Social learning with bounded confidence and heterogeneous agents
Social learning with bounded confidence and heterogeneous agents
复制标题
具有有限置信度和异构代理的社会学习
DOI：
10.1016/j.physa.2013.01.007
复制
发表时间：
2013-05-15
影响因子：
3.3
通讯作者：
Wang, Xiaofan
中科院分区：
文献类型：
--
作者：
Liu, Qipeng;Wang, Xiaofan
研究了具有有限置信度和异质主体的社交网络中的意见形成模型。网络拓扑结构由信念的同质性形成，这意味着任何一对智能体只有在它们的信念差异不大于称为置信度界限的正常数时才是邻居。我们考虑一个模型与知情的代理人和不知情的代理人之间的本质区别是知情的代理人有机会获得外部信号的功能的潜在的真实状态的社会事件有关。更准确地说，知情代理更新他们的信念，结合贝叶斯后验信念的基础上，他们的私人观察和加权平均的信念，他们的邻居。不知情的代理更新他们的信念简单地通过线性组合的信念，他们的邻居。我们发现，只有当置信区间大于与人口密度相关的正阈值时，整个群体才能了解真实状态。此外，模拟表明，所需的集体学习的知情代理的比例随着人口密度的增加而减少。通过调整知情代理的学习速度，我们发现：速度越高，整个群体达到稳定状态所需的时间越短，另一方面，速度越高，成功学习的代理比例越低-存在权衡。(C)2013爱思唯尔有限公司版权所有。
This paper investigates an opinion formation model in social networks with bounded confidence and heterogeneous agents. The network topologies are shaped by the homophily of beliefs, which means any pair of agents are neighbors only if their belief difference is not larger than a positive constant called the bound of confidence. We consider a model with both informed agents and uninformed agents, the essential difference between which is the informed agents have access to outside signals which are function of the underlying true state of the social event concerned. More precisely, the informed agents update their beliefs by combining the Bayesian posterior beliefs based on their private observations and weighted averages of the beliefs of their neighbors. The uninformed agents update their beliefs simply by linearly combining the beliefs of their neighbors. We find that the whole group can learn the true state only if the bound of confidence is larger than a positive threshold which is related to the population density. Furthermore, simulations show that the proportion of informed agents required for collective learning decreases as the population density increases. By tuning the learning speed of informed agents, we find the following: the higher the speed, the shorter the time needed for the whole group to achieve a steady state, and on the other hand, the higher the speed, the lower the proportion of agents with successful learning - there is a trade-off. (C) 2013 Elsevier B.V. All rights reserved.