Machine learning augmentation reduces prediction error in collective forecasting: development and validation across prediction markets with application to COVID events.

Background: The recent COVID-19 pandemic highlighted the challenges for traditional forecasting. Prediction markets are a promising way to generate collective forecasts and could potentially be enhanced if high-quality crowdsourced inputs were identified and preferentially weighted for likely accuracy in real-time with machine learning.

Methods: We aim to leverage human prediction markets with real-time machine weighting of likely higher accuracy trades to improve performance.