Improving Forecasting Accuracy Using Bayesian Network Decomposition in Prediction Markets

We propose to improve the accuracy of prediction market forecasts by using Bayesian networks to constrain probabilities among related questions. Prediction markets are already known to increase forecast accuracy compared to single best estimates. Our own flat prediction market substantially beat a baseline linear opinion pool during the first year. One way to improve performance is by expressing relationships among the questions. Elsewhere we describe work on combinatorial markets. Here we show how to use Bayesian networks within a flat market. The general approach is to decompose a target question (hypothesis) into a set of related variables (causal factors and evidence), when the relationship among the variables is known with some confidence. Then the marginal probabilities for the variables in the Bayes net are updated using the market estimates, with the Bayes net enforcing coherence. This paper describes the overall concept, shows the results for a particular model of the potential Greek exit from the European Union, and describes the team’s future research plan.