Complexity Results on Learning by Neural Nets
Machine Learning
Reasoning about knowledge
Extracting collective probabilistic forecasts from web games
Proceedings of the seventh ACM SIGKDD international conference on Knowledge discovery and data mining
Combinatorial Information Market Design
Information Systems Frontiers
Computation in a distributed information market
Proceedings of the 4th ACM conference on Electronic commerce
Information incorporation in online in-Game sports betting markets
Proceedings of the 4th ACM conference on Electronic commerce
Modeling information incorporation in markets, with application to detecting and explaining events
UAI'02 Proceedings of the Eighteenth conference on Uncertainty in artificial intelligence
Eliciting properties of probability distributions
Proceedings of the 9th ACM conference on Electronic commerce
Non-myopic strategies in prediction markets
Proceedings of the 9th ACM conference on Electronic commerce
Strategies in Dynamic Pari-Mutual Markets
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Prediction markets, mechanism design, and cooperative game theory
UAI '09 Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence
A Multi-agent Prediction Market Based on Boolean Network Evolution
WI-IAT '11 Proceedings of the 2011 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology - Volume 02
A multi-agent prediction market based on partially observable stochastic game
Proceedings of the 13th International Conference on Electronic Commerce
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According to economic theory--supported by empirical and laboratory evidence--the equilibrium price of a financial security reflects all of the information regarding the security's value. We investigate the computational process on the path toward equilibrium, where information distributed among traders is revealed step-by-step over time and incorporated into the market price. We develop a simplified model of an information market, along with trading strategies, in order to formalize the computational properties of the process. We show that securities whose payoffs cannot be expressed as weighted threshold functions of distributed input bits are not guaranteed to converge to the proper equilibrium predicted by economic theory. On the other hand, securities whose payoffs are threshold functions are guaranteed to converge, for all prior probability distributions. Moreover, these threshold securities converge in at most n rounds, where n is the number of bits of distributed information. We also prove a lower bound, showing a type of threshold security that requires at least n/2 rounds to converge in the worst case.