Designing Informative Securities

Yiling Chen, Mike Ruberry, Jennifer Wortman Vaughan
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:183-193, 2012.

Abstract

We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest as well as if he knew all of the traders’ private signals. We consider the design of markets that are always informative, markets that are informative for a particular signal structure of the participants, and informative markets constructed from a restricted selection of securities. We find that to achieve informativeness, it can be necessary to allow participants to express information that may not be directly of interest to the market organizer, and that understanding the participants’ signal structure is important for designing informative prediction markets.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-chen12c, title = {Designing Informative Securities}, author = {Chen, Yiling and Ruberry, Mike and Vaughan, Jennifer Wortman}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {183--193}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/chen12c/chen12c.pdf}, url = {https://proceedings.mlr.press/r10/chen12c.html}, abstract = {We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest as well as if he knew all of the traders’ private signals. We consider the design of markets that are always informative, markets that are informative for a particular signal structure of the participants, and informative markets constructed from a restricted selection of securities. We find that to achieve informativeness, it can be necessary to allow participants to express information that may not be directly of interest to the market organizer, and that understanding the participants’ signal structure is important for designing informative prediction markets.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Designing Informative Securities %A Yiling Chen %A Mike Ruberry %A Jennifer Wortman Vaughan %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-chen12c %I PMLR %P 183--193 %U https://proceedings.mlr.press/r10/chen12c.html %V R10 %X We create a formal framework for the design of informative securities in prediction markets. These securities allow a market organizer to infer the likelihood of events of interest as well as if he knew all of the traders’ private signals. We consider the design of markets that are always informative, markets that are informative for a particular signal structure of the participants, and informative markets constructed from a restricted selection of securities. We find that to achieve informativeness, it can be necessary to allow participants to express information that may not be directly of interest to the market organizer, and that understanding the participants’ signal structure is important for designing informative prediction markets. %Z Reissued by PMLR on 04 October 2026.
APA
Chen, Y., Ruberry, M. & Vaughan, J.W.. (2012). Designing Informative Securities. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:183-193 Available from https://proceedings.mlr.press/r10/chen12c.html. Reissued by PMLR on 04 October 2026.

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