  Despite high-profile successes in the field of Artificial Intelligence,
machine-driven technologies still suffer important limitations, particularly
for complex tasks where creativity, planning, common sense, intuition, or
learning from limited data is required. These limitations motivate effective
methods for human-machine collaboration. Our work makes two primary
contributions. We thoroughly experiment with an artificial prediction market
model to understand the effects of market parameters on model performance for
benchmark classification tasks. We then demonstrate, through simulation, the
impact of exogenous agents in the market, where these exogenous agents
represent primitive human behaviors. This work lays the foundation for a novel
set of hybrid human-AI machine learning algorithms.
