When eliciting people's forecasts or beliefs, you can ask for a point estimate-for example, what is the most likely state of the world?-or you can ask for an entire distribution of beliefs-for example, how likely is every possible state of the world? Eliciting belief distributions potentially yields more information, and researchers have increasingly tried to do so. In this article, we show that different elicitation methods elicit different belief distributions. We compare two popular methods used to elicit belief distributions: Distribution Builder and Sliders. In 10 preregistered studies (N = 14,553), we find that Distribution Builder elicits more accurate belief distributions than Sliders, except when true distributions are right-skewed, for which the results are mixed. This result holds when we assess accuracy (a) relative to a normative benchmark and (b) relative to participants' own beliefs. Our evidence suggests that participants approach these two methods differently: Sliders users are more likely to start with the lowest bins in the interface, which in turn leads them to put excessive mass in those bins. Our research sheds light on the process by which people construct belief distributions while offering a practical recommendation for future research: All else equal, Distribution Builder yields more accurate belief distributions. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

中文翻译：

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不同的方法会产生不同的信念分布。


当引出人们的预测或信念时，您可以要求点估计 - 例如，世界最可能的状态是什么？ - 或者您可以要求信念的整个分布 - 例如，每种可能状态的可能性有多大世界？得出信念分布可能会产生更多信息，研究人员也越来越多地尝试这样做。在本文中，我们表明不同的启发方法会引发不同的信念分布。我们比较了两种用于得出信念分布的流行方法：分布生成器和滑块。在 10 项预先注册的研究 (N = 14,553) 中，我们发现分布生成器比滑块能得出更准确的置信分布，除非真实分布是右偏的，此时结果是混合的。当我们评估（a）相对于规范基准和（b）相对于参与者自己的信念的准确性时，这一结果成立。我们的证据表明，参与者对这两种方法的处理方式不同：滑块用户更有可能从界面中最低的垃圾箱开始，这反过来又导致他们在这些垃圾箱中放置过多的质量。我们的研究揭示了人们构建信念分布的过程，同时为未来的研究提供了实用的建议：在其他条件相同的情况下，分布生成器会产生更准确的信念分布。 （PsycInfo 数据库记录 (c) 2024 APA，保留所有权利）。