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
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
An in-depth analysis of information markets with aggregate uncertainty
Electronic Commerce Research
Computation in a distributed information market
Theoretical Computer Science - Game theory meets theoretical computer science
When do markets with simple agents fail?
Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems: volume 1 - Volume 1
WINE'05 Proceedings of the First international conference on Internet and Network Economics
Rational market making with probabilistic knowledge
Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2
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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.