We study information elicitation in cost-function-based combinatorial prediction markets when the market maker\u2019s utility for information decreases over time. In the sudden revelation setting, it is known that some piece of information will be revealed to traders, and the market maker wishes to prevent guaranteed pro\ufb01ts for trading on the sure information. In the gradual decrease setting, the market maker\u2019s utility for (partial) information 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 diminish) rewards to traders for the publicly revealed information; (3) leave the reward structure unaffected for other information; and (4) maintain the market maker\u2019s worst-case loss. Our constructions utilize mixed Bregman divergence, which matches our notion of utility for information.<\/p>\n","protected":false},"excerpt":{"rendered":"
We study information elicitation in cost-function-based combinatorial prediction markets when the market maker\u2019s utility for information decreases over time. In the sudden revelation setting, it is known that some piece of information will be revealed to traders, and the market maker wishes to prevent guaranteed pro\ufb01ts for trading on the sure information. 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