Machine Learning Markets
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.
References listed on IDEAS
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- 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.
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03-025, Stanford Institute for Economic Policy Research.
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- Manski, Charles F., 2006.
"Interpreting the predictions of prediction markets,"
Elsevier, vol. 91(3), pages 425-429, June.
- Charles F. Manski, 2004. "Interpreting the Predictions of Prediction Markets," NBER Working Papers 10359, National Bureau of Economic Research, Inc.
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- 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.
- Aseem Brahma & Sanmay Das & Malik Magdon-Ismail, 2010. "Comparing Prediction Market Structures, With an Application to Market Making," Papers 1009.1446, arXiv.org.
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