  We use Monte Carlo techniques to simulate an organized prediction competition
between a group of a scientific experts acting under the influence of a
``self-governing'' prediction reward algorithm. Our aim is to illustrate the
advantages of a specific type of reward distribution rule that is designed to
address some of the limitations of traditional forecast scoring rules. The
primary extension of this algorithm as compared with standard forecast scoring
is that it incorporates measures of both group consensus and question relevance
directly into the reward distribution algorithm. Our model of the prediction
competition includes parameters that control both the level of bias from prior
beliefs and the influence of the reward incentive. The Monte Carlo simulations
demonstrate that, within the simplifying assumptions of the the model, experts
collectively approach belief in objectively true facts, so long as reward
influence is high and the bias stays below a critical threshold. The purpose of
this work is to motivate further research into prediction reward algorithms
that combine standard forecasting measures with factors like bias and
consensus.
