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大数之爱:消费者选择中的偏好偏差。

The Love of Large Numbers: A Popularity Bias in Consumer Choice.

机构信息

1 Department of Psychology, Stanford University.

2 Department of Psychological and Brain Sciences, Indiana University Bloomington.

出版信息

Psychol Sci. 2017 Oct;28(10):1432-1442. doi: 10.1177/0956797617711291. Epub 2017 Aug 21.

Abstract

Social learning-the ability to learn from observing the decisions of other people and the outcomes of those decisions-is fundamental to human evolutionary and cultural success. The Internet now provides social evidence on an unprecedented scale. However, properly utilizing this evidence requires a capacity for statistical inference. We examined how people's interpretation of online review scores is influenced by the numbers of reviews-a potential indicator both of an item's popularity and of the precision of the average review score. Our task was designed to pit statistical information against social information. We modeled the behavior of an "intuitive statistician" using empirical prior information from millions of reviews posted on Amazon.com and then compared the model's predictions with the behavior of experimental participants. Under certain conditions, people preferred a product with more reviews to one with fewer reviews even though the statistical model indicated that the latter was likely to be of higher quality than the former. Overall, participants' judgments suggested that they failed to make meaningful statistical inferences.

摘要

社会学习——即通过观察他人的决策以及这些决策的结果来学习的能力——是人类在进化和文化上取得成功的基础。互联网现在提供了前所未有的规模的社会证据。然而,要恰当地利用这些证据,需要具备统计推断的能力。我们研究了人们对在线评论分数的解释是如何受到评论数量的影响的——这是一个物品受欢迎程度和平均评论分数精度的潜在指标。我们的任务旨在让统计信息与社会信息相互竞争。我们使用来自亚马逊网站上发布的数百万条评论的经验先验信息来模拟“直觉统计学家”的行为,然后将模型的预测与实验参与者的行为进行比较。在某些条件下,人们更喜欢有更多评论的产品,而不是评论较少的产品,尽管统计模型表明后者的质量可能高于前者。总的来说,参与者的判断表明他们未能进行有意义的统计推断。

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