Prediction markets: an information aggregation perspective to the forecasting problem

Lately, traditional forecasting methods have been depicted as inferior to newer ones which are attempting to simulate the human decision making process. However, this goal might even be impossible to achieve. This paper introduces an inverse approach to the forecasting problem. The typical process of attempting to subtractively model the expert's knowledge and cognitive function and then perform the forecast is replaced by the dynamic extraction of pure experts' forecasts and the subsequent summing up of the information. The design and benefits of a business game that serves as an information aggregation tool producing valuable predictors is hereby supported.