Majority Rule Based Opinion Dynamics with Biased and Stubborn Agents

In this work, we study the opinion dynamics of majority-rule model on a complete graph with additional social behavior namely anticonformity. We consider four spins with three-one interaction; three spins persuade the fourth spin in the population. We perform analytical and numerical calculations to find the critical behavior of the system. From both, we obtained the agreement results, e.g. the system undergoes a second-order phase transition and the critical point of the system only depends on the population number. In addition, the critical point decays exponentially as the number population increases. For the infinite population, the obtained critical point is [Formula: see text], which agrees well with that of the previous work. We also obtained the critical exponents [Formula: see text] and [Formula: see text] of the model, thus, the model is in the same universality class with the mean-field Ising.

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Do zealots increase or decrease the polarization of social networks?

Journal of Complex Networks ◽

10.1093/comnet/cnz036 ◽

2019 ◽

Vol 8 (4) ◽

Author(s):

Snehal M Shekatkar

Keyword(s):

Social Networks ◽

Social Network ◽

Majority Rule ◽

Initial Conditions ◽

Opinion Dynamics ◽

Network Density ◽

Configuration Model ◽

Rule Model ◽

Total Fraction ◽

Topological Characteristics

Abstract Zealots are the vertices in a social network who do not change their opinions under social pressure and are crucial to the study of opinion dynamics on complex networks. In this article, we study the effect of zealots on the polarization dynamics of a deterministic majority rule model using the configuration model as a substrate. To this end, we propose a novel quantifier, called ‘correlated polarization’, for measuring the amount of polarization in the network when vertices can exist in two opposite states. The quantifier takes into account not only the fraction of vertices with each opinion but also how they are connected to each other. We then show that the presence of zealots does not have a fixed effect on the polarization, and can change it in positive, negative or neutral way depending upon their topological characteristics like degree, their total fraction in the network, density and degree heterogeneity of the network and the type of initial conditions of the dynamics. Our results particularly highlight the importance of the role played by the initial conditions in drifting the polarization towards lower or higher values as the total number of zealots is increased.

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Voter and Majority Dynamics with Biased and Stubborn Agents

Journal of Statistical Physics ◽

10.1007/s10955-020-02625-w ◽

2020 ◽

Vol 181 (4) ◽

pp. 1239-1265

Author(s):

Arpan Mukhopadhyay ◽

Ravi R. Mazumdar ◽

Rahul Roy

Keyword(s):

Majority Rule ◽

Branching Processes ◽

Mean Field ◽

Opinion Dynamics ◽

Majority Opinion ◽

Rule Model ◽

Interacting Agents ◽

The Mean ◽

Fully Connected ◽

Mean Time

Abstract We study binary opinion dynamics in a fully connected network of interacting agents. The agents are assumed to interact according to one of the following rules: (1) Voter rule: An updating agent simply copies the opinion of another randomly sampled agent; (2) Majority rule: An updating agent samples multiple agents and adopts the majority opinion in the selected group. We focus on the scenario where the agents are biased towards one of the opinions called the preferred opinion. Using suitably constructed branching processes, we show that under both rules the mean time to reach consensus is $$\varTheta (\log N)$$ Θ ( log N ) , where N is the number of agents in the network. Furthermore, under the majority rule model, we show that consensus can be achieved on the preferred opinion with high probability even if it is initially the opinion of the minority. We also study the majority rule model when stubborn agents with fixed opinions are present. We find that the stationary distribution of opinions in the network in the large system limit using mean field techniques.

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Performance Analysis of Regular Low-Density Generator-Matrix Codes Under Majority-Rule Based Iterative Decoding Algorithm

IEEE GLOBECOM 2007-2007 IEEE Global Telecommunications Conference ◽

10.1109/glocom.2007.740 ◽

2007 ◽

Author(s):

Cheng-Chun Chang ◽

Heung-No Lee

Keyword(s):

Performance Analysis ◽

Majority Rule ◽

Iterative Decoding ◽

Low Density ◽

Generator Matrix ◽

Decoding Algorithm ◽

Rule Based ◽

Matrix Codes

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Majority-Rule-Based Web Service Selection

Web Information Systems Engineering - WISE 2012 - Lecture Notes in Computer Science ◽

10.1007/978-3-642-35063-4_54 ◽

2012 ◽

pp. 689-695 ◽

Cited By ~ 4

Author(s):

Karim Benouaret ◽

Dimitris Sacharidis ◽

Djamal Benslimane ◽

Allel Hadjali

Keyword(s):

Web Service ◽

Majority Rule ◽

Service Selection ◽

Rule Based ◽

Web Service Selection

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Majority rule dynamics between a double coalition and a third opinion: coalition profit models and majority coalition ties

Adaptive Behavior ◽

10.1177/1059712319895486 ◽

2020 ◽

pp. 105971231989548

Author(s):

Felipe Gayosso Martínez ◽

Alexander Balankin

Keyword(s):

Majority Rule ◽

Social Dynamics ◽

Opinion Dynamics ◽

Network Size ◽

Opinion Formation ◽

Random Choice ◽

Rule Model ◽

Rule Dynamics ◽

Lattice Network ◽

Consensus Time

This article explores the opinion dynamics of a double coalition opinion against a third opinion under majority rule updates on odd fixed size connected groups. For this purpose, coalition benefit criteria and three opinion formation models which extend the 2-state majority rule model on lattices are introduced. The proposed models focus on the coalition profit of its constituent coalition opinions and cover the possible final scenarios from coalition alliance perspective: either minor opinion or major opinion is favored, or dynamics do not favor to any coalition opinion. Opinion exchanges take place on a torus embedded lattice network of a 3-state system having in consideration tie configurations and two rules to break them: either by random choice or leaving ties unaltered. Models were analyzed in the statistical mechanics spirit through Monte Carlo simulations without node replacement. Estimations for coalition benefits, the growth of coalition ties, and consensus probabilities are reported. The loss of coalition strengths due to coalition ties and its indecision is indicated. In particular, the logistic decay of consensus probability is due to the logistic adaptive growth of coalition ties. Scaling behaviors for consensus time and coalition ties in terms of network size are suggested. The results of numerical simulations are discussed in the context of social influence and social dynamics.

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