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