论文标题
随机合作下的社会学习
Social Learning under Randomized Collaborations
论文作者
论文摘要
我们研究一个社会学习计划，每次瞬间，每个代理都选择随机从其邻居中接收信息。我们表明，在这种稀疏的沟通方案下，代理商最终了解了真相，并且渐近收敛率与使用更多通信资源的标准算法保持不变。对于特殊情况下，我们还得出了对数值比率的较大偏差估计值，在这种情况下，每个代理都用所选邻居的信念取代了其信念。
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.
