开放多智能体系统中平均一致性的下界性能（扩展版）
Lower bound performances for average consensus in open multi-agent systems (extended version)
摘要：
我们得到的内在平均算法在开放的多智能体系统，这是系统的随机到达和离开的代理的性能的基本限制。每个代理持有一个值，其目标是估计系统中当前代理的平均值。我们提供了一个下界的预期均方误差的任何估计算法，假设代理的数量保持不变，通信是随机的，成对的。我们的推导是基于预期的误差与最优算法的条件下获得的更有利的比那些实际的问题允许，并依赖于分析的约束条件的信息传播机制在系统中，这些放宽。
英文摘要：
We derive fundamental limitations on the performances of intrinsic averaging algorithms in open multi-agent systems, which are systems subject to random arrivals and departures of agents. Each agent holds a value, and their goal is to estimate the average of the values of the agents presently in the system. We provide a lower bound on the expected Mean Square Error for any estimation algorithm, assuming that the number of agents remains constant and that communications are random and pairwise. Our derivation is based on the expected error obtained with an optimal algorithm under conditions more favorable than those the actual problem allows, and relies on an analysis of the constraints on the information spreading mechanisms in the system, and relaxations of these.
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