Online Prediction via Continuous Artificial Prediction Markets

Fatemeh Jahedpari, Talal Rahwan, Sattar Hashemi, Tomasz P Michalak, Marina De Vos, Julian Padget, Wei Lee Woon

OAI: oai:purehost.bath.ac.uk:publications/b8b6e8c3-ed38-417d-928d-232be95cdc3a • DOI: https://doi.org/10.1109/MIS.2017.12

Prediction markets are well-established tools for aggregating information from diverse sources into accurate forecasts. Their success has been demonstrated in a wide range applications, including presidential campaigns, sporting events, and economic outcomes. Recently, they've been introduced to the machine learning community in the form of artificial prediction markets, in which algorithms trade contracts reflecting their levels of confidence. To date, these markets have mostly been studied in the context of offline classification, with promising results. The authors extend them to enable their use in online regression and introduce adaptive trading strategies informed by individual trading history and the ability of participants to revise their predictions by reflecting on the wisdom of the crowd, which is manifested in the collective performance of the market. The authors empirically evaluate their model using multiple datasets and show that it outperforms several well-established techniques from the literature on online regression.