Truthful Calibration Measures for Sequential Prediction
The paper investigates whether a calibration measure for sequential binary prediction can be perfectly truthful while maintaining completeness and soundness. It proves that exact truthfulness is impossible because requiring perfect accuracy on rare, miscalibrated histories violates the requirements of completeness and soundness. However, it shows how to achieve approximate truthfulness using specific reductions involving smooth calibration errors.