California State University, Northridge, Northridge, USA.
California State University San Marcos, San Marcos, USA.
Cogn Res Princ Implic. 2020 Nov 11;5(1):57. doi: 10.1186/s41235-020-00256-z.
The "surprisingly popular" method (SP) of aggregating individual judgments has shown promise in overcoming a weakness of other crowdsourcing methods-situations in which the majority is incorrect. This method relies on participants' estimates of other participants' judgments; when an option is chosen more often than the average metacognitive judgments of that option, it is "surprisingly popular" and is selected by the method. Although SP has been shown to improve group decision making about factual propositions (e.g., state capitals), its application to future outcomes has been limited. In three preregistered studies, we compared SP to other methods of aggregating individual predictions about future events. Study 1 examined predictions of football games, Study 2 examined predictions of the 2018 US midterm elections, and Study 3 examined predictions of basketball games. When applied to judgments made by objectively assessed experts, SP performed slightly better than other aggregation methods. Although there is still more to learn about the conditions under which SP is effective, it shows promise as a means of crowdsourcing predictions of future outcomes.
“出乎意料地受欢迎”的聚合个体判断的方法（简称 SP 方法），在克服其他众包方法的一个弱点方面表现出了潜力——即在多数人判断错误的情况下。该方法依赖于参与者对其他参与者判断的估计；当一个选项比该选项的平均元认知判断更频繁地被选择时，它就是“出乎意料地受欢迎”的，并被该方法选中。尽管 SP 已被证明可以提高对事实命题（例如州首府）的群体决策，但它在未来结果方面的应用受到限制。在三项预先注册的研究中，我们比较了 SP 与其他聚合个体对未来事件预测的方法。研究 1 检验了对足球比赛的预测，研究 2 检验了对 2018 年美国中期选举的预测，研究 3 检验了对篮球比赛的预测。当应用于由客观评估的专家做出的判断时，SP 表现略优于其他聚合方法。尽管关于 SP 有效的条件还有更多需要了解，但它作为一种众包预测未来结果的方法显示出了潜力。
