随机协作下的社会学习
Social Learning under Randomized Collaborations
摘要：
我们研究了一个社会学习方案，在每一个时刻，每个代理选择随机接收来自其邻居之一的信息。我们表明，在这种稀疏的通信方案下，代理学习的真理最终和渐近收敛速度保持不变的标准算法，使用更多的通信资源。我们还推导出一个特殊情况下，每个代理取代其信念与所选择的邻居的对数置信比的大偏差估计。
英文摘要：
We study a social learning scheme where at every time instant, each agent chooses to receive information from one of its neighbors at random. We show that under this sparser communication scheme, the agents learn the truth eventually and the asymptotic convergence rate remains the same as the standard algorithms which use more communication resources. We also derive large deviation estimates of the log-belief ratios for a special case where each agent replaces its belief with that of the chosen neighbor.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
