Prediction Markets, Mechanism Design, and Cooperative Game Theory

Abstract

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.

arXiv commentAppears in Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence (UAI2009)

Cited by in corpus (8)

• Double or Nothing: Multiplicative Incentive Mechanisms for Crowdsourcing
• Solving Cooperative Reliability Games
• Selling Data at an Auction under Privacy Constraints
• Incentive Compatible Active Learning
• Eliciting Forecasts from Self-interested Experts: Scoring Rules for Decision Makers
• Strategy-Proof Incentives for Predictions
• Parametric Prediction from Parametric Agents
• Strategy-Proof Prediction Markets