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article

Graphical model market maker for combinatorial prediction markets

Authors:

Kathryn Blackmond Laskey,

Charles Twardy,

Shou Matsumoto,

Brandon GoldfedderAuthors Info & Claims

Journal of Artificial Intelligence Research, Volume 63, Issue 1

Pages 421 - 460

https://doi.org/10.1613/jair.1.11249

Published: 01 September 2018 Publication History

Abstract

We describe algorithms for use by prediction markets in forming a crowd consensus joint probability distribution over thousands of related events. Equivalently, we describe market mechanisms to efficiently crowdsource both structure and parameters of a Bayesian network. Prediction markets are among the most accurate methods to combine forecasts; forecasters form a consensus probability distribution by trading contingent securities. A combinatorial prediction market forms a consensus joint distribution over many related events by allowing conditional trades or trades on Boolean combinations of events. Explicitly representing the joint distribution is infeasible, but standard inference algorithms for graphical probability models render it tractable for large numbers of base events. We show how to adapt these algorithms to compute expected assets conditional on a prospective trade, and to find the conditional state where a trader has minimum assets, allowing full asset reuse. We compare the performance of three algorithms: the straightforward algorithm from the DAGGRE (Decomposition-Based Aggregation) prediction market for geopolitical events, the simple block-merge model from the SciCast market for science and technology forecasting, and a more sophisticated algorithm we developed for future markets.

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Cited By

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
https://dl.acm.org/doi/10.5555/3463952.3464011
Martin BChakraborty MKutty SCalinescu ASzpruch L(2021)Timing is moneyProceedings of the Second ACM International Conference on AI in Finance10.1145/3490354.3494406(1-9)Online publication date: 3-Nov-2021
https://dl.acm.org/doi/10.1145/3490354.3494406
Wang XPennock DDevanur NRothschild DTao BWellman MBiró PChawla SEchenique F(2021)Designing a Combinatorial Financial Options MarketProceedings of the 22nd ACM Conference on Economics and Computation10.1145/3465456.3467634(864-883)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3465456.3467634

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Published In

cover image Journal of Artificial Intelligence Research

Journal of Artificial Intelligence Research Volume 63, Issue 1

September 2018

1012 pages

ISSN:1076-9757

Editor:
Francesca Rossi

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AI Access Foundation

El Segundo, CA, United States

Publication History

Published: 01 September 2018

Published in JAIR Volume 63, Issue 1

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Cited By

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
https://dl.acm.org/doi/10.5555/3463952.3464011
Martin BChakraborty MKutty SCalinescu ASzpruch L(2021)Timing is moneyProceedings of the Second ACM International Conference on AI in Finance10.1145/3490354.3494406(1-9)Online publication date: 3-Nov-2021
https://dl.acm.org/doi/10.1145/3490354.3494406
Wang XPennock DDevanur NRothschild DTao BWellman MBiró PChawla SEchenique F(2021)Designing a Combinatorial Financial Options MarketProceedings of the 22nd ACM Conference on Economics and Computation10.1145/3465456.3467634(864-883)Online publication date: 18-Jul-2021
https://dl.acm.org/doi/10.1145/3465456.3467634

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