Isoelastic Agents and Wealth Updates in Machine Learning Markets
This work addresses the challenge of developing more effective machine learning markets for researchers and practitioners, though it appears incremental by building on existing prediction market frameworks.
The paper tackles the problem of improving prediction market mechanisms for machine learning by introducing agents with isoelastic utilities, showing that these markets outperform state-of-the-art classifiers like random forests and neural networks on benchmarks, with isoelastic methods generally better than logarithmic ones.
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.