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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References
Ben-Gal I (2007) “Bayesian networks” (PDF). In: Ruggeri F, Kennett RS, Faltin FW (eds) Encyclopedia of statistics in quality and reliability. Wiley, Chichester
Brier GW (1950) Verification of forecasts expressed in terms of probability. Mon Weather Rev 75:1–3
Cameron WB (1963) Informal sociology: a casual introduction to sociological thinking. Random House, New York
Hanson R (2003) Combinatorial information market design. Inf Syst Front 5(1):107–119
Hanson R (2007) Logarithmic market scoring rules for modular combinatorial information aggregation. J Predict Mark 1(1):3–15
Holland JH, Miller JH (1991) Artificial adaptive agents in economic theory. American Economic Review Papers and Proceedings, 81:365–370
Jeffreys H (1946) An invariant form for the prior probability in estimation problems. Proc R Soc Lond Ser A Math Phys Sci 186(1007):453–461
Lyon A, Pacuit E (2013) The Wisdom of crowds: methods of human judgement aggregation. In: Michelucci P (ed) Handbook of human computation. Springer
Matsumoto S, Carvalho RN, Ladeira M, da Costa PCG, Santos LL, Silva D, Onishi M, Machado E, Cai K (2011) UnBBayes: a java framework for probabilistic models in AI. In: Java in academia and research. iConcept Press.
Pennock D, Xia L (2011) Price updating in combinatorial prediction markets with Bayesian net- works. In Proceedings of the Twenty-Seventh Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-11), pages 581–588, Corvallis, Oregon. AUAI Press
Sun W, Hanson R, Laskey KB, Twardy CR (2012) Probability and asset updating using bayesian networks for combinatorial prediction markets. In: Proceedings of the 28th conference on uncertainty in artificial intelligence (UAI-2012), Catalina
Surowiecki J (2005) The wisdom of crowds. Anchor Books, New York
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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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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