What systems should we use to elicit and aggregate judgmental forecasts? Who should be asked to make such forecasts? We address these questions by assessing two widely used crowd prediction systems: prediction markets and prediction polls. Our main test compares a prediction market against team-based prediction polls, using data from a large, multi-year forecasting competition. Each of these two systems uses inputs from either a large, sub-elite or a small, elite crowd. We find that small, elite crowds outperform larger ones, whereas the two systems are statistically tied. In addition to this main research question, we examine two complementary questions. First, we compare two market structures—continuous double auction (CDA) markets and logarithmic market scoring rule (LMSR) markets—and find that the LMSR market produces more accurate forecasts than the CDA market, especially on low-activity questions. Second, given the importance of elite forecasters, we compare the talent-spotting properties of the two systems and find that markets and polls are equally effective at identifying elite forecasters. Overall, the performance benefits of “superforecasting” hold across systems. Managers should move towards identifying and deploying small, select crowds to maximize forecasting performance.

中文翻译：

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人群预测系统：市场、民意调查和精英预测者

我们应该使用什么系统来引发和汇总判断性预测？应该要求谁做出这样的预测？我们通过评估两种广泛使用的人群预测系统来解决这些问题：预测市场和预测民意调查。我们的主要测试使用来自大型、多年预测竞赛的数据，将预测市场与基于团队的预测民意调查进行比较。这两个系统都使用来自大量次精英或少量精英人群的输入。我们发现，小规模的精英群体的表现优于大群体，而这两个系统在统计上是相关的。除了这个主要研究问题之外，我们还研究了两个补充问题。首先，我们比较两种市场结构——连续双重拍卖（CDA）市场和对数市场评分规则（LMSR）市场——并发现 LMSR 市场比 CDA 市场产生更准确的预测，尤其是在低活跃度问题上。其次，考虑到精英预测员的重要性，我们比较了两个系统的人才发现特性，发现市场和民意调查在识别精英预测员方面同样有效。总体而言，“超级预测”的性能优势在整个系统中都存在。管理者应该努力识别和部署小规模、精选的人群，以最大限度地提高预测绩效。