社交意见形成与通信趋势下的决策
Social Opinion Formation and Decision Making Under Communication Trends
AI总结：
本研究探讨通信趋势下社交网络的部分信息共享学习，证明即使不讨论真实假设也能以相当速度学习，并揭示基于自身信念的先验可能导致意见集群阻碍完全置信学习。
摘要：
本文研究了在部分且随机信息共享条件下社交网络上的学习过程。在传统社交学习模型中，智能体在试图推断自然真实状态时，会相互交换完整的信念信息。我们研究了智能体仅共享关于一个假设（即热门话题）的信息的情况，该话题可能在每次迭代时随机变化。我们证明，即使智能体不讨论真实假设，也能以与传统社交学习相当的速度学习到真实假设。我们还表明，使用自身信念作为先验来估计邻居未传输的信念，可能会产生意见集群，从而阻碍完全置信的学习。当与真实对应的单一假设在所有时间被独家交换时，这种现象会发生。然而，这种做法避免了在任何信息交换程序下对真实假设的完全拒绝——如果先验是均匀的，这种情况可能发生。
英文摘要：
This work studies the learning process over social networks under partial and random information sharing. In traditional social learning models, agents exchange full belief information with each other while trying to infer the true state of nature. We study the case where agents share information about only one hypothesis, namely, the trending topic, which can be randomly changing at every iteration. We show that agents can learn the true hypothesis even if they do not discuss it, at rates comparable to traditional social learning. We also show that using one's own belief as a prior for estimating the neighbors' non-transmitted beliefs might create opinion clusters that prevent learning with full confidence. This phenomenon occurs when a single hypothesis corresponding to the truth is exchanged exclusively during all times. Such a practice, however, avoids the complete rejection of the truth under any information exchange procedure -- something that could happen if priors were uniform.
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。
