Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic Game

Prediction markets offer an efficient market-based mechanism to aggregate large amounts of dispersed or distributed information from different people to predict the possible outcome of future events. Recently, automated prediction markets where software trading agents perform market operations such as trading and updating beliefs on behalf of humans have been proposed. A challenging aspect in automated prediction markets is to develop suitable techniques that can be used by automated trading agents to update the price at which they should trade securities related to an event so that they can increase their profit. This problem is nontrivial, as the decision to trade and the price at which trading should occur depends on several dynamic factors, such as incoming information related to the event for which the security is being traded, the belief-update mechanism and risk attitude of the trading agent, and the trading decision and trading prices of other agents. To address this problem, we have proposed a new behavior model for trading agents based on a game-theoretic framework called partially observable stochastic game with information (POSGI). We propose a correlated equilibrium (CE)-based solution strategy for this game that allows each agent to dynamically choose an action (to buy or sell or hold) in the prediction market. We have also performed extensive simulation experiments using the data obtained from the Intrade prediction market for four different prediction markets. Our results show that our POSGI model and CE strategy produces prices that are strongly correlated with the prices of the real prediction markets. Results comparing our CE strategy with five other strategies commonly used in similar market show that our CE strategy improves price predictions and provides higher utilities to the agents compared to other existing strategies.