通过葡萄藤学习：噪音的影响以及社交网络的广度和深度
Learning through the Grapevine: The Impact of Noise and the Breadth and Depth of Social Networks
摘要：
我们研究了当信息在人与人之间嘈杂地传递时，人们的学习效果如何;我们研究了通信平台如何在不审查或事实核查信息的情况下改善学习。我们将学习分析为社交网络深度（信息被中继的次数）和广度（访问的中继链数量）的函数。噪音随着深度的增加而增加，所以学习需要更大的广度。在突变（故意或随机）和信息传输失败的存在下，我们表征了接收器完全学习的宽度的尖锐阈值，低于该阈值，他们什么也学不到。当突变率存在不确定性时，优化学习需要限制深度，或者如果不可能，通过限制某人可以转发消息的人数来限制广度。限制宽度减少了接收到的消息的数量，但也减少了进一步源自接收器的部分，因此可以增加信噪比。最后，我们扩展了我们的模型来研究从消息生存中学习：例如，人们更倾向于传递一个结论而不是另一个结论的信息。我们发现，随着深度的增长，所有的学习要么来自接收到的消息的总数，要么来自接收到的消息的内容，但学习者不需要同时关注这两个。
英文摘要：
We examine how well people learn when information is noisily relayed from person to person; and we study how communication platforms can improve learning without censoring or fact-checking messages. We analyze learning as a function of social network depth (how many times information is relayed) and breadth (the number of relay chains accessed). Noise builds up as depth increases, so learning requires greater breadth. In the presence of mutations (deliberate or random) and transmission failures of messages, we characterize sharp thresholds for breadths above which receivers learn fully and below which they learn nothing. When there is uncertainty about mutation rates, optimizing learning requires either capping depth, or if that is not possible, limiting breadth by capping the number of people to whom someone can forward a message. Limiting breadth cuts the number of messages received but also decreases the fraction originating further from the receiver, and so can increase the signal to noise ratio. Finally, we extend our model to study learning from message survival: e.g., people are more likely to pass messages with one conclusion than another. We find that as depth grows, all learning comes from either the total number of messages received or from the content of received messages, but the learner does not need to pay attention to both.
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。
