Narrow Results

We propose a mechanism of opinion evolution in a closed system. The opinion evolution process can be described as the disposal of multi-information inputs with one opinion accepted as the output. We introduce the opinion neutrality in the system, observing the characteristics of the opinion evolution process; and find that the initial opinion distribution fractions play an important role in the final results. When the two opposite opinions' fraction are equal, the neutrality fraction will affect the final opinion distribution dramatically.

In this paper we study the model of opinion dynamics with individuals on nodes of a scale-free network, introducing an opinion evolution mechanism and according to the nodes' degrees defining broad-sense-concept leaders on the network. We compare the strength of opinion influence between leaders and followers. With computer simulations of opinion dynamics, we found that the more complex is the scale-free network, the easier for leaders to make their opinion accepted by the masses.

The Sznajd model for the spread of opinions gives a good explanation of the influence among the neighbors, that is the external factor. In this paper, we add an internal factor to Sznajd model, which determines the probability of one's acceptance of other's opinion. We define this factor as social temperature, and we study the relationship between this temperature and time. We also consider the people who do not change their opinions as defenders and analyze their efforts.

We propose a three-opinion Sznajd model with limited persuasion in which two neighbors still can influence others when the two opinions differ by a small amount. The simulation results show that the initial condition plays a crucial role in the final results and the interaction between people's opinion occurs in the common boundaries of the small districts constituted by the people holding the same opinion. We apply the model to describe the interaction on the attitude toward studying between students in a class or school which is similar with the opinion evolution and give a rough explanation about why a class or school mostly consists of ordinary students.

In this paper, the Sznajd model is used to study the opinion evolution but with parents influence and social influence division, as well as age barriers between different social members when interacting. To get the age information, we introduce Penna model to describe the human society. The result indicates that age does not affect the final consensus of the Sznajd model, but it has effect on the opinion distribution area when the initial state is 50% for either of the two opinions. With the presence of parents influence, the fluctuations of opinions in different age groups are different. When the site is not fully filled initially, with increasing age threshold, the fluctuation of opinions becomes larger.

Web encounter facilitate contacts between people from different communities outside space and time. Implicit Community Structure is exhibited because of highly connected links within community and sparse encounters between communities. Considering the imperceptible influence of encounter on opinions, Sznajd updating rules are used to mimic people's behaviors after encountering a stranger in another community. We introduce a model for opinion evolution, in which the interconnectivity between different communities is represented as encounter frequency, and leadership is introduced to control the strength of community's opinion guide. In this scenario, the effects of Implicit Community Structure of contact network on opinion evolution, for asymmetric and random initial distribution but with heterogeneous opinion guide, are investigated respectively. It is shown that large encounter frequency favors consensus of the whole populations and successful opinion spreading, which is qualitatively agree with the results observed in Majority model defined on substrates with predefined community structure.

Community structure is another important feature besides small-world and scale-free property of complex networks. Communities can be coupled through specific fixed links between nodes, or occasional encounter behavior. We introduce a model for opinion evolution with multiple cluster-coupled patterns, in which the interconnectivity denotes the coupled degree of communities by fixed links, and encounter frequency controls the coupled degree of communities by encounter behaviors. Considering the complicated cognitive system of people, the CODA (continuous opinions and discrete actions) update rules are used to mimic how people update their decisions after interacting with someone. It is shown that, large interconnectivity and encounter frequency both can promote consensus, reduce competition between communities and propagate some opinion successfully across the whole population. Encounter frequency is better than interconnectivity at facilitating the consensus of decisions. When the degree of social cohesion is same, small interconnectivity has better effects on lessening the competence between communities than small encounter frequency does, while large encounter frequency can make the greater degree of agreement across the whole populations than large interconnectivity can.

We consider a dynamic group composed with a constant number of people and the people will change periodically. Every member in the community owns a value of confidence — a mechanism that measures the agent’s coherence to his or her own attitude. Based on Cellular Automata, the opinions of all agents are synchronously updated. As long as the updating frequency and updating proportion are appropriate, the open system can reach a democracy-like steady state. The majority of agents in the community will hold the same opinion.