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Machine Learning Markets

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  • Amos Storkey

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.

Suggested Citation

  • Amos Storkey, 2011. "Machine Learning Markets," Papers 1106.4509, arXiv.org.
  • Handle: RePEc:arx:papers:1106.4509
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    References listed on IDEAS

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    1. Manski, Charles F., 2006. "Interpreting the predictions of prediction markets," Economics Letters, Elsevier, vol. 91(3), pages 425-429, June.
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    3. Justin Wolfers & Eric Zitzewitz, 2004. "Prediction Markets," Journal of Economic Perspectives, American Economic Association, vol. 18(2), pages 107-126, Spring.
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    5. 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.
    6. Jouini, E. & Napp, C., 2006. "Aggregation of heterogeneous beliefs," Journal of Mathematical Economics, Elsevier, vol. 42(6), pages 752-770, September.
    7. Tseng, Jie-Jun & Lin, Chih-Hao & Lin, Chih-Ting & Wang, Sun-Chong & Li, Sai-Ping, 2010. "Statistical properties of agent-based models in markets with continuous double auction mechanism," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(8), pages 1699-1707.
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    9. 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 330-336, May.
    10. repec:hal:journl:halshs-00176505 is not listed on IDEAS
    11. Aseem Brahma & Sanmay Das & Malik Magdon-Ismail, 2010. "Comparing Prediction Market Structures, With an Application to Market Making," Papers 1009.1446, arXiv.org.
    12. 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.
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    Cited by:

    1. Jinli Hu, 2012. "Combinatorial Modelling and Learning with Prediction Markets," Papers 1201.3851, arXiv.org.
    2. G. Bottazzi & D. Giachini, 2019. "Far from the madding crowd: collective wisdom in prediction markets," Quantitative Finance, Taylor & Francis Journals, vol. 19(9), pages 1461-1471, September.

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