群众什么时候是明智的？
When is a crowd wise?
摘要：
大量的研究和轶事证明了“群众的智慧”，即一个群体的综合判断令人惊讶的准确性。然而，人们对群体智慧的普遍性知之甚少。例如，即使群体成员有系统性的判断偏见，群体还是明智的吗？或者在成员做出判断之前，群体能够相互影响吗？如果是这样的话，在某些情况下，我们可以预期群体的准确性低于熟练的个人吗？我们提供了一个精确但一般的群体智慧的定义：一个群体是明智的，如果一个线性聚合，例如一个平均值，其成员的判断是更接近目标值比随机，但不一定是均匀的，抽样的群体成员。基于这一定义，我们开发了一个理论框架，用于先验地研究人群何时以及在何种程度上是明智的。我们系统地研究在这个框架内的人群智慧的边界条件，并确定条件下，人群的准确性优势最大化。我们的研究结果表明，群体智慧是非常强大的：即使判断是有偏见的和相关的，一个人将需要几乎确定性地选择只有一个高度熟练的法官之前，个人的判断可以预期是更准确的比一个简单的平均人群。我们的研究结果还提供了一个准确的理由背后需要的多样性的判断组成员。相反，民间解释的群体智慧认为，判断应该是理想的独立，使错误抵消，我们发现，群体智慧最大化时，系统的判断尽可能不同。我们重新分析了两项已发表的研究数据，证实了我们的理论结果。
英文摘要：
Numerous studies and anecdotes demonstrate the "wisdom of the crowd," the surprising accuracy of a group's aggregated judgments. Less is known, however, about the generality of crowd wisdom. For example, are crowds wise even if their members have systematic judgmental biases, or can influence each other before members render their judgments? If so, are there situations in which we can expect a crowd to be less accurate than skilled individuals? We provide a precise but general definition of crowd wisdom: A crowd is wise if a linear aggregate, for example a mean, of its members' judgments is closer to the target value than a randomly, but not necessarily uniformly, sampled member of the crowd. Building on this definition, we develop a theoretical framework for examining, a priori, when and to what degree a crowd will be wise. We systematically investigate the boundary conditions for crowd wisdom within this framework and determine conditions under which the accuracy advantage for crowds is maximized. Our results demonstrate that crowd wisdom is highly robust: Even if judgments are biased and correlated, one would need to nearly deterministically select only a highly skilled judge before an individual's judgment could be expected to be more accurate than a simple averaging of the crowd. Our results also provide an accuracy rationale behind the need for diversity of judgments among group members. Contrary to folk explanations of crowd wisdom which hold that judgments should ideally be independent so that errors cancel out, we find that crowd wisdom is maximized when judgments systematically differ as much as possible. We re-analyze data from two published studies that confirm our theoretical results.
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