Market Making with Decreasing Utility for Information

Miroslav Dudik, Rafael Frongillo, Jennifer Wortman Vaughan
Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, PMLR R12:166-175, 2014.

Abstract

We study information elicitation in cost-func- tion-based combinatorial prediction markets when the market maker’s utility for information decreases over time. In the sudden revelation set- ting, it is known that some piece of information will be revealed to traders, and the market maker wishes to prevent guaranteed profits for trading on the sure information. In the gradual decrease setting, the market maker’s utility for (partial) in- formation decreases continuously over time. We design adaptive cost functions for both settings which: (1) preserve the information previously gathered in the market; (2) eliminate (or dimin- ish) rewards to traders for the publicly revealed information; (3) leave the reward structure unaf- fected for other information; and (4) maintain the market maker’s worst-case loss. Our construc- tions utilize mixed Bregman divergence, which matches our notion of utility for information.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR12-dudik14a, title = {Market Making with Decreasing Utility for Information}, author = {Dudik, Miroslav and Frongillo, Rafael and Vaughan, Jennifer Wortman}, booktitle = {Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence}, pages = {166--175}, year = {2014}, editor = {Zhang, Nevin L. and Tian, Jin}, volume = {R12}, series = {Proceedings of Machine Learning Research}, month = {23--27 Jul}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r12/main/assets/dudik14a/dudik14a.pdf}, url = {https://proceedings.mlr.press/r12/dudik14a.html}, abstract = {We study information elicitation in cost-func- tion-based combinatorial prediction markets when the market maker’s utility for information decreases over time. In the sudden revelation set- ting, it is known that some piece of information will be revealed to traders, and the market maker wishes to prevent guaranteed profits for trading on the sure information. In the gradual decrease setting, the market maker’s utility for (partial) in- formation decreases continuously over time. We design adaptive cost functions for both settings which: (1) preserve the information previously gathered in the market; (2) eliminate (or dimin- ish) rewards to traders for the publicly revealed information; (3) leave the reward structure unaf- fected for other information; and (4) maintain the market maker’s worst-case loss. Our construc- tions utilize mixed Bregman divergence, which matches our notion of utility for information.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T Market Making with Decreasing Utility for Information %A Miroslav Dudik %A Rafael Frongillo %A Jennifer Wortman Vaughan %B Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2014 %E Nevin L. Zhang %E Jin Tian %F pmlr-vR12-dudik14a %I PMLR %P 166--175 %U https://proceedings.mlr.press/r12/dudik14a.html %V R12 %X We study information elicitation in cost-func- tion-based combinatorial prediction markets when the market maker’s utility for information decreases over time. In the sudden revelation set- ting, it is known that some piece of information will be revealed to traders, and the market maker wishes to prevent guaranteed profits for trading on the sure information. In the gradual decrease setting, the market maker’s utility for (partial) in- formation decreases continuously over time. We design adaptive cost functions for both settings which: (1) preserve the information previously gathered in the market; (2) eliminate (or dimin- ish) rewards to traders for the publicly revealed information; (3) leave the reward structure unaf- fected for other information; and (4) maintain the market maker’s worst-case loss. Our construc- tions utilize mixed Bregman divergence, which matches our notion of utility for information. %Z Reissued by PMLR on 04 October 2026.
APA
Dudik, M., Frongillo, R. & Vaughan, J.W.. (2014). Market Making with Decreasing Utility for Information. Proceedings of the 30th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R12:166-175 Available from https://proceedings.mlr.press/r12/dudik14a.html. Reissued by PMLR on 04 October 2026.

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