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Combinatorial Modelling and Learning with Prediction Markets

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  • Jinli Hu
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    Abstract

    Combining models in appropriate ways to achieve high performance is commonly seen in machine learning fields today. Although a large amount of combinatorial models have been created, little attention is drawn to the commons in different models and their connections. A general modelling technique is thus worth studying to understand model combination deeply and shed light on creating new models. Prediction markets show a promise of becoming such a generic, flexible combinatorial model. By reviewing on several popular combinatorial models and prediction market models, this paper aims to show how the market models can generalise different combinatorial stuctures and how they implement these popular combinatorial models in specific conditions. Besides, we will see among different market models, Storkey's \emph{Machine Learning Markets} provide more fundamental, generic modelling mechanisms than the others, and it has a significant appeal for both theoretical study and application.

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    File URL: http://arxiv.org/pdf/1201.3851
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    Paper provided by arXiv.org in its series Papers with number 1201.3851.

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    Date of creation: Jan 2012
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    Handle: RePEc:arx:papers:1201.3851

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    Web page: http://arxiv.org/

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    1. Justin Wolfers & Eric Zitzewitz, 2006. "Interpreting prediction market prices as probabilities," Working Paper Series 2006-11, Federal Reserve Bank of San Francisco.
    2. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    3. Amos Storkey, 2011. "Machine Learning Markets," Papers 1106.4509, arXiv.org.
    4. Robin Hanson, 2007. "Logarithmic Market Scoring Rules for Modular Combinatorial Information Aggregation," Journal of Prediction Markets, University of Buckingham Press, vol. 1(1), pages 3-15, February.
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