机器学习增强可以减少集体预测中的预测错误:在整个预测市场的开发和验证,并应用于COVID事件
Alexander Gruen1, Karl R Mattingly2, Ellen Morwitch1
1The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia.
EBioMedicine
|September 14, 2023
概括
机器学习通过实时权衡准确的人类交易来增强预测市场. 这种混合方法提高了集体预测的准确性,为新出现的风险提供了更好的预测.
科学领域:
- 计算社会科学 计算社会科学
- 机器学习应用 机器学习应用
- 预测方法 预测方法
背景情况:
- 随着COVID-19的流行,传统预测方法的局限性被暴露出来.
- 预测市场为集体预测提供了一个有希望的途径.
- 增强预测市场需要识别和权衡高质量的众包输入,使用机器学习.
研究的目的:
- 通过整合实时机器学习来改善预测市场的表现.
- 利用来自预测市场的众包数据来提高预测准确度.
主要方法:
- 使用了阿尔马尼斯预测市场平台 (n=1822) 和下一代社会科学 (NGS2) 平台 (n=103).
- 开发了一个43个特征模型,以根据布里尔准确度来预测准确的预测者.
- 应用实时机器分类来根据预测的准确性对人类交易进行权衡.
主要成果:
- 该模型准确地识别了顶级预测者,在两个样本外数据集 (p<1x10−9) 中进行了验证.
- 与单独的市场预测相比,准确度评分加权的预测显示了显著的AUC增长 (13.2%和13.8%),特别是当预测不同时.
- 混合模型在72.7%的时间内正确预测了COVID-19事件,而不一致的市场模型则为27.3% (p=0.007).
结论:
- 基于预测准确度的实时机器分类和人力交易权重增强了集体预测.
- 这种方法可以带来更好的预测和应对新出现的风险.
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