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Handbook of Human Computation pp 367–376Cite as

Adaptive Agents in Combinatorial Prediction Markets

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Abstract

DAGGRE is a research project that aims to improve the forecasting methods of world events using the first combinatorial prediction market in the world. The DAGGRE prediction market aggregates estimates from hundreds of participants to forecast the outcomes considering potential links between events. This market also aggregates estimates from a series of autotraders (algorithms) that trade live alongside of human users. The combinatorial prediction market allows Bayes Net models to be implemented and tested against the aggregate information extracted through crowdsourcing. On several of these Bayes Nets, we implemented a Bayes Net autotrader and this research shows the forecasting results of the Bayes Net autotrader in a combinatorial prediction market.

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Acknowledgements

The author is very grateful for the software and coding support provided by Rob Alexander and Dan Maxwell from KaDSci and Shou Matsumoto and Chris Karvetski from George Mason University. Supported by the Intelligence Advanced Research Projects Activity (IARPA) via Department of Interior National Business Center contract number D11PC20062. The U.S. Government is authorized to reproduce and distribute reprints for Governmental purposes notwithstanding any copyright annotation thereon. Disclaimer: The views and conclusions contained herein are those of the authors and should not be interpreted as necessarily representing the official policies or endorsements, either expressed or implied, of IARPA, DoI/NBC, or the U.S. Government.

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Authors and Affiliations

  1. C4I Center of Excellence, Volgenau School of Engineering, George Mason University, Fairfax, VA, USA

    Anamaria Berea

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  1. Anamaria Berea
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Correspondence to Anamaria Berea .

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  1. ThinkSplash, LLC, Fairfax, Virginia, USA

    Pietro Michelucci

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Berea, A. (2013). Adaptive Agents in Combinatorial Prediction Markets. In: Michelucci, P. (eds) Handbook of Human Computation. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-8806-4_29

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  • DOI: https://doi.org/10.1007/978-1-4614-8806-4_29

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  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4614-8805-7

  • Online ISBN: 978-1-4614-8806-4

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