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Decision makers often need to rely on imperfect probabilistic forecasts.While average performance metrics are typically available, it is difficult toassess the quality of individual forecasts and the corresponding utilities. Toconvey confidence about individual predictions to decision-makers, we propose acompensation mechanism ensuring that the forecasted utility matches theactually accrued utility. While a naive scheme to compensate decision-makersfor prediction errors can be exploited and might not be sustainable in the longrun, we propose a mechanism based on fair bets and online learning thatprovably cannot be exploited. We demonstrate an application showing howpassengers could confidently optimize individual travel plans based on flightdelay probabilities estimated by an airline.

Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration