Uncertainty in Artificial Intelligence
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Market Making with Decreasing Utility for Information
Miroslav Dudik, Rafael Frongillo, Jennifer Wortman Vaughan
We study information elicitation in cost-func- tion-based combinatorial prediction markets when the market makerÔ??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 makerÔ??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 makerÔ??s worst-case loss. Our construc- tions utilize mixed Bregman divergence, which matches our notion of utility for information.
Pages: 152-161
PS Link:
PDF Link: /papers/14/p152-dudik.pdf
AUTHOR = "Miroslav Dudik and Rafael Frongillo and Jennifer Wortman Vaughan",
TITLE = "Market Making with Decreasing Utility for Information",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "152--161"

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