早期适应趋势：使用被动通信的自稳定信息传播
Early Adapting to Trends: Self-Stabilizing Information Spread using Passive Communication
摘要：
如何在系统中高效、可靠地传播信息是分布式计算中最基本的问题之一。最近，受生物学场景的启发，一些作品专注于确定在错误条件下传播信息所需的最小通信资源。在这里，我们研究了Boczkowski，Korman和Natale在[SODA 2017]中介绍的自稳定比特传播问题。这个问题考虑了一个由n个代理人组成的全连接网络，有一个二元世界的观点，其中一个被称为正确的。在任何给定的时间，每个代理持有一个意见位作为其公共输出。种群包含一个知道哪种观点是正确的源代理。这个代理人接受正确的意见，并在整个执行过程中始终坚持。我们认为，基本的Pandem模型的通信，其中每个代理观察相对较少的随机选择的代理在每一轮。非源代理的目标是快速收敛于正确的意见，尽管具有任意的初始配置，即，以自我稳定的方式。一旦人们接受了正确的观点，他们就应该永远坚持下去。受动物观察他人行为并对其做出反应的生物学场景的启发，我们专注于被动通信的极端约束模型，该模型假设当观察另一个代理时，可以提取的唯一信息是该代理的意见位。我们证明，这个问题可以解决在一个多对数在n轮的概率很高，而在每一轮的代理人的对数数量进行采样。以前的作品解决了这个问题更快，使用更少的样本，但他们这样做是通过解耦的消息发送代理从他们的输出意见，因此不适合被动通信的框架。此外，这些工作使用复杂的递归算法，具有不太可能被生物实体使用的精确时钟。相比之下，我们提出的算法有一个自然的吸引力，因为它是基于让代理估计当前的趋势方向的动态，然后适应新出现的趋势。
英文摘要：
How to efficiently and reliably spread information in a system is one of the most fundamental problems in distributed computing. Recently, inspired by biological scenarios, several works focused on identifying the minimal communication resources necessary to spread information under faulty conditions. Here we study the self-stabilizing bit-dissemination problem, introduced by Boczkowski, Korman, and Natale in [SODA 2017]. The problem considers a fully-connected network of n agents, with a binary world of opinions, one of which is called correct. At any given time, each agent holds an opinion bit as its public output. The population contains a source agent which knows which opinion is correct. This agent adopts the correct opinion and remains with it throughout the execution. We consider the basic PULL model of communication, in which each agent observes relatively few randomly chosen agents in each round. The goal of the non-source agents is to quickly converge on the correct opinion, despite having an arbitrary initial configuration, i.e., in a self-stabilizing manner. Once the population converges on the correct opinion, it should remain with it forever. Motivated by biological scenarios in which animals observe and react to the behavior of others, we focus on the extremely constrained model of passive communication, which assumes that when observing another agent the only information that can be extracted is the opinion bit of that agent. We prove that this problem can be solved in a poly-logarithmic in n number of rounds with high probability, while sampling a logarithmic number of agents at each round. Previous works solved this problem faster and using fewer samples, but they did that by decoupling the messages sent by agents from their output opinion, and hence do not fit the framework of passive communication. Moreover, these works use complex recursive algorithms with refined clocks that are unlikely to be used by biological entities. In contrast, our proposed algorithm has a natural appeal as it is based on letting agents estimate the current tendency direction of the dynamics, and then adapt to the emerging trend.
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