论文标题
社交网络中的确认偏见
Confirmation Bias in Social Networks
论文作者
论文摘要
在这项研究中，我提出了一种理论社会学习模型，以调查确认偏见如何影响通过社交网络交换信息时的意见。因此，除了与朋友交换意见外，代理商还观察到了一系列潜在模棱两可的信号，并根据包括确认偏见的规则来解释它。首先，这项研究表明，无论人们或网络社会的歧义程度如何，都只能形成两种类型的观点，并且两者都有偏见。但是，一种意见类型的偏见不如另一种偏见，具体取决于世界的状态。两种偏差的大小都取决于状态的歧义水平和相对幅度和确认偏见。因此，即使人们公正地解释歧义，也无法实现长期学习。最后，从分析上证实当人们连接并具有不同的先验时，偏见的共识的出现概率是困难的。因此，我使用仿真来分析其决定因素，并发现了三个主要结果：i）一些网络拓扑更有利于共识效率，ii）某种程度的党派化提高了共识效率，即使在确认偏见和iii下）（即，当Partisans同意与对方的游击队的交流意见时）可能会抑制某些案例的效率。
In this study, I present a theoretical social learning model to investigate how confirmation bias affects opinions when agents exchange information over a social network. Hence, besides exchanging opinions with friends, agents observe a public sequence of potentially ambiguous signals and interpret it according to a rule that includes confirmation bias. First, this study shows that regardless of level of ambiguity both for people or networked society, only two types of opinions can be formed, and both are biased. However, one opinion type is less biased than the other depending on the state of the world. The size of both biases depends on the ambiguity level and relative magnitude of the state and confirmation biases. Hence, long-run learning is not attained even when people impartially interpret ambiguity. Finally, analytically confirming the probability of emergence of the less-biased consensus when people are connected and have different priors is difficult. Hence, I used simulations to analyze its determinants and found three main results: i) some network topologies are more conducive to consensus efficiency, ii) some degree of partisanship enhances consensus efficiency even under confirmation bias and iii) open-mindedness (i.e. when partisans agree to exchange opinions with opposing partisans) might inhibit efficiency in some cases.
