Automated Market-Making in the Large: The Gates Hillman Prediction Market

摘要

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.