntropy 2015, 17 3161 tr C T T >> , the opinion's recession rate is determined during the first 3.3 update cycles. On the other hand, we found that the initial heterogeneity of the opinion spatial distribution assists the formation of quasi-stable regions, in which this opinion is dominant. The results of Monte Carlo simulations are discussed with regard to the electoral competition of political parties.

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Van Kampen's expansion approach in an opinion formation model

Cited by 45 publications

References 21 publications

Statistical physics of social dynamics

Statistical physics of social dynamics

Dynamical model for competing opinions

Effect of Heterogeneity in Initial Geographic Distribution on Opinions’ Competitiveness

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