Consequence of reputation in the Sznajd consensus model

Abstract

In this work we study a modified version of the Sznajd sociophysics model. In particular we introduce reputation, a mechanism that limits the capacity of persuasion of the agents. The reputation is introduced as a score which is time-dependent, and its introduction avoid dictatorship (all spins parallel) for a wide range of parameters. The relaxation time follows a log-normal-like distribution. In addition, we show that the usual phase transition also occurs, as in the standard model, and it depends on the initial concentration of individuals following an opinion, occurring at a initial density of up spins greater than $1/2$. The transition point is determined by means of a finite-size scaling analysis.Comment: 8 pages, 4 figures, To be published in Physics Letters

Opinion evolution influenced by informed agents

“…6 In the discrete opinion models, each agent carries one of a finite number of different opinions, frequently two opposite 7 binary points of view (e.g., yes or no, support or opposition, accept or reject). Among several different discrete opinion 8 models, we highlight voter model [17,18], Sznajd model [19][20][21], and majority-rule model [22][23][24]. In the voter model, each 9 agent is randomly chosen to adopt the opinion of one of his neighbors, whereas in the Sznajd model two agents with the Q3 10 same opinion persuade their neighbors to accept their opinion.…”

“…18 In addition to the generic opinion dynamics models, some researchers have attempted to develop the specific models to 19 explore the practical opinion evolution processes on several online social media [29][30][31]. For example, Xiong and Liu [30] 20 collected people's comments on three electronic products (i.e., ''iPhone 4'', ''Blackberry'', and ''iPad 2'') from Twitter and 21 proposed an opinion model to explore the evolution processes of people's opinions on the three electronic products. 22 The aforementioned opinion models have been employed to study the influences of contrarian agents [32], inflexible 23 agents [28,33,34], opinion leaders [35], and extremists [1] on the dynamic evolution of public opinions, and the obtained 24 results have been used to explain some sociological phenomena, such as hung elections [11] and extremism propagation 25 [36,37].…”

Effect of Heterogeneity in Initial Geographic Distribution on Opinions’ Competitiveness

“…The system evolution is governed by an updating rule and is strongly dependent on the network topology, connectivity, heterogeneity, etc. [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].…”

Reaching a nonlinear consensus: Polynomial stochastic operators

“…Since that time, the consensus which is the most ubiquitous phenomenon of multi-agent systems becomes popular in various scientific communities, such as biology, physics, control engineering and social science [2][3][4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The dynamics of opinion sharing, competing, and the emergence of consensus have became an active topic of the recent research in statistical and nonlinear physics [21].…”

“…However, many systems, such as the well-known Kuramoto oscillator, exhibit nonlinear, locally passive dynamics as discussed in [44]. The consensus problem in some nonlinear protocols were studied in [5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. In this paper, we are aiming to provide some nonlinear protocols of multi-agent systems and to study the consensus problem for the provided nonlinear protocols.…”