社交网络中非贝叶斯异步学习达成共识
Reaching Consensus via non-Bayesian Asynchronous Learning in Social Networks
摘要：
我们研究了在线社交网络中信息聚合的结果。我们的主要结果是，具有某些现实结构特性的网络避免了信息级联，并使人口能够有效地聚合信息。在我们的模型中，网络中的每个人都对产品或想法持有私人的独立意见，偏向于地面真相。个体异步地声明他们的观点，可以观察他们邻居的声明观点，并且可以随着时间的推移自由地更新他们的声明。假设个体与其邻居的大多数报告一致，我们要问的是，群体最终是否会就基本事实达成共识。我们表明，答案取决于网络结构：存在网络的共识是不可能的，或声明收敛于不正确的意见与积极的概率。另一方面，我们证明了对于稀疏和可扩展的网络，人口将以高概率收敛到正确的观点。
英文摘要：
We study the outcomes of information aggregation in online social networks. Our main result is that networks with certain realistic structural properties avoid information cascades and enable a population to effectively aggregate information. In our model, each individual in a network holds a private, independent opinion about a product or idea, biased toward a ground truth. Individuals declare their opinions asynchronously, can observe the stated opinions of their neighbors, and are free to update their declarations over time. Supposing that individuals conform with the majority report of their neighbors, we ask whether the population will eventually arrive at consensus on the ground truth. We show that the answer depends on the network structure: there exist networks for which consensus is unlikely, or for which declarations converge on the incorrect opinion with positive probability. On the other hand, we prove that for networks that are sparse and expansive, the population will converge to the correct opinion with high probability.
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。
