research-article

Automated market-making in the large: the gates hillman prediction market

Authors:

Abraham Othman,

Tuomas SandholmAuthors Info & Claims

EC '10: Proceedings of the 11th ACM conference on Electronic commerce

Pages 367 - 376

https://doi.org/10.1145/1807342.1807401

Published: 07 June 2010 Publication History

Abstract

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.

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Automated market-making in the large: the gates hillman prediction market

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Published In

cover image ACM Conferences

EC '10: Proceedings of the 11th ACM conference on Electronic commerce

June 2010

400 pages

ISBN:9781605588223

DOI:10.1145/1807342

General Chair:
David C. Parkes
Harvard University, USA
,
Program Chairs:
Chrysanthos Dellarocas
Boston University, USA
,
Moshe Tennenholtz
Technion and Microsoft Israel R&D Center

Copyright © 2010 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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SIGecom: Special Interest Group on Economics and Computation

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 07 June 2010

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EC '10

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SIGecom

EC '10: ACM Conference on Electronic Commerce

June 7 - 11, 2010

Massachusetts, Cambridge, USA

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Overall Acceptance Rate 664 of 2,389 submissions, 28%

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https://doi.org/10.1109/ACCESS.2024.3372402
Mussoi Almeida LMüller FPerlin M(2024)Risk Forecasting Comparisons in Decentralized Finance: An Approach in Constant Product Market MakersComputational Economics10.1007/s10614-024-10585-6Online publication date: 1-Apr-2024
https://doi.org/10.1007/s10614-024-10585-6
Amini HBichuch MFeinstein Z(2023)Decentralized Prediction Markets and Sports BooksSSRN Electronic Journal10.2139/ssrn.4513239Online publication date: 2023
https://doi.org/10.2139/ssrn.4513239
Aspembitova ABentley M(2022)Oracles in Decentralized Finance: Attack Costs, Profits and Mitigation MeasuresEntropy10.3390/e2501006025:1(60)Online publication date: 28-Dec-2022
https://doi.org/10.3390/e25010060
Dudík MWang XPennock DRothschild DDignum FLomuscio AEndriss UNowé A(2021)Log-time Prediction Markets for Interval SecuritiesProceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems10.5555/3463952.3464011(465-473)Online publication date: 3-May-2021
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Wah ELahaie SPennock D(2016)An empirical game-theoretic analysis of price discovery in prediction marketsProceedings of the Twenty-Fifth International Joint Conference on Artificial Intelligence10.5555/3060621.3060693(510-516)Online publication date: 9-Jul-2016
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Jumadinova JDasgupta P(2015)Automated Pricing in a Multiagent Prediction Market Using a Partially Observable Stochastic GameACM Transactions on Intelligent Systems and Technology10.1145/27004886:4(1-25)Online publication date: 29-Jul-2015
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