We develop a dynamic model of information transmission and aggregation in social networks in which continued membership in the network is contingent on the accuracy of opinions. Agents have opinions about a state of the world and form links to others in a directed fashion probabilistically. Agents update their opinions by averaging those of their connections, weighted by how long their connections have been in the system. Agents survive or die based on how far their opinions are from the true state. In contrast to the results in the extant literature on DeGroot learning, we show through simulations that for some parameterizations the model cycles stochastically between periods of high connectivity, in which agents arrive at a consensus opinion close to the state, and periods of low connectivity, in which agents’ opinions are widely dispersed.

我们开发了一种动态的社交网络信息传输和聚集模型，其中，网络的持续成员资格取决于意见的准确性。代理对世界的状态有意见，并有可能以有针对性的方式与他人建立联系。代理通过平均其连接的意见来更新其意见，并对其连接已进入系统的时间加权。特工根据其意见与真实状态的距离而生存或死亡。与现有关于DeGroot学习的文献中的结果相反，我们通过仿真显示，对于某些参数化，模型在高连通性时段之间随机地循环，在该阶段，代理人会得出与该状态接近的共识，而在低连通性时期，代理商的意见广泛散布在其中。