Isoelastic Agents and Wealth Updates in Machine Learning Markets

Amos Storkey, Jono Millin, Krzysztof Geras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha-mixtures, with a particular form of mixing component relating to each agent's wealth. We also demonstrate that wealth accumulation for logarithmic and other isoelastic agents (through payoffs on prediction of training targets) can implement both Bayesian model updates and mixture weight updates by imposing different market payoff structures. An iterative algorithm is given for market equilibrium computation. We demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests, as well as single classifiers (neural networks, decision trees) on a number of machine learning benchmarks, and show that isoelastic combination methods are generally better than their logarithmic counterparts.
Original languageEnglish
Title of host publicationProceedings of the 29th International Conference on Machine Learning (ICML 2012)
Number of pages8
Publication statusPublished - 27 Jun 2012

Keywords

  • cs.LG
  • cs.GT
  • stat.ML

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