Norm Emergence via Influential Weight Propagation in Complex Networks

Shibusawa, Ryosuke; Sugawara, Toshiharu

doi:10.1109/enic.2014.28

Cited by 9 publications

(1 citation statement)

References 6 publications

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“…Clearly, the scale-free network is the most efficient network for achieving high level of consensus compared with the other two networks. Previous studies have shown that this effect is due to the small graph diameter of scale-free networks 11 39 40 41 .…”

Section: Resultsmentioning

confidence: 97%

Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

Tan

et al. 2016

Sci Rep

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Learning is an important capability of humans and plays a vital role in human society for forming beliefs and opinions. In this paper, we investigate how learning affects the dynamics of opinion formation in social networks. A novel learning model is proposed, in which agents can dynamically adapt their learning behaviours in order to facilitate the formation of consensus among them, and thus establish a consistent social norm in the whole population more efficiently. In the model, agents adapt their opinions through trail-and-error interactions with others. By exploiting historical interaction experience, a guiding opinion, which is considered to be the most successful opinion in the neighbourhood, can be generated based on the principle of evolutionary game theory. Then, depending on the consistency between its own opinion and the guiding opinion, a focal agent can realize whether its opinion complies with the social norm (i.e., the majority opinion that has been adopted) in the population, and adapt its behaviours accordingly. The highlight of the model lies in that it captures the essential features of people’s adaptive learning behaviours during the evolution and formation of opinions. Experimental results show that the proposed model can facilitate the formation of consensus among agents, and some critical factors such as size of opinion space and network topology can have significant influences on opinion dynamics.

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