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Machine Learning Markets

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  • Amos Storkey
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    Abstract

    Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utility-based framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is shown that such markets can implement model combination methods used in machine learning, such as product of expert and mixture of expert approaches as equilibrium pricing models, by varying agent utility functions. They can also implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions. Conversely, the market mechanisms implement inference in the relevant probabilistic models. This means that market mechanism can be utilized for implementing parallelized model building and inference for probabilistic modelling.

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    File URL: http://arxiv.org/pdf/1106.4509
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    Bibliographic Info

    Paper provided by arXiv.org in its series Papers with number 1106.4509.

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    Date of creation: Jun 2011
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    Publication status: Published in Journal of Machine Learning Research W&CP 15(AISTATS):716-724, 2011
    Handle: RePEc:arx:papers:1106.4509

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    Web page: http://arxiv.org/

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    References

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    1. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
    2. Arrow, Kenneth J. & Forsythe, Robert & Gorham, Michael & Hahn, Robert W. & Hanson, Robin & Ledyard, John O. & Levmore, Saul & Litan, Robert E. & Milgrom, Paul & Nelson, Forrest D. & Neumann, George R., 2008. "The Promise of Prediction Markets," Working paper 460, Regulation2point0.
    3. David S. Lee & Enrico Moretti, 2009. "Bayesian Learning and the Pricing of New Information: Evidence from Prediction Markets," American Economic Review, American Economic Association, vol. 99(2), pages 330-36, May.
    4. repec:reg:rpubli:259 is not listed on IDEAS
    5. Charles F. Manski, 2004. "Interpreting the Predictions of Prediction Markets," NBER Working Papers 10359, National Bureau of Economic Research, Inc.
    6. Jie-Jun Tseng & Chih-Hao Lin & Chih-Ting Lin & Sun-Chong Wang & Sai-Ping Li, 2010. "Statistical properties of agent-based models in markets with continuous double auction mechanism," Papers 1002.0917, arXiv.org.
    7. Aseem Brahma & Sanmay Das & Malik Magdon-Ismail, 2010. "Comparing Prediction Market Structures, With an Application to Market Making," Papers 1009.1446, arXiv.org.
    8. Jouini, E. & Napp, C., 2006. "Aggregation of heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 42(6), pages 752-770, September.
    9. repec:reg:rpubli:460 is not listed on IDEAS
    10. Marco Ottaviani & Peter Norman Sørensen, 2007. "Aggregation of Information and Beliefs in Prediction Markets," FRU Working Papers 2007/01, University of Copenhagen. Department of Economics. Finance Research Unit.
    11. Napp, Clotilde & Jouini, Elyès, 2006. "Aggregation of Heterogeneous Beliefs," Economics Papers from University Paris Dauphine 123456789/80, Paris Dauphine University.
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    Cited by:
    1. Jinli Hu, 2012. "Combinatorial Modelling and Learning with Prediction Markets," Papers 1201.3851, arXiv.org.

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