论文标题
图表上的社会学习的最佳聚合策略
Optimal Aggregation Strategies for Social Learning over Graphs
论文作者
论文摘要
自适应社会学习是研究图形上分布式决策问题的有用工具。本文研究了组合政策对自适应社会学习策略表现的影响。使用大差分分析，它首先衍生出稳态误差概率的结合，并表征了组合策略的Perron特征向量的最佳选择。随后，它通过估计低信噪比的适应时间来研究组合政策对学习策略瞬态行为的影响。在此过程中，人们发现，有趣的是，组合政策对瞬态行为的影响微不足道，因此采用增强稳态绩效的政策更为重要。理论结论通过计算机模拟说明。
Adaptive social learning is a useful tool for studying distributed decision-making problems over graphs. This paper investigates the effect of combination policies on the performance of adaptive social learning strategies. Using large-deviation analysis, it first derives a bound on the steady-state error probability and characterizes the optimal selection for the Perron eigenvectors of the combination policies. It subsequently studies the effect of the combination policy on the transient behavior of the learning strategy by estimating the adaptation time in the low signal-to-noise ratio regime. In the process, it is discovered that, interestingly, the influence of the combination policy on the transient behavior is insignificant, and thus it is more critical to employ policies that enhance the steady-state performance. The theoretical conclusions are illustrated by means of computer simulations.
