Social learning with multiple true states
Physica A: Statistical Mechanics and its Applications(2019)
摘要
In order to investigate social learning with multiple true states, a social learning model with time-varying topology and reliance weight is proposed. In this model, a time-varying topology mechanism for social networks is constructed since people always tend to communicate with those who have similar opinions with them. Simultaneously, the adaptive time-varying reliance weight mechanism is designed according to the closeness degree of agents’ neighbors. The simulation results show that asymptotic learning can be achieved and communities emerge under certain parameter values. Finally, how the parameters influence the belief evolution is analyzed, and a first order phase transition phenomenon is discovered.
更多查看译文
关键词
Social learning,Multiple true states,Asymptotic learning,Community emergence,Parameter analysis
AI 理解论文
溯源树
样例
![](https://originalfileserver.aminer.cn/sys/aminer/pubs/mrt_preview.jpeg)
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要