Combinatorial Information Market Design
Information Systems Frontiers
Betting boolean-style: a framework for trading in securities based on logical formulas
Proceedings of the 4th ACM conference on Electronic commerce
Eliciting properties of probability distributions
Proceedings of the 9th ACM conference on Electronic commerce
Complexity of combinatorial market makers
Proceedings of the 9th ACM conference on Electronic commerce
Zero-intelligence agents in prediction markets
Proceedings of the 7th international joint conference on Autonomous agents and multiagent systems - Volume 2
A unified framework for dynamic pari-mutuel information market design
Proceedings of the 10th ACM conference on Electronic commerce
A practical liquidity-sensitive automated market maker
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
An axiomatic characterization of continuous-outcome market makers
WINE'10 Proceedings of the 6th international conference on Internet and network economics
Liquidity-sensitive automated market makers via homogeneous risk measures
WINE'11 Proceedings of the 7th international conference on Internet and Network Economics
Proceedings of the 13th ACM Conference on Electronic Commerce
Proceedings of the 13th ACM Conference on Electronic Commerce
A multi-agent prediction market based on partially observable stochastic game
Proceedings of the 13th International Conference on Electronic Commerce
A Practical Liquidity-Sensitive Automated Market Maker
ACM Transactions on Economics and Computation
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We designed and built the Gates Hillman Prediction Market (GHPM) to predict the opening day of the Gates and Hillman Centers, the new computer science buildings at Carnegie Mellon University. The market ran for almost a year and attracted 169 active traders who placed almost 40,000 bets with an automated market maker. Ranging over 365 possible opening days, the market's event partition size is the largest ever elicited in any prediction market by an order of magnitude. A market of this size required new advances, including a novel span-based elicitation interface. The results of the GHPM are important for two reasons. First, we uncovered two flaws of current automated market makers: spikiness and liquidity-insensitivity, and we develop the mathematical underpinnings of these flaws. Second, the market provides a valuable corpus of identity-linked trades. We use this data set to explore whether the market reacted to or anticipated official communications, how self-reported trader confidence had little relation to actual performance, and how trade frequencies suggest a power law distribution. Most significantly, the data enabled us to evaluate two competing hypotheses about how markets aggregate information, the Marginal Trader Hypothesis and the Hayek Hypothesis; the data strongly support the former.