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We study information elicitation in cost-function-based combinatorialprediction markets when the market maker's utility for information decreasesover time. In the sudden revelation setting, it is known that some piece ofinformation will be revealed to traders, and the market maker wishes to preventguaranteed profits for trading on the sure information. In the gradual decreasesetting, the market maker's utility for (partial) information decreasescontinuously over time. We design adaptive cost functions for both settingswhich: (1) preserve the information previously gathered in the market; (2)eliminate (or diminish) rewards to traders for the publicly revealedinformation; (3) leave the reward structure unaffected for other information;and (4) maintain the market maker's worst-case loss. Our constructions utilizemixed Bregman divergence, which matches our notion of utility for information.

Market Making with Decreasing Utility for Information