Social network-embedded prediction markets: The effects of information acquisition and communication on predictions

Information aggregation mechanisms are designed explicitly for collecting and aggregating dispersed information. An excellent example of the use of this “wisdom of crowds” is a prediction market. The purpose of our social network-embedded prediction market is to suggest that carefully designed market mechanisms can elicit and gather dispersed information that can improve our predictions. Simulation results show that our network-embedded prediction market can produce better predictions as a result of the information exchange in social networks and can outperform other non-networked prediction markets. It is shown that forecasting errors decrease with the cost of acquiring information in a network-embedded prediction market. We also develop an information system that combines the power of prediction markets with the popularity of Twitter.