  Prediction markets are designed to elicit information from multiple agents in
order to predict (obtain probabilities for) future events. A good prediction
market incentivizes agents to reveal their information truthfully; such
incentive compatibility considerations are commonly studied in mechanism
design. While this relation between prediction markets and mechanism design is
well understood at a high level, the models used in prediction markets tend to
be somewhat different from those used in mechanism design. This paper considers
a model for prediction markets that fits more straightforwardly into the
mechanism design framework. We consider a number of mechanisms within this
model, all based on proper scoring rules. We discuss basic properties of these
mechanisms, such as incentive compatibility. We also draw connections between
some of these mechanisms and cooperative game theory. Finally, we speculate how
one might build a practical prediction market based on some of these ideas.

Computer Science and Game Theory