Decision makers who receive many signals are subject to imperfect recall. This is especially important when learning from feeds that aggregate messages from many senders on social media platforms. In this paper, we study a stylized model of learning from feeds and highlight the inefficiencies that arise due to imperfect recall. In our model, failure to recall a specific message comes from the accumulation of messages which creates interference. We characterize the influence of each sender according to the rate at which she sends messages and to the strength of interference. Our analysis indicates that imperfect recall not only leads to double-counting and extreme opinions in finite populations, but also impedes the ability of the receiver to learn the true state as the population of the senders increases. We estimate the strength of interference in an online experiment where participants are exposed to (non-informative) repeated messages and they need to estimate the opinion of others. Results show that interference plays a significant role and is weaker among participants who disagree with each other. Our work has implication for the diffusion of information in networks, especially when it is false because it is shared and repeated more than true information.
翻译:接收到许多信号的决策者会被不完全的回忆。当从社交媒体平台上从许多发送者汇总的信息中学习信息时,这一点特别重要。在本文中,我们研究了一种从反馈中学习的典型模式,并突出强调了由于不完善的回忆而产生的效率低下。在我们的模型中,不回顾某一特定信息来自造成干扰的信息的积累。我们根据每个发送者发送信息的速度和干扰的力量来描述每个发送者的影响。我们的分析表明,不准确的回忆不仅导致在有限人群中进行重复计算和极端观点,而且还妨碍接收者随着发送者人口增加而了解真实状况的能力。我们估计了参与者在网上实验中受到干扰的程度,因为参与者会接触(非信息性)反复的信息,他们需要估计其他人的意见。结果显示,干扰起着重要作用,而且对彼此不相容的参与者而言,我们的工作对网络信息传播有影响,特别是由于共享和重复的信息多于真实信息,我们的工作对网络信息传播有影响。