  We present a new model for prediction markets, in which we use risk measures
to model agents and introduce a market maker to describe the trading process.
This specific choice on modelling tools brings us mathematical convenience. The
analysis shows that the whole market effectively approaches a global objective,
despite that the market is designed such that each agent only cares about its
own goal. Additionally, the market dynamics provides a sensible algorithm for
optimising the global objective. An intimate connection between machine
learning and our markets is thus established, such that we could 1) analyse a
market by applying machine learning methods to the global objective, and 2)
solve machine learning problems by setting up and running certain markets.
