Modelling Adaptive Learning Behaviours for Consensus Formation in Human Societies

Chao Yu, Guozhen Tan, Hongtao Lv, Zhen Wang, Jun Meng, Jianye Hao, Fenghui Ren
2016 Scientific Reports  
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
more » ... 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. Opinion dynamics is an attempt at understanding the evolution and formation of social opinions achieved through microscopic interactions between individuals in a multiagent society 1-4 . Researchers from a variety of disciplines including statistical physics, econophysics, sociophysics and computer science have made significant contributions to this field 5-7 . By using theoretical models and experimental methods, socially macroscopic phenomena such as global consensus (i.e., social norm), polarization, or anarchy (diversity of opinions) can be observed and analyzed, providing us a comprehensive understanding of the dynamics of evolution and formation of opinions 8-10 , social conventions and rules 11,12 , as well as languages 13,14 in human societies. In the literature, a number of opinion dynamics models, such as the classic voter model 15 , the Galam model 16 , the social impact model 17 , the Sznajd model 18 , the Deffuant model 19 and the Kraus-Hegselmanmodel model 20 , have been proposed and extensively analyzed. Other models have focused on investigating the influence of social factors such as information sharing or exchange on the evolution of opinions 21, 22 . Also, there is abundant of research in the area of evolutionary game theory to investigate how opinions (i.e., defection and cooperation) evolve based on their interaction performance 23 . In most opinion dynamics models, each individual is considered to be an agent holding continuous or discrete opinions in favor of one decision or choice (accept/reject, or cooperate/defect), and each individual interacts with others and tries to persuade or impact others through his/ her opinion. The focus is on investigating macroscopic phenomenon achieved through local dynamics that are based on simple social learning rules, such as local majority and conformity 8,24 , imitating a neighbor 23,25 , or the coupling of these two rules 26, 27 . In real-life situations, however, people's decision making is far more complex than simple imitation or voting. Rather, people usually learn through trail-and-error interactions with others when facing uncertainties about their decisions or choices. This kind of experience-based learning is an essential capability of human and plays a
doi:10.1038/srep27626 pmid:27282089 pmcid:PMC4901282 fatcat:66bhzatayjeohjtm3rvkarj73a