Challenges in Collective Intelligence: A Survey

Collective Intelligence (CI) has been gaining significant attention as an
effective method for decision-making and forecasting. Prediction Markets
(PMs), as a subset of CI, aim to aggregate participants' diverse opinions and
knowledge to produce more accurate predictions than any individual could make
alone. The unique market-based mechanism of PMs incentivizes participants to
reveal their information truthfully, leading to a collectively superior
prediction. However, CI and PMs have challenges, including manipulation,
fallacies, and group polarization. This paper provides an overview of the
challenges facing CI and PMs as tools for collective knowledge aggregation and
examines the role of machine learning (ML) models as tools for amplification
and hybridization in the future development of CI. Furthermore, the importance
of continued research in this field is emphasized.