平台上的资讯泛滥及其影响

Gad Allon, K. Drakopoulos, V. Manshadi
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引用次数: 10

摘要

在本文中,我们研究了一个信息消费模型,在这个模型中,消费者依次与一个平台进行交互,该平台提供了一个关于世界(事实)的潜在状态的信号(帖子)菜单。每次,由于无法消费所有帖子,消费者只能筛选帖子,并从提供的菜单中只选择(并消费)一个帖子。我们表明,在这些帖子的准确性存在不确定性的情况下,随着帖子数量的增加,不良影响,如缓慢的学习和两极分化出现。具体来说,我们确定,在这种情况下,偏见是消费者筛选过程的结果。也就是说,消费者在选择能够减少他们对世界状态的不确定性的帖子时,会选择消费最接近他们自己信念的帖子。我们研究了信念的进化,我们发现这种筛选偏见减缓了学习过程,学习速度随着菜单的大小而下降。此外,我们发现即使在社会信念分布不是先天极化的情况下,社会也会在漫长的学习过程中出现两极分化。
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Information Inundation on Platforms and Implications
In this paper we study a model of information consumption where consumers sequentially interact with a platform that offers a menu of signals (posts) about an underlying state of the world (fact). At each time, incapable of consuming all posts, consumers screen the posts and only select (and consume) one from the offered menu. We show that in the presence of uncertainty about the accuracy of these posts, and as the number of posts increases, adverse effects such as slow learning and polarization arise. Specifically, we establish that, in this setting, bias emerges as a consequence of the consumer's screening process. Namely, consumers, in their quest to choose the post that reduces their uncertainty about the state of the world, choose to consume the post that is closest to their own beliefs. We study the evolution of beliefs and we show that such a screening bias slows down the learning process, and the speed of learning decreases with the menu size. Further, we show that the society becomes polarized during the prolonged learning process even in situations where the society's belief distribution was not a priori polarized.
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