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.

[1]  C. Manski Interpreting the Predictions of Prediction Markets , 2004 .

[2]  Geoffrey E. Hinton Training Products of Experts by Minimizing Contrastive Divergence , 2002, Neural Computation.

[3]  L. Walras Elements of Pure Economics , 1954 .

[4]  Kshanti A. Greene Collective belief models for representing consensus and divergence in communities of Bayesian decision-makers , 2010 .

[5]  Jie-Jun Tseng,et al.  Statistical properties of agent-based models in markets with continuous double auction mechanism , 2010, 1002.0917.

[6]  Sanmay Das,et al.  Comparing Prediction Market Structures, With an Application to Market Making , 2010, ArXiv.

[7]  Robert E. Schapire,et al.  The Boosting Approach to Machine Learning An Overview , 2003 .

[8]  J. Wolfers,et al.  Prediction Markets , 2003 .

[9]  Paul C. Tetlock,et al.  The Promise of Prediction Markets , 2008, Science.

[10]  Nathan Lay,et al.  Supervised Aggregation of Classifiers using Artificial Prediction Markets , 2010, ICML.

[11]  Geoffrey E. Hinton,et al.  Adaptive Mixtures of Local Experts , 1991, Neural Computation.

[12]  Thomas Hofmann,et al.  Probabilistic Latent Semantic Analysis , 1999, UAI.

[13]  Michael P. Wellman,et al.  Representing Aggregate Belief through the Competitive Equilibrium of a Securities Market , 1997, UAI.

[14]  Ramesh A. Gopinath,et al.  Gaussianization , 2000, NIPS.

[15]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[16]  Clotilde Napp,et al.  Aggregation of Heterogeneous Beliefs , 2006 .

[17]  Marco Ottaviani,et al.  Aggregation of Information and Beliefs in Prediction Markets , 2007 .

[18]  David M. Pennock,et al.  An Empirical Comparison of Algorithms for Aggregating Expert Predictions , 2006, UAI.

[19]  R. Tibshirani,et al.  Additive Logistic Regression : a Statistical View ofBoostingJerome , 1998 .

[20]  David S. Lee,et al.  Bayesian Learning and the Pricing of New Information: Evidence from Prediction Markets , 2009 .

[21]  Christopher M. Bishop,et al.  Advances in Neural Information Processing Systems 8 (NIPS 1995) , 1991 .

[22]  Yehuda Koren,et al.  Lessons from the Netflix prize challenge , 2007, SKDD.

[23]  K. Arrow,et al.  EXISTENCE OF AN EQUILIBRIUM FOR A COMPETITIVE ECONOMY , 1954 .