Melbourne School of Psychological Sciences, The University of Melbourne, Parkville, Victoria, Australia.
Department of Economics, The University of Melbourne, Parkville, Victoria, Australia.
PLoS One. 2020 Apr 24;15(4):e0232058. doi: 10.1371/journal.pone.0232058. eCollection 2020.
A common approach to improving probabilistic forecasts is to identify and leverage the forecasts from experts in the crowd based on forecasters' performance on prior questions with known outcomes. However, such information is often unavailable to decision-makers on many forecasting problems, and thus it can be difficult to identify and leverage expertise. In the current paper, we propose a novel algorithm for aggregating probabilistic forecasts using forecasters' meta-predictions about what other forecasters will predict. We test the performance of an extremised version of our algorithm against current forecasting approaches in the literature and show that our algorithm significantly outperforms all other approaches on a large collection of 500 binary decision problems varying in five levels of difficulty. The success of our algorithm demonstrates the potential of using meta-predictions to leverage latent expertise in environments where forecasters' expertise cannot otherwise be easily identified.
一种常见的改进概率预测的方法是根据预测者在具有已知结果的先前问题上的表现，识别和利用来自众包专家的预测。然而，在许多预测问题上，决策者往往无法获得此类信息，因此很难识别和利用专业知识。在当前的论文中，我们提出了一种使用预测者关于其他预测者将预测什么的元预测来聚合概率预测的新算法。我们测试了我们算法的一个极端版本的性能，该算法在文献中的当前预测方法中表现出色，结果表明我们的算法在 500 个具有五个难度级别的二进制决策问题的大型集合上显著优于所有其他方法。我们算法的成功证明了在无法轻易识别预测者专业知识的环境中，使用元预测来利用潜在专业知识的潜力。
