Right Decisions from Wrong Predictions: A Mechanism Design Alternative to Individual Calibration
This addresses the challenge for decision-makers in assessing individual forecast quality, though it appears incremental as it builds on existing mechanism design and online learning concepts.
The paper tackles the problem of decision-makers relying on imperfect probabilistic forecasts by proposing a compensation mechanism that ensures forecasted utility matches actual accrued utility, demonstrating its application in optimizing travel plans based on flight delay probabilities.
Decision makers often need to rely on imperfect probabilistic forecasts. While average performance metrics are typically available, it is difficult to assess the quality of individual forecasts and the corresponding utilities. To convey confidence about individual predictions to decision-makers, we propose a compensation mechanism ensuring that the forecasted utility matches the actually accrued utility. While a naive scheme to compensate decision-makers for prediction errors can be exploited and might not be sustainable in the long run, we propose a mechanism based on fair bets and online learning that provably cannot be exploited. We demonstrate an application showing how passengers could confidently optimize individual travel plans based on flight delay probabilities estimated by an airline.