Extracting the Wisdom of Crowds When Information Is Shared
Extracting the Wisdom of Crowds When Information Is Shared
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DOI：
10.2139/ssrn.2636376
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发表时间：
2015-09
期刊：
影响因子：
--
通讯作者：
Asa B. Palley;Jack B. Soll
中科院分区：
文献类型：
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作者：
Asa B. Palley;Jack B. Soll
使用人群的智慧 - 结合许多个人预测以获得总估计值 - 可以是提高预测准确性的有效技术。但是，相关的预测错误极大地限制了人群恢复真理的智慧的能力。在实践中，由于信息是共享的，因此通常会出现这种依赖性。为了解决这个问题，我们提出了一个启发程序，要求每个受访者既提供自己的最佳预测，又要猜测所有其他受访者将提供的平均预测。我们开发了一种称为枢纽的聚合方法，该方法将单个预测分离为共享和私人信息，然后以最佳方式重新组合这些结果。在几项研究中，我们研究了该方法并检查了骨料预测的准确性。总体而言，经验数据表明，旋转方法提供了有效的预测聚合程序，可以显着胜过简单的人群平均值。
Using the wisdom of crowds -- combining many individual forecasts to obtain an aggregate estimate -- can be an effective technique for improving forecast accuracy. However, correlated forecast errors greatly limit the ability of the wisdom of crowds to recover the truth. In practice, this dependence often emerges because information is shared. To address this problem, we propose an elicitation procedure in which each respondent is asked to provide both their own best forecast and a guess of the average forecast that will be given by all other respondents. We develop an aggregation method, called pivoting, which separates individual forecasts into shared and private information and then recombines these results in the optimal manner. In several studies, we investigate the method and examine the accuracy of the aggregate forecasts. Overall, the empirical data suggest that the pivoting method provides an effective forecast aggregation procedure that can significantly outperform the simple crowd average.