Combinatorial Information Market Design
Information Systems Frontiers
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The Journal of Machine Learning Research
Maximizing Non-Monotone Submodular Functions
FOCS '07 Proceedings of the 48th Annual IEEE Symposium on Foundations of Computer Science
Pricing combinatorial markets for tournaments
STOC '08 Proceedings of the fortieth annual ACM symposium on Theory of computing
Complexity of combinatorial market makers
Proceedings of the 9th ACM conference on Electronic commerce
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Parimutuel Betting on Permutations
WINE '08 Proceedings of the 4th International Workshop on Internet and Network Economics
Combinatorial prediction markets for event hierarchies
Proceedings of The 8th International Conference on Autonomous Agents and Multiagent Systems - Volume 1
Betting Boolean-style: a framework for trading in securities based on logical formulas
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An optimization-based framework for automated market-making
Proceedings of the 12th ACM conference on Electronic commerce
An efficient Monte-Carlo algorithm for pricing combinatorial prediction markets for tournaments
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume One
A combinatorial prediction market for the U.S. elections
Proceedings of the fourteenth ACM conference on Electronic commerce
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We present a new automated market maker for providing liquidity across multiple logically interrelated securities. Our approach lies somewhere between the industry standard---treating related securities as independent and thus not transmitting any information from one security to another---and a full combinatorial market maker for which pricing is computationally intractable. Our market maker, based on convex optimization and constraint generation, is tractable like independent securities yet propagates some information among related securities like a combinatorial market maker, resulting in more complete information aggregation. We prove several favorable properties of our scheme and evaluate its information aggregation performance on survey data involving hundreds of thousands of complex predictions about the 2008 U.S. presidential election.