Combinatorial Modelling and Learning with Prediction Markets

TL;DR

This paper explores how prediction markets can serve as a versatile and general framework for combinatorial modeling in machine learning, unifying various existing models and highlighting the potential of Storkey's Machine Learning Markets.

Contribution

It demonstrates the generalization capabilities of market models for combinatorial structures and identifies Storkey's Machine Learning Markets as a fundamental, flexible approach.

Findings

Market models can generalize different combinatorial structures.

Storkey's Machine Learning Markets offer fundamental, generic modeling mechanisms.

Prediction markets have potential for broad application in model combination.

Abstract

Combining models in appropriate ways to achieve high performance is commonly seen in machine learning fields today. Although a large amount of combinatorial models have been created, little attention is drawn to the commons in different models and their connections. A general modelling technique is thus worth studying to understand model combination deeply and shed light on creating new models. Prediction markets show a promise of becoming such a generic, flexible combinatorial model. By reviewing on several popular combinatorial models and prediction market models, this paper aims to show how the market models can generalise different combinatorial stuctures and how they implement these popular combinatorial models in specific conditions. Besides, we will see among different market models, Storkey's \emph{Machine Learning Markets} provide more fundamental, generic modelling mechanisms than the others, and it has a significant appeal for both theoretical study and application.