A new kind of science
Artificial Intelligence: A Modern Approach
Artificial Intelligence: A Modern Approach
Computation in a distributed information market
Theoretical Computer Science - Game theory meets theoretical computer science
Non-myopic strategies in prediction markets
Proceedings of the 9th ACM conference on Electronic commerce
The effects of market-making on price dynamics
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
Proceedings of the 11th ACM conference on Electronic commerce
A practical liquidity-sensitive automated market maker
Proceedings of the 11th ACM conference on Electronic commerce
A multi-agent system for analyzing the effect of information on prediction markets
International Journal of Intelligent Systems
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Prediction markets have been shown to be a useful tool in forecasting the outcome of future events by aggregating public opinion about the events' outcome. Previous research on prediction markets has mostly analyzed the prediction markets by building complex analytical models. In this paper, we posit that simpler yet powerful Boolean rules can be used to adequately describe the operations of a prediction market. We have used a multi-agent based prediction market where Boolean network based rules are used to capture the evolution of the beliefs of the market's participants, as well as to aggregate the prices in the market. We show that despite the simplification of the traders' beliefs in the prediction market into Boolean states, the aggregated market price calculated using our BN model is strongly correlated with the price calculated by a commonly used aggregation strategy in existing prediction markets called the Logarithmic Market Scoring Rule (LMSR). We also empirically show that our Boolean network-based prediction market can stabilize market prices under the presence of untruthful belief revelation by the traders.