论文标题
与随机挖掘有关的非拜拜西亚社会学习，具有多个变化的网络连接性
Non-Bayesian Social Learning on Random Digraphs with Aperiodically Varying Network Connectivity
论文作者
论文摘要
我们在随机的定向图上研究了非巴约西亚社会学习，并表明在轻度连通性假设下，如果相关的加权邻接矩阵的顺序属于$ \ pstar $（一类均匀连接的紧密连接链），那么所有代理几乎肯定会及时学习世界的真实状态。我们表明，统一的牢固的连通性在不需要渐近学习的同时，可以确保所有代理人的信念几乎可以肯定地融合到共识，即使不可识别真正的状态。然后，我们提供了我们主要结果的一些推论，其中一些适用于原始更新规则的变体，例如通过扩散和适应性进行惯性的非bayesian学习和学习。其他包括有关社会学习的已知结果的扩展。我们还表明，如果影响网络在某种意义上是平衡的，那么渐近学习即使在没有统一的牢固的连通性的情况下也肯定会发生。
We study non-Bayesian social learning on random directed graphs and show that under mild connectivity assumptions, all the agents almost surely learn the true state of the world asymptotically in time if the sequence of the associated weighted adjacency matrices belongs to Class $\pstar$ (a broad class of stochastic chains that subsumes uniformly strongly connected chains). We show that uniform strong connectivity, while being unnecessary for asymptotic learning, ensures that all the agents' beliefs converge to a consensus almost surely, even when the true state is not identifiable. We then provide a few corollaries of our main results, some of which apply to variants of the original update rule such as inertial non-Bayesian learning and learning via diffusion and adaptation. Others include extensions of known results on social learning. We also show that, if the network of influences is balanced in a certain sense, then asymptotic learning occurs almost surely even in the absence of uniform strong connectivity.
