Prediction Markets, Mechanism Design, and Cooperative Game Theory

Vincent Conitzer
Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, PMLR R7:101-108, 2009.

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR7-conitzer09a, title = {Prediction Markets, Mechanism Design, and Cooperative Game Theory}, author = {Conitzer, Vincent}, booktitle = {Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence}, pages = {101--108}, year = {2009}, editor = {Bilmes, Jeff and Ng, Andrew Y.}, volume = {R7}, series = {Proceedings of Machine Learning Research}, month = {18--21 Jun}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r7/main/assets/conitzer09a/conitzer09a.pdf}, url = {https://proceedings.mlr.press/r7/conitzer09a.html}, 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.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Prediction Markets, Mechanism Design, and Cooperative Game Theory %A Vincent Conitzer %B Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2009 %E Jeff Bilmes %E Andrew Y. Ng %F pmlr-vR7-conitzer09a %I PMLR %P 101--108 %U https://proceedings.mlr.press/r7/conitzer09a.html %V R7 %X 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. %Z Reissued by PMLR on 04 October 2026.
APA
Conitzer, V.. (2009). Prediction Markets, Mechanism Design, and Cooperative Game Theory. Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R7:101-108 Available from https://proceedings.mlr.press/r7/conitzer09a.html. Reissued by PMLR on 04 October 2026.

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