时变有向图上不确定模型的非贝叶斯社会学习
Non-Bayesian Social Learning with Uncertain Models over Time-Varying Directed Graphs
摘要：
我们研究了具有不确定模型的非贝叶斯社会学习问题，在该模型中，代理网络根据一系列观察到的信号寻求合作识别世界的状态。与现有的文献相比，我们把注意力集中在这样的情况下，由代理人持有的统计模型的可能状态的世界是建立从有限的观察。我们发现，现有的非贝叶斯社会学习方法可能会选择一个错误的假设与非零概率在这些条件下。因此，我们提出了一种新的算法来迭代地构建一组信念，表明某个假设是否得到了经验证据的支持。该算法可以在时变有向图上实现，且权值为非{-}双随机。
英文摘要：
We study the problem of non-Bayesian social learning with uncertain models, in which a network of agents seek to cooperatively identify the state of the world based on a sequence of observed signals. In contrast with the existing literature, we focus our attention on the scenario where the statistical models held by the agents about possible states of the world are built from finite observations. We show that existing non-Bayesian social learning approaches may select a wrong hypothesis with non-zero probability under these conditions. Therefore, we propose a new algorithm to iteratively construct a set of beliefs that indicate whether a certain hypothesis is supported by the empirical evidence. This new algorithm can be implemented over time-varying directed graphs, with non{-}doubly stochastic weights.
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