Truthful Calibration Measures for Sequential Prediction
Calibration requires probabilistic reports to be conditionally unbiased and reliably interpretable as probabilities. A calibration measure assigns numerical error to miscalibrated reports. Haghtalab et al. (2024) proposed an approximately truthful calibration measure for online prediction, leaving open whether exact truthfulness is compatible with completeness and soundness. We resolve this question negatively for sequential binary prediction: exact truthfulness is incompatible with completeness and soundness, even for independent outcomes. We then show that this impossibility is specific to
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- LinkedLinked via arxiv author · 85%Anagha Gokul →
“Truthful Calibration Measures for Sequential Prediction”
- LinkedLinked via arxiv author · 85%Jason Hartline →
“Truthful Calibration Measures for Sequential Prediction”
- LinkedLinked via arxiv author · 85%Lunjia Hu →
“Truthful Calibration Measures for Sequential Prediction”
- LinkedLinked via arxiv author · 85%Jonathan Ullman →
“Truthful Calibration Measures for Sequential Prediction”
- LinkedLinked via arxiv author · 85%Yifan Wu →
“Truthful Calibration Measures for Sequential Prediction”